Discriminant analysis of the metabolic effects of a new combined contraceptive vaginal ring containing Nestorone/EE vs. a second-generation oral contraceptive containing levonorgestrel/EE.

PubMed

Rad, Mandana; Burggraaf, Jacobus; de Kam, Marieke L; Cohen, Adam F; Kluft, Cornelis

2012-09-01

Discriminant analysis (DA) was performed on data of two combined hormonal contraceptives (CHC) differing in estrogen ratio to explore whether a combination of variables rather than a single variable distinguishes CHCs better. Data were used of a parallel study in premenopausal women treated for three cycles (21 days on, 7 days off) with a contraceptive vaginal ring delivering Nestorone and ethinyl estradiol (EE) or an oral contraceptive containing levonorgestrel and EE. DA was performed on the change from baseline (CFB) and the end-of-treatment values at 3 months for lipids, sex-hormone binding globulin (SHBG), C-reactive protein, angiotensinogen, blood pressure and hemostasis variables, and on the hemostasis variables only. For the complete set, the CFB for factor VII (FVII), SHBG and plasminogen (PLG), or end-of-treatment SHBG- and FVII level discriminated the treatments best. Maximal discrimination for the hemostasis data was by CFB for FVII and PLG or end-of-treatment FVII level. DA identifies differences between CHCs and may provide information on the factors associated with thrombotic risk. Copyright © 2012 Elsevier Inc. All rights reserved.

Phenolic Analysis and Theoretic Design for Chinese Commercial Wines' Authentication.

PubMed

Li, Si-Yu; Zhu, Bao-Qing; Reeves, Malcolm J; Duan, Chang-Qing

2018-01-01

To develop a robust tool for Chinese commercial wines' varietal, regional, and vintage authentication, phenolic compounds in 121 Chinese commercial dry red wines were detected and quantified by using high-performance liquid chromatography triple-quadrupole mass spectrometry (HPLC-QqQ-MS/MS), and differentiation abilities of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were compared. Better than PCA and PLS-DA, OPLS-DA models used to differentiate wines according to their varieties (Cabernet Sauvignon or other varieties), regions (east or west Cabernet Sauvignon wines), and vintages (young or old Cabernet Sauvignon wines) were ideally established. The S-plot provided in OPLS-DA models showed the key phenolic compounds which were both statistically and biochemically significant in sample differentiation. Besides, the potential of the OPLS-DA models in deeper sample differentiating of more detailed regional and vintage information of wines was proved optimistic. On the basis of our results, a promising theoretic design for wine authentication was further proposed for the first time, which might be helpful in practical authentication of more commercial wines. The phenolic data of 121 Chinese commercial dry red wines was processed with different statistical tools for varietal, regional, and vintage differentiation. A promising theoretical design was summarized, which might be helpful for wine authentication in practical situation. © 2017 Institute of Food Technologists®.

Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse.

PubMed

Alladio, E; Giacomelli, L; Biosa, G; Corcia, D Di; Gerace, E; Salomone, A; Vincenti, M

2018-01-01

The chronic intake of an excessive amount of alcohol is currently ascertained by determining the concentration of direct alcohol metabolites in the hair samples of the alleged abusers, including ethyl glucuronide (EtG) and, less frequently, fatty acid ethyl esters (FAEEs). Indirect blood biomarkers of alcohol abuse are still determined to support hair EtG results and diagnose a consequent liver impairment. In the present study, the supporting role of hair FAEEs is compared with indirect blood biomarkers with respect to the contexts in which hair EtG interpretation is uncertain. Receiver Operating Characteristics (ROC) curves and multivariate Principal Component Analysis (PCA) demonstrated much stronger correlation of EtG results with FAEEs than with any single indirect biomarker or their combinations. Partial Least Squares Discriminant Analysis (PLS-DA) models based on hair EtG and FAEEs were developed to maximize the biomarkers information content on a multivariate background. The final PLS-DA model yielded 100% correct classification on a training/evaluation dataset of 155 subjects, including both chronic alcohol abusers and social drinkers. Then, the PLS-DA model was validated on an external dataset of 81 individual providing optimal discrimination ability between chronic alcohol abusers and social drinkers, in terms of specificity and sensitivity. The PLS-DA scores obtained for each subject, with respect to the PLS-DA model threshold that separates the probabilistic distributions for the two classes, furnished a likelihood ratio value, which in turn conveys the strength of the experimental data support to the classification decision, within a Bayesian logic. Typical boundary real cases from daily work are discussed, too. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of Lipitor counterfeits: a comparison of NIR and Raman spectroscopy in combination with chemometrics.

PubMed

de Peinder, P; Vredenbregt, M J; Visser, T; de Kaste, D

2008-08-05

Research has been carried on the feasibility of near infrared (NIR) and Raman spectroscopy as rapid screening methods to discriminate between genuine and counterfeits of the cholesterol-lowering medicine Lipitor. Classification, based on partial least squares discriminant analysis (PLS-DA) models, appears to be successful for both spectroscopic techniques, irrespective of whether atorvastatine or lovastatine has been used as the active pharmaceutical ingredient (API). The discriminative power of the NIR model, in particular, largely relies on the spectral differences of the tablet matrix. This is due to the relative large sample volume that is probed with NIR and the strong spectroscopic activity of the excipients. PLS-DA models based on NIR or Raman spectra can also be applied to distinguish between atorvastatine and lovastatine as the API used in the counterfeits tested in this study. A disadvantage of Raman microscopy for this type of analysis is that it is primarily a surface technique. As a consequence spectra of the coating and the tablet core might differ. Besides, spectra may change with the position of the laser in case the sample is inhomogeneous. However, the robustness of the PLS-DA models turned out to be sufficiently large to allow a reliable discrimination. Principal component analysis (PCA) of the spectra revealed that the conditions, at which tablets have been stored, affect the NIR data. This effect is attributed to the adsorption of water from the atmosphere after unpacking from the blister. It implies that storage conditions should be taken into account when the NIR technique is used for discriminating purposes. However, in this study both models based on NIR spectra and Raman data enabled reliable discrimination between genuine and counterfeited Lipitor tablets, regardless of their storage conditions.

Rare earth elements minimal harvest year variation facilitates robust geographical origin discrimination: The case of PDO "Fava Santorinis".

PubMed

Drivelos, Spiros A; Danezis, Georgios P; Haroutounian, Serkos A; Georgiou, Constantinos A

2016-12-15

This study examines the trace and rare earth elemental (REE) fingerprint variations of PDO (Protected Designation of Origin) "Fava Santorinis" over three consecutive harvesting years (2011-2013). Classification of samples in harvesting years was studied by performing discriminant analysis (DA), k nearest neighbours (κ-NN), partial least squares (PLS) analysis and probabilistic neural networks (PNN) using rare earth elements and trace metals determined using ICP-MS. DA performed better than κ-NN, producing 100% discrimination using trace elements and 79% using REEs. PLS was found to be superior to PNN, achieving 99% and 90% classification for trace and REEs, respectively, while PNN achieved 96% and 71% classification for trace and REEs, respectively. The information obtained using REEs did not enhance classification, indicating that REEs vary minimally per harvesting year, providing robust geographical origin discrimination. The results show that seasonal patterns can occur in the elemental composition of "Fava Santorinis", probably reflecting seasonality of climate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Refining Stimulus Parameters in Assessing Infant Speech Perception Using Visual Reinforcement Infant Speech Discrimination: Sensation Level.

PubMed

Uhler, Kristin M; Baca, Rosalinda; Dudas, Emily; Fredrickson, Tammy

2015-01-01

Speech perception measures have long been considered an integral piece of the audiological assessment battery. Currently, a prelinguistic, standardized measure of speech perception is missing in the clinical assessment battery for infants and young toddlers. Such a measure would allow systematic assessment of speech perception abilities of infants as well as the potential to investigate the impact early identification of hearing loss and early fitting of amplification have on the auditory pathways. To investigate the impact of sensation level (SL) on the ability of infants with normal hearing (NH) to discriminate /a-i/ and /ba-da/ and to determine if performance on the two contrasts are significantly different in predicting the discrimination criterion. The design was based on a survival analysis model for event occurrence and a repeated measures logistic model for binary outcomes. The outcome for survival analysis was the minimum SL for criterion and the outcome for the logistic regression model was the presence/absence of achieving the criterion. Criterion achievement was designated when an infant's proportion correct score was >0.75 on the discrimination performance task. Twenty-two infants with NH sensitivity participated in this study. There were 9 males and 13 females, aged 6-14 mo. Testing took place over two to three sessions. The first session consisted of a hearing test, threshold assessment of the two speech sounds (/a/ and /i/), and if time and attention allowed, visual reinforcement infant speech discrimination (VRISD). The second session consisted of VRISD assessment for the two test contrasts (/a-i/ and /ba-da/). The presentation level started at 50 dBA. If the infant was unable to successfully achieve criterion (>0.75) at 50 dBA, the presentation level was increased to 70 dBA followed by 60 dBA. Data examination included an event analysis, which provided the probability of criterion distribution across SL. The second stage of the analysis was a repeated measures logistic regression where SL and contrast were used to predict the likelihood of speech discrimination criterion. Infants were able to reach criterion for the /a-i/ contrast at statistically lower SLs when compared to /ba-da/. There were six infants who never reached criterion for /ba-da/ and one never reached criterion for /a-i/. The conditional probability of not reaching criterion by 70 dB SL was 0% for /a-i/ and 21% for /ba-da/. The predictive logistic regression model showed that children were more likely to discriminate the /a-i/ even when controlling for SL. Nearly all normal-hearing infants can demonstrate discrimination criterion of a vowel contrast at 60 dB SL, while a level of ≥70 dB SL may be needed to allow all infants to demonstrate discrimination criterion of a difficult consonant contrast. American Academy of Audiology.

[Discriminant Analysis of Lavender Essential Oil by Attenuated Total Reflectance Infrared Spectroscopy].

PubMed

Tang, Jun; Wang, Qing; Tong, Hong; Liao, Xiang; Zhang, Zheng-fang

2016-03-01

This work aimed to use attenuated total reflectance Fourier transform infrared spectroscopy to identify the lavender essential oil by establishing a Lavender variety and quality analysis model. So, 96 samples were tested. For all samples, the raw spectra were pretreated as second derivative, and to determine the 1 750-900 cm(-1) wavelengths for pattern recognition analysis on the basis of the variance calculation. The results showed that principal component analysis (PCA) can basically discriminate lavender oil cultivar and the first three principal components mainly represent the ester, alcohol and terpenoid substances. When the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was established, the 68 samples were used for the calibration set. Determination coefficients of OPLS-DA regression curve were 0.959 2, 0.976 4, and 0.958 8 respectively for three varieties of lavender essential oil. Three varieties of essential oil's the root mean square error of prediction (RMSEP) in validation set were 0.142 9, 0.127 3, and 0.124 9, respectively. The discriminant rate of calibration set and the prediction rate of validation set had reached 100%. The model has the very good recognition capability to detect the variety and quality of lavender essential oil. The result indicated that a model which provides a quick, intuitive and feasible method had been built to discriminate lavender oils.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

PubMed

Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

2015-02-01

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data. Copyright © 2014 Elsevier Inc. All rights reserved.

Multivariate analysis of standoff laser-induced breakdown spectroscopy spectra for classification of explosive-containing residues

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Lucia, Frank C. Jr.; Gottfried, Jennifer L.; Munson, Chase A.

2008-11-01

A technique being evaluated for standoff explosives detection is laser-induced breakdown spectroscopy (LIBS). LIBS is a real-time sensor technology that uses components that can be configured into a ruggedized standoff instrument. The U.S. Army Research Laboratory has been coupling standoff LIBS spectra with chemometrics for several years now in order to discriminate between explosives and nonexplosives. We have investigated the use of partial least squares discriminant analysis (PLS-DA) for explosives detection. We have extended our study of PLS-DA to more complex sample types, including binary mixtures, different types of explosives, and samples not included in the model. We demonstrate themore » importance of building the PLS-DA model by iteratively testing it against sample test sets. Independent test sets are used to test the robustness of the final model.« less

Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

PubMed

Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

2000-01-01

Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis

PubMed Central

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-01-01

Abstract. Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens. PMID:26057029

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis.

PubMed

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-06-01

Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens.

Determination of Chinese rice wine from different wineries by near-infrared spectroscopy combined with chemometrics methods

NASA Astrophysics Data System (ADS)

Niu, Xiaoying; Ying, Yibin; Yu, Haiyan; Xie, Lijuan; Fu, Xiaping; Zhou, Ying; Jiang, Xuesong

2007-09-01

In this paper, 104 samples of Chinese rice wines of the same variety (Shaoxing rice wine), collected in three winery ("guyuelongshan", "pagoda" brand, "kuaijishan"), three brewed years (2002, 2004, 2004-2006) were analyzed by near-infrared transmission spectroscopy between 800 and 2500 nm. The spectral differences were studied by principal components analysis (PCA), and Classifications, according the brand, were carried out by discriminant analysis (DA) and partial least squares discriminant analysis (PLSDA). The DA model gained a total accuracy of 94.23% and when used to predict the brand of the validation set samples, a better result, correctly classified all of the three kinds of Chinese rice wine up to 100%, are obtained by PLSDA model. The work reported here is a feasibility study and requires further development with considerable samples of more different brands. Further studies are needed in order to improve the accuracy and robustness, and to extend the discrimination to other Chinese rice wine varieties or brands.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars.

PubMed

Hu, Boran; Yue, Yaqing; Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W

2015-01-01

Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach.

Improved detection of highly energetic materials traces on surfaces by standoff laser-induced thermal emission incorporating neural networks

NASA Astrophysics Data System (ADS)

Figueroa-Navedo, Amanda; Galán-Freyle, Nataly Y.; Pacheco-Londoño, Leonardo C.; Hernández-Rivera, Samuel P.

2013-05-01

Terrorists conceal highly energetic materials (HEM) as Improvised Explosive Devices (IED) in various types of materials such as PVC, wood, Teflon, aluminum, acrylic, carton and rubber to disguise them from detection equipment used by military and security agency personnel. Infrared emissions (IREs) of substrates, with and without HEM, were measured to generate models for detection and discrimination. Multivariable analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and neural networks (NN) were employed to generate models, in which the emission of IR light from heated samples was stimulated using a CO2 laser giving rise to laser induced thermal emission (LITE) of HEMs. Traces of a specific target threat chemical explosive: PETN in surface concentrations of 10 to 300 ug/cm2 were studied on the surfaces mentioned. Custom built experimental setup used a CO2 laser as a heating source positioned with a telescope, where a minimal loss in reflective optics was reported, for the Mid-IR at a distance of 4 m and 32 scans at 10 s. SVM-DA resulted in the best statistical technique for a discrimination performance of 97%. PLS-DA accurately predicted over 94% and NN 88%.

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates.

PubMed

Gottfried, Jennifer L

2011-07-01

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.

Application of Near Infrared Reflectance Spectroscopy for Rapid and Non-Destructive Discrimination of Hulled Barley, Naked Barley, and Wheat Contaminated with Fusarium

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Kim, Geonseob; Ham, Hyeonheui; Kim, Seongmin; Kim, Moon S.

2018-01-01

Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175–2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified. PMID:29301319

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

Improved discrimination between monocotyledonous and dicotyledonous plants for weed control based on the blue-green region of ultraviolet-induced fluorescence spectra.

PubMed

Panneton, Bernard; Guillaume, Serge; Roger, Jean-Michel; Samson, Guy

2010-01-01

Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400-425 nm and 425-490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed-maize discrimination.

External characteristic determination of eggs and cracked eggs identification using spectral signature

PubMed Central

Xie, Chuanqi; He, Yong

2016-01-01

This study was carried out to use hyperspectral imaging technique for determining color (L*, a* and b*) and eggshell strength and identifying cracked chicken eggs. Partial least squares (PLS) models based on full and selected wavelengths suggested by regression coefficient (RC) method were established to predict the four parameters, respectively. Partial least squares-discriminant analysis (PLS-DA) and RC-partial least squares-discriminant analysis (RC-PLS-DA) models were applied to identify cracked eggs. PLS models performed well with the correlation coefficient (rp) of 0.788 for L*, 0.810 for a*, 0.766 for b* and 0.835 for eggshell strength. RC-PLS models also obtained the rp of 0.771 for L*, 0.806 for a*, 0.767 for b* and 0.841 for eggshell strength. The classification results were 97.06% in PLS-DA model and 88.24% in RC-PLS-DA model. It demonstrated that hyperspectral imaging technique has the potential to be used to detect color and eggshell strength values and identify cracked chicken eggs. PMID:26882990

Quantitative and Discriminative Evaluation of Contents of Phenolic and Flavonoid and Antioxidant Competence for Chinese Honeys from Different Botanical Origins.

PubMed

Shen, Shi; Wang, Jingbo; Zhuo, Qin; Chen, Xi; Liu, Tingting; Zhang, Shuang-Qing

2018-05-08

Phenolics and flavonoids in honey are considered as the main phytonutrients which not only act as natural antioxidants, but can also be used as floral markers for honey identification. In this study, the chemical profiles of phenolics and flavonoids, antioxidant competences including total phenolic content, DPPH and ABTS assays and discrimination using chemometric analysis of various Chinese monofloral honeys from six botanical origins (acacia, Vitex , linden, rapeseed, Astragalus and Codonopsis ) were examined. A reproducible and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was optimized and validated for the simultaneous determination of 38 phenolics, flavonoids and abscisic acid in honey. Formononetin, ononin, calycosin and calycosin-7- O -β-d-glucoside were identified and quantified in honeys for the first time. Principal component analysis (PCA) showed obvious differences among the honey samples in three-dimensional space accounting for 72.63% of the total variance. Hierarchical cluster analysis (HCA) also revealed that the botanical origins of honey samples correlated with their phenolic and flavonoid contents. Partial least squares-discriminant analysis (PLS-DA) classification was performed to derive a model with high prediction ability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) model was employed to identify markers specific to a particular honey type. The results indicated that Chinese honeys contained various and discriminative phenolics and flavonoids, as well as antioxidant competence from different botanical origins, which was an alternative approach to honey identification and nutritional evaluation.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

Development and evaluation of habitat models for herpetofauna and small mammals

Treesearch

William M. Block; Michael L. Morrison; Peter E. Scott

1998-01-01

We evaluated the ability of discriminant analysis (DA), logistic regression (LR), and multiple regression (MR) to describe habitat use by amphibians, reptiles, and small mammals found in California oak woodlands. We also compared models derived from pitfall and live trapping data for several species. Habitat relations modeled by DA and LR produced similar results,...

Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease.

PubMed

Singh, Brajesh; Jana, Saikat K; Ghosh, Nilanjana; Das, Soumen K; Joshi, Mamata; Bhattacharyya, Parthasarathi; Chaudhury, Koel

2017-01-05

Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1 H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and l-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative determination and evaluation of Paris polyphylla var. yunnanensis with different harvesting times using UPLC-UV-MS and FT-IR spectroscopy in combination with partial least squares discriminant analysis.

PubMed

Yang, Yuan-Gui; Zhang, Ji; Zhao, Yan-Li; Zhang, Jin-Yu; Wang, Yuan-Zhong

2017-07-01

A rapid method was developed and validated by ultra-performance liquid chromatography-triple quadrupole mass spectroscopy with ultraviolet detection (UPLC-UV-MS) for simultaneous determination of paris saponin I, paris saponin II, paris saponin VI and paris saponin VII. Partial least squares discriminant analysis (PLS-DA) based on UPLC and Fourier transform infrared (FT-IR) spectroscopy was employed to evaluate Paris polyphylla var. yunnanensis (PPY) at different harvesting times. Quantitative determination implied that the various contents of bioactive compounds with different harvesting times may lead to different pharmacological effects; the average content of total saponins for PPY harvested at 8 years was higher than that from other samples. The PLS-DA of FT-IR spectra had a better performance than that of UPLC for discrimination of PPY from different harvesting times. Copyright © 2016 John Wiley & Sons, Ltd.

Paper spray mass spectrometry and chemometric tools for a fast and reliable identification of counterfeit blended Scottish whiskies.

PubMed

Teodoro, Janaína Aparecida Reis; Pereira, Hebert Vinicius; Sena, Marcelo Martins; Piccin, Evandro; Zacca, Jorge Jardim; Augusti, Rodinei

2017-12-15

A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit. Copyright © 2017 Elsevier Ltd. All rights reserved.

Non-destructive profiling of volatile organic compounds using HS-SPME/GC-MS and its application for the geographical discrimination of white rice.

PubMed

Lim, Dong Kyu; Mo, Changyeun; Lee, Dong-Kyu; Long, Nguyen Phuoc; Lim, Jongguk; Kwon, Sung Won

2018-01-01

The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice. Copyright © 2017. Published by Elsevier B.V.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

PubMed

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars

PubMed Central

Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W.

2015-01-01

Background and Aims Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. Methods and Results We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Conclusions Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. Significance of the Study The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach. PMID:26658757

Multi-element fingerprinting as a tool in origin authentication of four east China marine species.

PubMed

Guo, Lipan; Gong, Like; Yu, Yanlei; Zhang, Hong

2013-12-01

The contents of 25 elements in 4 types of commercial marine species from the East China Sea were determined by inductively coupled plasma mass spectrometry and atomic absorption spectrometry. The elemental composition was used to differentiate marine species according to geographical origin by multivariate statistical analysis. The results showed that principal component analysis could distinguish samples from different areas and reveal the elements which played the most important role in origin diversity. The established models by partial least squares discriminant analysis (PLS-DA) and by probabilistic neural network (PNN) can both precisely predict the origin of the marine species. Further study indicated that PLS-DA and PNN were efficacious in regional discrimination. The models from these 2 statistical methods, with an accuracy of 97.92% and 100%, respectively, could both distinguish samples from different areas without the need for species differentiation. © 2013 Institute of Food Technologists®

Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis

NASA Astrophysics Data System (ADS)

Mabood, Fazal; Boqué, Ricard; Folcarelli, Rita; Busto, Olga; Al-Harrasi, Ahmed; Hussain, Javid

2015-05-01

We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720 nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20 nm, 40 nm, 60 nm and 80 nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20 nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

PubMed

Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin

2016-12-01

Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Detection of drug active ingredients by chemometric processing of solid-state NMR spectrometry data -- the case of acetaminophen.

PubMed

Paradowska, Katarzyna; Jamróz, Marta Katarzyna; Kobyłka, Mariola; Gowin, Ewelina; Maczka, Paulina; Skibiński, Robert; Komsta, Łukasz

2012-01-01

This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.

NMR metabolomics of ripened and developing oilseed rape (Brassica napus) and turnip rape (Brassica rapa).

PubMed

Kortesniemi, Maaria; Vuorinen, Anssi L; Sinkkonen, Jari; Yang, Baoru; Rajala, Ari; Kallio, Heikki

2015-04-01

The oilseeds of the commercially important oilseed rape (Brassica napus) and turnip rape (Brassica rapa) were investigated with (1)H NMR metabolomics. The compositions of ripened (cultivated in field trials) and developing seeds (cultivated in controlled conditions) were compared in multivariate models using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Differences in the major lipids and the minor metabolites between the two species were found. A higher content of polyunsaturated fatty acids and sucrose were observed in turnip rape, while the overall oil content and sinapine levels were higher in oilseed rape. The genotype traits were negligible compared to the effect of the growing site and concomitant conditions on the oilseed metabolome. This study demonstrates the applicability of NMR-based analysis in determining the species, geographical origin, developmental stage, and quality of oilseed Brassicas. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

Discrimination of Radix Polygoni Multiflori from different geographical areas by UPLC-QTOF/MS combined with chemometrics.

PubMed

Tang, Jin-Fa; Li, Wei-Xia; Zhang, Fan; Li, Yu-Hui; Cao, Ying-Jie; Zhao, Ya; Li, Xue-Lin; Ma, Zhi-Jie

2017-01-01

Nowadays, Radix Polygoni Multiflori (RPM, Heshouwu in Chinese) from different geographical origins were used in clinic. In order to characterize the chemical profiles of different geographical origins of RPM samples, ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) combined with chemometrics (partial least squared discriminant analysis, PLS‑DA) method was applied in the present study. The chromatography, chemical composition and MS information of RPM samples from 18 geographical origins were acquired and profiled by UPLC-QTOF/MS. The chemical markers contributing the differentiation of RPM samples were observed and characterized by supervised PLS‑DA method of chemometrics. The chemical composition differences of RPM samples derived from 18 different geographical origins were observed. Nine chemical markers were tentatively identified which could be used as specific chemical markers for the differentiation of geographical RPM samples. UPLC-QTOF/MS method coupled with chemometrics analysis has potential to be used for discriminating different geographical TCMs. Results will help to develop strategies for conservation and utilization of RPM samples.

Temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy field at Kelantan, Malaysia.

PubMed

Hussain, Hazilia; Yusoff, Mohd Kamil; Ramli, Mohd Firuz; Abd Latif, Puziah; Juahir, Hafizan; Zawawi, Mohamed Azwan Mohammed

2013-11-15

Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S.; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-01-01

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting. PMID:24763251

Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging.

PubMed

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-04-24

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400-700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares-discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400-700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600-700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Relevant principal component analysis applied to the characterisation of Portuguese heather honey.

PubMed

Martins, Rui C; Lopes, Victor V; Valentão, Patrícia; Carvalho, João C M F; Isabel, Paulo; Amaral, Maria T; Batista, Maria T; Andrade, Paula B; Silva, Branca M

2008-01-01

The main purpose of this study was the characterisation of 'Serra da Lousã' heather honey by using novel statistical methodology, relevant principal component analysis, in order to assess the correlations between production year, locality and composition. Herein, we also report its chemical composition in terms of sugars, glycerol and ethanol, and physicochemical parameters. Sugars profiles from 'Serra da Lousã' heather and 'Terra Quente de Trás-os-Montes' lavender honeys were compared and allowed the discrimination: 'Serra da Lousã' honeys do not contain sucrose, generally exhibit lower contents of turanose, trehalose and maltose and higher contents of fructose and glucose. Different localities from 'Serra da Lousã' provided groups of samples with high and low glycerol contents. Glycerol and ethanol contents were revealed to be independent of the sugars profiles. These data and statistical models can be very useful in the comparison and detection of adulterations during the quality control analysis of 'Serra da Lousã' honey.

Partial dopaminergic denervation-induced impairment in stimulus discrimination acquisition in parkinsonian rats: a model for early Parkinson's disease.

PubMed

Eagle, Andrew L; Olumolade, Oluyemi O; Otani, Hajime

2015-03-01

Parkinson's disease (PD) produces progressive nigrostriatal dopamine (DA) denervation resulting in cognitive and motor impairment. However, it is unknown whether cognitive impairments, such as instrumental learning deficits, are associated with the early stage PD-induced mild DA denervation. The current study sought to model early PD-induced instrumental learning impairments by assessing the effects of low dose (5.5μg), bilateral 6OHDA-induced striatal DA denervation on acquisition of instrumental stimulus discrimination in rats. 6OHDA (n=20) or sham (n=10) lesioned rats were tested for stimulus discrimination acquisition either 1 or 2 weeks post surgical lesion. Stimulus discrimination acquisition across 10 daily sessions was used to assess discriminative accuracy, or a probability measure of the shift toward reinforced responding under one stimulus condition (Sd) away from extinction, when reinforcement was withheld, under another (S(d) phase). Striatal DA denervation was assayed by tyrosine hydroxylase (TH) staining intensity. Results indicated that 6OHDA lesions produced significant loss of dorsal striatal TH staining intensity and marked impairment in discrimination acquisition, without inducing akinetic motor deficits. Rather 6OHDA-induced impairment was associated with perseveration during extinction (S(Δ) phase). These findings suggest that partial, bilateral striatal DA denervation produces instrumental learning deficits, prior to the onset of gross motor impairment, and suggest that the current model is useful for investigating mild nigrostriatal DA denervation associated with early stage clinical PD. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

PubMed

Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

2017-08-15

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monoaminergic Psychomotor Stimulants: Discriminative Stimulus Effects and Dopamine Efflux

PubMed Central

Desai, Rajeev I.; Paronis, Carol A.; Martin, Jared; Desai, Ramya

2010-01-01

The present studies were conducted to investigate the relationship between discriminative stimulus effects of indirectly acting monoaminergic psychostimulants and their ability to increase extracellular levels of dopamine (DA) in the nucleus accumbens (NAcb) shell. First, the behavioral effects of methamphetamine (MA), cocaine (COC), 1-[2-[bis(4-fluorophenyl-)methoxy]ethyl]-4-(3-phenylpropyl)piperazine (GBR 12909), d-amphetamine, and methylphenidate were established in rats trained to discriminate intraperitoneal injections of 0.3 mg/kg MA from saline. In other studies, in vivo microdialysis was used to determine the effects of MA, COC, and GBR 12909 on extracellular DA levels in the NAcb shell. Results show that all drugs produced dose-related and full substitution for the discriminative stimulus effects of 0.3 mg/kg MA. In microdialysis studies, cumulatively administered MA (0.3–3 mg/kg), COC (3–56 mg/kg), and GBR 12909 (3–30 mg/kg) produced dose-dependent increases in DA efflux in the NAcb shell to maxima of approximately 1200 to 1300% of control values. The increase in DA levels produced by MA and COC was rapid and short-lived, whereas the effect of GBR 12909 was slower and longer lasting. Dose-related increases in MA lever selection produced by MA, COC, and GBR 12909 corresponded with graded increases in DA levels in the NAcb shell. Doses of MA, COC, and GBR 12909 that produced full substitution increased DA levels to approximately 200 to 400% of control values. Finally, cumulatively administered MA produced comparable changes in DA levels in both naive and 0.3 mg/kg MA-trained rats. These latter results suggest that sensitization of DA release does not play a prominent role in the discriminative stimulus effects of psychomotor stimulants. PMID:20190012

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].

PubMed

Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin

2016-05-01

An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study.

PubMed

Li, Yanyun; Chen, Minjian; Liu, Cuiping; Xia, Yankai; Xu, Bo; Hu, Yanhui; Chen, Ting; Shen, Meiping; Tang, Wei

2018-05-01

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Assessment of Fecal Microbiota and Fecal Metabolome in Symptomatic Uncomplicated Diverticular Disease of the Colon.

PubMed

Tursi, Antonio; Mastromarino, Paola; Capobianco, Daniela; Elisei, Walter; Miccheli, Alfredo; Capuani, Giorgio; Tomassini, Alberta; Campagna, Giuseppe; Picchio, Marcello; Giorgetti, GianMarco; Fabiocchi, Federica; Brandimarte, Giovanni

2016-10-01

The aim of this study was to assess fecal microbiota and metabolome in a population with symptomatic uncomplicated diverticular disease (SUDD). Whether intestinal microbiota and metabolic profiling may be altered in patients with SUDD is unknown. Stool samples from 44 consecutive women [15 patients with SUDD, 13 with asymptomatic diverticulosis (AD), and 16 healthy controls (HCs)] were analyzed. Real-time polymerase chain reaction was used to quantify targeted microorganisms. High-resolution proton nuclear magnetic resonance spectroscopy associated with multivariate analysis with partial least-square discriminant analysis (PLS-DA) was applied on the metabolite data set. The overall bacterial quantity did not differ among the 3 groups (P=0.449), with no difference in Bacteroides/Prevotella, Clostridium coccoides, Bifidobacterium, Lactobacillus, and Escherichia coli subgroups. The amount of Akkermansia muciniphila species was significantly different between HC, AD, and SUDD subjects (P=0.017). PLS-DA analysis of nuclear magnetic resonance -based metabolomics associated with microbiological data showed significant discrimination between HCs and AD patients (R=0.733; Q=0.383; P<0.05, LV=2). PLS analysis showed lower N-acetyl compound and isovalerate levels in AD, associated with higher levels of A. municiphila, as compared with the HC group. PLS-DA applied on AD and SUDD samples showed a good discrimination between these 2 groups (R=0.69; Q=0.35; LV=2). SUDD patients were characterized by low levels of valerate, butyrate, and choline and by high levels of N-acetyl derivatives and U1. SUDD and AD do not show colonic bacterial overgrowth, but a significant difference in the levels of fecal A. muciniphila was observed. Moreover, increasing expression of some metabolites as expression of different AD and SUDD metabolic activity was found.

Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics for comparison of caffeinated and decaffeinated coffee and its implications for Alzheimer's disease.

PubMed

Chang, Kai Lun; Ho, Paul C

2014-01-01

Findings from epidemiology, preclinical and clinical studies indicate that consumption of coffee could have beneficial effects against dementia and Alzheimer's disease (AD). The benefits appear to come from caffeinated coffee, but not decaffeinated coffee or pure caffeine itself. Therefore, the objective of this study was to use metabolomics approach to delineate the discriminant metabolites between caffeinated and decaffeinated coffee, which could have contributed to the observed therapeutic benefits. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics approach was employed to characterize the metabolic differences between caffeinated and decaffeinated coffee. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed distinct separation between the two types of coffee (cumulative Q(2) = 0.998). A total of 69 discriminant metabolites were identified based on the OPLS-DA model, with 37 and 32 metabolites detected to be higher in caffeinated and decaffeinated coffee, respectively. These metabolites include several benzoate and cinnamate-derived phenolic compounds, organic acids, sugar, fatty acids, and amino acids. Our study successfully established GC-TOF-MS based metabolomics approach as a highly robust tool in discriminant analysis between caffeinated and decaffeinated coffee samples. Discriminant metabolites identified in this study are biologically relevant and provide valuable insights into therapeutic research of coffee against AD. Our data also hint at possible involvement of gut microbial metabolism to enhance therapeutic potential of coffee components, which represents an interesting area for future research.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

PubMed

Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-07-15

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils. Copyright © 2016 Elsevier Ltd. All rights reserved.

Gas Chromatography Time-Of-Flight Mass Spectrometry (GC-TOF-MS)-Based Metabolomics for Comparison of Caffeinated and Decaffeinated Coffee and Its Implications for Alzheimer’s Disease

PubMed Central

Chang, Kai Lun; Ho, Paul C.

2014-01-01

Findings from epidemiology, preclinical and clinical studies indicate that consumption of coffee could have beneficial effects against dementia and Alzheimer’s disease (AD). The benefits appear to come from caffeinated coffee, but not decaffeinated coffee or pure caffeine itself. Therefore, the objective of this study was to use metabolomics approach to delineate the discriminant metabolites between caffeinated and decaffeinated coffee, which could have contributed to the observed therapeutic benefits. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics approach was employed to characterize the metabolic differences between caffeinated and decaffeinated coffee. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed distinct separation between the two types of coffee (cumulative Q2 = 0.998). A total of 69 discriminant metabolites were identified based on the OPLS-DA model, with 37 and 32 metabolites detected to be higher in caffeinated and decaffeinated coffee, respectively. These metabolites include several benzoate and cinnamate-derived phenolic compounds, organic acids, sugar, fatty acids, and amino acids. Our study successfully established GC-TOF-MS based metabolomics approach as a highly robust tool in discriminant analysis between caffeinated and decaffeinated coffee samples. Discriminant metabolites identified in this study are biologically relevant and provide valuable insights into therapeutic research of coffee against AD. Our data also hint at possible involvement of gut microbial metabolism to enhance therapeutic potential of coffee components, which represents an interesting area for future research. PMID:25098597

Identification of Terpenoid Chemotypes Among High (-)-trans-Δ9- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars.

PubMed

Fischedick, Justin T

2017-01-01

Introduction: With laws changing around the world regarding the legal status of Cannabis sativa (cannabis) it is important to develop objective classification systems that help explain the chemical variation found among various cultivars. Currently cannabis cultivars are named using obscure and inconsistent nomenclature. Terpenoids, responsible for the aroma of cannabis, are a useful group of compounds for distinguishing cannabis cultivars with similar cannabinoid content. Methods: In this study we analyzed terpenoid content of cannabis samples obtained from a single medical cannabis dispensary in California over the course of a year. Terpenoids were quantified by gas chromatography with flame ionization detection and peak identification was confirmed with gas chromatography mass spectrometry. Quantitative data from 16 major terpenoids were analyzed using hierarchical clustering analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Results: A total of 233 samples representing 30 cultivars were used to develop a classification scheme based on quantitative data, HCA, PCA, and OPLS-DA. Initially cultivars were divided into five major groups, which were subdivided into 13 classes based on differences in terpenoid profile. Different classification models were compared with PLS-DA and found to perform best when many representative samples of a particular class were included. Conclusion: A hierarchy of terpenoid chemotypes was observed in the data set. Some cultivars fit into distinct chemotypes, whereas others seemed to represent a continuum of chemotypes. This study has demonstrated an approach to classifying cannabis cultivars based on terpenoid profile.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

PubMed

Pereira, Hebert Vinicius; Amador, Victória Silva; Sena, Marcelo Martins; Augusti, Rodinei; Piccin, Evandro

2016-10-12

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit. Copyright © 2016 Elsevier B.V. All rights reserved.

Young infants’ perception of liquid coarticulatory influences on following stop consonants

PubMed Central

FOWLER, CAROL A.; BEST, CATHERINE T.; McROBERTS, GERALD W.

2009-01-01

Phonetic segments are coarticulated in speech. Accordingly, the articulatory and acoustic properties of the speech signal during the time frame traditionally identified with a given phoneme are highly context-sensitive. For example, due to carryover coarticulation, the front tongue-tip position for /l/ results in more fronted tongue-body contact for a /g/ preceded by /l/ than for a /g/ preceded by /r/. Perception by mature listeners shows a complementary sensitivity—when a synthetic /da/–/ga/ continuum is preceded by either /al/ or /ar/, adults hear more /g/s following /l/ rather than /r/. That is, some of the fronting information in the temporal domain of the stop is perceptually attributed to /l/ (Mann, 1980). We replicated this finding and extended it to a signal-detection test of discrimination with adults, using triads of disyllables. Three equidistant items from a /da/–/ga/ continuum were used preceded by /al/ and /ar/. In the identification test, adults had identified item ga5 as “ga,” and da1 as “da,” following both /al/ and /ar/, whereas they identified the crucial item d/ga3 predominantly as “ga” after /al/ but as “da” after /ar/. In the discrimination test, they discriminated d/ga3 from da1 preceded by /al/ but not /ar/; compatibly, they discriminated d/ga3 readily from ga5 preceded by /ar/ but poorly preceded by /al/. We obtained similar results with 4-month-old infants. Following habituation to either ald/ga3 or ard/ga3, infants heard either the corresponding ga5 or da1 disyllable. As predicted, the infants discriminated d/ga3 from da1 following /al/ but not /ar/; conversely, they discriminated d/ga3 from ga5 following /ar/ but not /al/. The results suggest that prelinguistic infants disentangle consonant-consonant coarticulatory influences in speech in an adult-like fashion. PMID:2270188

Comparison of the meat metabolite composition of Linwu and Pekin ducks using 600 MHz 1H nuclear magnetic resonance spectroscopy.

PubMed

Wang, Xiangrong; Fang, Chengkun; He, Jianhua; Dai, Qiuzhong; Fang, Rejun

2017-01-01

In an effort to further understand of the differences of meat flavor and texture between Linwu ducks and Pekin ducks at market age, we investigated the meat metabolite composition of the two breeds of ducks using 600 MHz 1 H nuclear magnetic resonance (NMR) spectroscopy. Comprehensive multivariate data analysis including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) were applied to analyze the 1 H-NMR profiling data to identify the distinguishing metabolites of breast meat between two breeds of ducks. Compared with 42-d-old Pekin duck meat, breast from 72-d-old Linwu duck has higher concentration of anserine, carnosine, homocarnosine, and nicotinamide, but significantly lower concentration of succinate, creatine, and myo-inositol. These results contribute to a better understanding of the differences in meat metabolite composition between 72-d-old Linwu and 42-d-old Pekin ducks, which could be used to help assess the quality of duck meat as a food. © 2016 Poultry Science Association Inc.

Quality Evaluation of Potentilla fruticosa L. by High Performance Liquid Chromatography Fingerprinting Associated with Chemometric Methods.

PubMed

Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue

2016-01-01

The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines.

Quality Evaluation of Potentilla fruticosa L. by High Performance Liquid Chromatography Fingerprinting Associated with Chemometric Methods

PubMed Central

Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue

2016-01-01

The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines. PMID:26890416

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis.

PubMed

Mabood, F; Boqué, R; Folcarelli, R; Busto, O; Jabeen, F; Al-Harrasi, Ahmed; Hussain, J

2016-05-15

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration. Copyright © 2016 Elsevier B.V. All rights reserved.

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis

NASA Astrophysics Data System (ADS)

Mabood, F.; Boqué, R.; Folcarelli, R.; Busto, O.; Jabeen, F.; Al-Harrasi, Ahmed; Hussain, J.

2016-05-01

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration.

Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting.

PubMed

Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao

2013-12-15

Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discrimination of genetically modified sugar beets based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Tao; Li, Zhi; Yin, Xianhua; Hu, Fangrong; Hu, Cong

2016-01-01

The objective of this paper was to apply terahertz (THz) spectroscopy combined with chemometrics techniques for discrimination of genetically modified (GM) and non-GM sugar beets. In this paper, the THz spectra of 84 sugar beet samples (36 GM sugar beets and 48 non-GM ones) were obtained by using terahertz time-domain spectroscopy (THz-TDS) system in the frequency range from 0.2 to 1.2 THz. Three chemometrics methods, principal component analysis (PCA), discriminant analysis (DA) and discriminant partial least squares (DPLS), were employed to classify sugar beet samples into two groups: genetically modified organisms (GMOs) and non-GMOs. The DPLS method yielded the best classification result, and the percentages of successful classification for GM and non-GM sugar beets were both 100%. Results of the present study demonstrate the usefulness of THz spectroscopy together with chemometrics methods as a powerful tool to distinguish GM and non-GM sugar beets.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: A comparative study between different modeling methods

NASA Astrophysics Data System (ADS)

Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram

2013-03-01

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm-1. Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.

Quality evaluation of yellow peach chips prepared by explosion puffing drying.

PubMed

Lyu, Jian; Zhou, Lin-Yan; Bi, Jin-Feng; Liu, Xuan; Wu, Xin-Ye

2015-12-01

Nineteen evaluation indicators in 15 yellow peach chips prepared by explosion puffing drying were analyzed, including color, rehydration ratio, texture, and so on. The analysis methods of principle component analysis (PCA), analytic hierarchy process (AHP), K-means cluster (KC) and Discriminate analysis (DA) were used to analyze the comprehensive quality of the yellow peach chips. The dispersed coefficient of variation of the 19 evaluation indicators varied from 3.58 to 852.89 %, suggesting significant differences among yellow peach cultivars. The characteristic evaluation indicators, namely, reducing sugar content, out-put ratio, water content, a value and L value were analyzed by PCA, and their weights 0.0429, 0.1140, 0.4816, 1.1807 and 0.1807 were obtained by AHP. The levels in 15 cultivars effectively were classified by discrimination functions which obtained by KC and DA. The results suggested that three levels of comprehensive quality for yellow peach chips were divided, and the highest synthesis scores was observed in "senggelin" (11.1037), while the lowest synthesis value was found in "goldbaby" (-3.7600).

Characterization of oils and fats by 1H NMR and GC/MS fingerprinting: classification, prediction and detection of adulteration.

PubMed

Fang, Guihua; Goh, Jing Yeen; Tay, Manjun; Lau, Hiu Fung; Li, Sam Fong Yau

2013-06-01

The correct identification of oils and fats is important to consumers from both commercial and health perspectives. Proton nuclear magnetic resonance ((1)H NMR) spectroscopy, gas chromatography-mass spectrometry (GC/MS) fingerprinting and chemometrics were employed successfully for the quality control of oils and fats. Principal component analysis (PCA) of both techniques showed group clustering of 14 types of oils and fats. Partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) using GC/MS data had excellent classification sensitivity and specificity compared to models using NMR data. Depending on the availability of the instruments, data from either technique can effectively be applied for the establishment of an oils and fats database to identify unknown samples. Partial least squares (PLS) models were successfully established for the detection of as low as 5% of lard and beef tallow spiked into canola oil, thus illustrating possible applications in Islamic and Jewish countries. Copyright © 2012 Elsevier Ltd. All rights reserved.

Infrared-metabolomics approach in detecting changes in Andrographis paniculata metabolites due to different harvesting ages and times.

PubMed

Yusof, Nur A'thifah; Isha, Azizul; Ismail, Intan Safinar; Khatib, Alfi; Shaari, Khozirah; Abas, Faridah; Rukayadi, Yaya

2015-09-01

The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm). OPLS-DA successfully discriminated the A. paniculata crude extracts into groups of which the main constituents - andrographolide and neoandrographolide - could be mainly observed in the morning session of 120 DAT for P and T, while H gave the highest intensities of these constituents at 150 DAT. The information extracted from ATR-FTIR data through OPLS-DA could be useful in tailoring this plant harvest stage in relation to the content of its two major diterpene lactones: andrographolide and neoandrographolide. © 2014 Society of Chemical Industry.

Recognition of beer brand based on multivariate analysis of volatile fingerprint.

PubMed

Cajka, Tomas; Riddellova, Katerina; Tomaniova, Monika; Hajslova, Jana

2010-06-18

Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach. Copyright (c) 2010 Elsevier B.V. All rights reserved.

ViDa1: The Development and Validation of a New Questionnaire for Measuring Health-Related Quality of Life in Patients with Type 1 Diabetes

PubMed Central

Alvarado-Martel, Dácil; Ruiz Fernández, M. Angeles; Cuadrado Vigaray, Maribel; Carrillo, Armando; Boronat, Mauro; Expósito Montesdeoca, Ana; Nattero Chávez, Lía; Pozuelo Sánchez, Maite; López Quevedo, Pino; Santana Suárez, Ana D.; Hillman, Natalia; Subias, David; Martin Vaquero, Pilar; Sáez de Ibarra, Lourdes; Mauricio, Didac; de Pablos-Velasco, Pedro; Nóvoa, Francisco J.; Wägner, Ana M.

2017-01-01

This study describes the development of a new questionnaire to measure health-related quality of life (HRQoL) in patients with type 1 diabetes (the ViDa1 questionnaire) and provides information on its psychometric properties. For its development, open interviews with patients took place and topics relevant to patients' HRQoL were identified and items were generated. Qualitative analysis of items, expert review, and refinement of the questionnaire followed. A pilot study (N = 150) was conducted to explore the underlying structure of the 40-item ViDa1 questionnaire. A Principal Component Analysis (PCA) was performed and six of the items that did not load on any of the factors were eliminated. The results supported a four-dimensional structure for ViDa1, the dimensions being Interference of diabetes in everyday life, Self-care, Well-being, and Worry about the disease. Subsequently, the PCA was repeated in a larger sample (N = 578) with the reduced 34-item version of the questionnaire, and a Confirmatory Factor Analysis (CFA) was performed (N = 428). Overall fit indices obtained presented adequate values which supported the four-factor model initially proposed [(χ(df=554)2= 2601.93) (p < 0.001); Root Mean Square Error of Approximation = 0.060 (CI = 0.056 −0.064)]. As regards reliability, the four dimensions of the ViDa1 demonstrated good internal consistency, with Cronbach's alphas ranging between 0.71 and 0.86. Evidence of convergent-discriminant validity in the form of high correlations with another specific HRQoL questionnaire for diabetes and low correlations with other constructs such as self-efficacy, anxiety, and depression were presented. The ViDa1 also discriminated between different aspects of clinical interest such as type of insulin treatment, presence of chronic complications, and glycemic control, temporal stability, and sensitivity to change after an intervention. In conclusion, the ViDa1 questionnaire presents adequate psychometric properties and may represent a good alternative for the evaluation of HRQoL in type 1 diabetes. PMID:28620331

ViDa1: The Development and Validation of a New Questionnaire for Measuring Health-Related Quality of Life in Patients with Type 1 Diabetes.

PubMed

Alvarado-Martel, Dácil; Ruiz Fernández, M Angeles; Cuadrado Vigaray, Maribel; Carrillo, Armando; Boronat, Mauro; Expósito Montesdeoca, Ana; Nattero Chávez, Lía; Pozuelo Sánchez, Maite; López Quevedo, Pino; Santana Suárez, Ana D; Hillman, Natalia; Subias, David; Martin Vaquero, Pilar; Sáez de Ibarra, Lourdes; Mauricio, Didac; de Pablos-Velasco, Pedro; Nóvoa, Francisco J; Wägner, Ana M

2017-01-01

This study describes the development of a new questionnaire to measure health-related quality of life (HRQoL) in patients with type 1 diabetes (the ViDa1 questionnaire) and provides information on its psychometric properties. For its development, open interviews with patients took place and topics relevant to patients' HRQoL were identified and items were generated. Qualitative analysis of items, expert review, and refinement of the questionnaire followed. A pilot study ( N = 150) was conducted to explore the underlying structure of the 40-item ViDa1 questionnaire. A Principal Component Analysis (PCA) was performed and six of the items that did not load on any of the factors were eliminated. The results supported a four-dimensional structure for ViDa1, the dimensions being Interference of diabetes in everyday life, Self-care, Well-being, and Worry about the disease. Subsequently, the PCA was repeated in a larger sample ( N = 578) with the reduced 34-item version of the questionnaire, and a Confirmatory Factor Analysis (CFA) was performed ( N = 428). Overall fit indices obtained presented adequate values which supported the four-factor model initially proposed [([Formula: see text] 2601.93) ( p < 0.001); Root Mean Square Error of Approximation = 0.060 (CI = 0.056 -0.064)]. As regards reliability, the four dimensions of the ViDa1 demonstrated good internal consistency, with Cronbach's alphas ranging between 0.71 and 0.86. Evidence of convergent-discriminant validity in the form of high correlations with another specific HRQoL questionnaire for diabetes and low correlations with other constructs such as self-efficacy, anxiety, and depression were presented. The ViDa1 also discriminated between different aspects of clinical interest such as type of insulin treatment, presence of chronic complications, and glycemic control, temporal stability, and sensitivity to change after an intervention. In conclusion, the ViDa1 questionnaire presents adequate psychometric properties and may represent a good alternative for the evaluation of HRQoL in type 1 diabetes.

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

NASA Astrophysics Data System (ADS)

Hu, Leqian; Ma, Shuai; Yin, Chunling

2018-03-01

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570 nm with excitation in the range of 320-480 nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Raman microspectroscopy of nucleus and cytoplasm for human colon cancer diagnosis.

PubMed

Liu, Wenjing; Wang, Hongbo; Du, Jingjing; Jing, Chuanyong

2017-11-15

Subcellular Raman analysis is a promising clinic tool for cancer diagnosis, but constrained by the difficulty of deciphering subcellular spectra in actual human tissues. We report a label-free subcellular Raman analysis for use in cancer diagnosis that integrates subcellular signature spectra by subtracting cytoplasm from nucleus spectra (Nuc.-Cyt.) with a partial least squares-discriminant analysis (PLS-DA) model. Raman mapping with the classical least-squares (CLS) model allowed direct visualization of the distribution of the cytoplasm and nucleus. The PLS-DA model was employed to evaluate the diagnostic performance of five types of spectral datasets, including non-selective, nucleus, cytoplasm, ratio of nucleus to cytoplasm (Nuc./Cyt.), and nucleus minus cytoplasm (Nuc.-Cyt.), resulting in diagnostic sensitivity of 88.3%, 84.0%, 98.4%, 84.5%, and 98.9%, respectively. Discriminating between normal and cancerous cells of actual human tissues through subcellular Raman markers is feasible, especially when using the nucleus-cytoplasm difference spectra. The subcellular Raman approach had good stability, and had excellent diagnostic performance for rectal as well as colon tissues. The insights gained from this study shed new light on the general applicability of subcellular Raman analysis in clinical trials. Copyright © 2017 Elsevier B.V. All rights reserved.

Authentication of animal origin of heparin and low molecular weight heparin including ovine, porcine and bovine species using 1D NMR spectroscopy and chemometric tools.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed

2018-02-05

High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

NASA Astrophysics Data System (ADS)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

FT-IR spectroscopy and multivariate analysis as an auxiliary tool for diagnosis of mental disorders: Bipolar and schizophrenia cases

NASA Astrophysics Data System (ADS)

Ogruc Ildiz, G.; Arslan, M.; Unsalan, O.; Araujo-Andrade, C.; Kurt, E.; Karatepe, H. T.; Yilmaz, A.; Yalcinkaya, O. B.; Herken, H.

2016-01-01

In this study, a methodology based on Fourier-transform infrared spectroscopy and principal component analysis and partial least square methods is proposed for the analysis of blood plasma samples in order to identify spectral changes correlated with some biomarkers associated with schizophrenia and bipolarity. Our main goal was to use the spectral information for the calibration of statistical models to discriminate and classify blood plasma samples belonging to bipolar and schizophrenic patients. IR spectra of 30 samples of blood plasma obtained from each, bipolar and schizophrenic patients and healthy control group were collected. The results obtained from principal component analysis (PCA) show a clear discrimination between the bipolar (BP), schizophrenic (SZ) and control group' (CG) blood samples that also give possibility to identify three main regions that show the major differences correlated with both mental disorders (biomarkers). Furthermore, a model for the classification of the blood samples was calibrated using partial least square discriminant analysis (PLS-DA), allowing the correct classification of BP, SZ and CG samples. The results obtained applying this methodology suggest that it can be used as a complimentary diagnostic tool for the detection and discrimination of these mental diseases.

PCA as a practical indicator of OPLS-DA model reliability.

PubMed

Worley, Bradley; Powers, Robert

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools.

PubMed

Canizo, Brenda V; Escudero, Leticia B; Pérez, María B; Pellerano, Roberto G; Wuilloud, Rodolfo G

2018-03-01

The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

¹H NMR spectroscopy reveals the effect of genotype and growth conditions on composition of sea buckthorn (Hippophaë rhamnoides L.) berries.

PubMed

Kortesniemi, Maaria; Sinkkonen, Jari; Yang, Baoru; Kallio, Heikki

2014-03-15

¹H NMR spectroscopy and multivariate data analysis were applied to the metabolic profiling and discrimination of wild sea buckthorn (Hippophaë rhamnoides L.) berries from different locations in Finland (subspecies (ssp.) rhamnoides) and China (ssp. sinensis). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed discrimination of the two subspecies and different growth sites. The discrimination of ssp. rhamnoides was mainly associated with typically higher temperature, radiation and humidity and lower precipitation in the south, yielding higher levels of O-ethyl β-d-glucopyranoside and d-glucose, and lower levels of malic, quinic and ascorbic acids. Significant metabolic differences (p<0.05) in genetically identical berries were observed between latitudes 60° and 67° north in Finland. High altitudes (> 2,000 m) correlated with greater levels of malic and ascorbic acids in ssp. sinensis. The NMR metabolomics approach applied here is effective for identification of metabolites, geographical origin and subspecies of sea buckthorn berries. Copyright © 2013 Elsevier Ltd. All rights reserved.

Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses.

PubMed

Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela

2017-12-15

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolite profiling of Clinacanthus nutans leaves extracts obtained from different drying methods by 1H NMR-based metabolomics

NASA Astrophysics Data System (ADS)

Hashim, Noor Haslinda Noor; Latip, Jalifah; Khatib, Alfi

2016-11-01

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

PubMed Central

Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties. PMID:29494626

Cognitive control predicted by color vision, and vice versa.

PubMed

Colzato, Lorenza S; Sellaro, Roberta; Hulka, Lea M; Quednow, Boris B; Hommel, Bernhard

2014-09-01

One of the most important functions of cognitive control is to continuously adapt cognitive processes to changing and often conflicting demands of the environment. Dopamine (DA) has been suggested to play a key role in the signaling and resolution of such response conflict. Given that DA is found in high concentration in the retina, color vision discrimination has been suggested as an index of DA functioning and in particular blue-yellow color vision impairment (CVI) has been used to indicate a central hypodopaminergic state. We used color discrimination (indexed by the total color distance score; TCDS) to predict individual differences in the cognitive control of response conflict, as reflected by conflict-resolution efficiency in an auditory Simon task. As expected, participants showing better color discrimination were more efficient in resolving response conflict. Interestingly, participants showing a blue-yellow CVI were associated with less efficiency in handling response conflict. Our findings indicate that color vision discrimination might represent a promising predictor of cognitive controlability in healthy individuals. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characteristic fingerprinting based on macamides for discrimination of maca (Lepidium meyenii) by LC/MS/MS and multivariate statistical analysis.

PubMed

Pan, Yu; Zhang, Ji; Li, Hong; Wang, Yuan-Zhong; Li, Wan-Yi

2016-10-01

Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca (Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty-seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC-UV/MS/MS). Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS-DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. When combined with a PLS-DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Metabolic Characterization of Peripheral Host Responses to Drainage-Resistant Klebsiella pneumoniae Liver Abscesses by Serum 1H-NMR Spectroscopy.

PubMed

Chang, Zhihui; Wang, Hairui; Li, Beibei; Liu, Zhaoyu; Zheng, Jiahe

2018-01-01

Purpose: To explore the metabolic characterization of host responses to drainage-resistant Klebsiella pneumoniae liver abscesses (DRKPLAs) with serum 1H-nuclear magnetic resonance (NMR) spectroscopy. Materials and Methods: The hospital records of all patients with a diagnosis of a liver abscess between June 2015 and December 2016 were retrieved from an electronic hospital database. Eighty-six patients with Klebsiella pneumoniae ( K. pneumoniae ) liver abscesses who underwent percutaneous drainage were identified. Twenty patients with confirmed DRKPLAs were studied. Moreover, we identified 20 consecutive patients with drainage-sensitive Klebsiella pneumoniae liver abscesses (DSKPLAs) as controls. Serum samples from the two groups were analyzed with 1H NMR spectroscopy. Partial least squares discriminant analysis (PLS-DA) was used to perform 1H NMR metabolic profiling. Metabolites were identified using the Human Metabolome Database, and pathway analysis was performed with MetaboAnalyst 3.0. Results: The PLS-DA test was able to discriminate between the two groups. Five key metabolites that contributed to their discrimination were identified. Glucose, lactate, and 3-hydroxybutyrate were found to be upregulated in DRKPLAs, whereas glutamine and alanine were downregulated compared with the DSKPLAs. Pathway analysis indicated that amino acid metabolisms were significantly different between the DRKPLAs and the DSKPLAs. The D-glutamine and D-glutamate metabolisms exhibited the greatest influences. Conclusions: The five key metabolites identified in our study may be potential targets for guiding novel therapeutics of DRKPLAs and are worthy of additional investigation.

Variation of metabolic profiles in developing maize kernels up- and down-regulated for the hda101 gene

PubMed Central

Castro, Cecilia; Motto, Mario; Rossi, Vincenzo; Manetti, Cesare

2008-01-01

To shed light on the specific contribution of HDA101 in modulating metabolic pathways in the maize seed, changes in the metabolic profiles of kernels obtained from hda101 mutant plants have been investigated by a metabonomic approach. Dynamic properties of chromatin folding can be mediated by enzymes that modify DNA and histones. The enzymes responsible for the steady-state of histone acetylation are histone acetyltransferase and histone deacetylase (HDA). Therefore, it is interesting to evaluate the effects of up- and down-regulation of a Rpd-3 type HDA on the development of maize seeds in terms of metabolic changes. This has been reached by analysing nuclear magnetic resonance spectra by different chemometrician approaches, such as Orthogonal Projection to Latent Structure-Discriminant Analysis, Parallel Factors Analysis, and Multi-way Partial Least Squares-Discriminant Analysis (N-PLS-DA). In particular, the latter approaches were chosen because they explicitly take time into account, organizing data into a set of slices that refer to different steps of the developing process. The results show the good discriminating capabilities of the N-PLS-DA approach, even if the number of samples ought be increased to obtain better predictive capabilities. However, using this approach, it was possible to show differences in the accumulation of metabolites during development and to highlight the changes occuring in the modified seeds. In particular, the results confirm the role of this gene in cell cycle control. PMID:18836140

Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil.

PubMed

Alves, Junia O; Botelho, Bruno G; Sena, Marcelo M; Augusti, Rodinei

2013-10-01

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils. Copyright © 2013 John Wiley & Sons, Ltd.

Detection of pit fragments in fresh cherries using near infrared spectroscopy

USDA-ARS?s Scientific Manuscript database

NIR spectroscopy in the wavelength region from 900nm to 2600nm was evaluated as the basis for a rapid, non-destructive method for the detection of pits and pit fragments in fresh cherries. Partial Least Squares discriminant analysis (PLS-DA) following various spectral pretreatments was applied to sp...

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: a comparative study between different modeling methods.

PubMed

Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram

2013-03-01

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm(-1). Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity. Copyright © 2012 Elsevier B.V. All rights reserved.

Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools.

PubMed

Ghasemi-Varnamkhasti, Mahdi; Amiri, Zahra Safari; Tohidi, Mojtaba; Dowlati, Majid; Mohtasebi, Seyed Saeid; Silva, Adenilton C; Fernandes, David D S; Araujo, Mário C U

2018-01-01

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds. Copyright © 2017 Elsevier B.V. All rights reserved.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Predicting First Graders' Development of Calculation versus Word-Problem Performance: The Role of Dynamic Assessment.

PubMed

Seethaler, Pamela M; Fuchs, Lynn S; Fuchs, Douglas; Compton, Donald L

2012-02-01

The purpose of this study was to assess the value of dynamic assessment (DA; degree of scaffolding required to learn unfamiliar mathematics content) for predicting 1(st)-grade calculations (CA) and word problems (WP) development, while controlling for the role of traditional assessments. Among 184 1(st) graders, predictors (DA, Quantity Discrimination, Test of Mathematics Ability, language, and reasoning) were assessed near the start of 1(st) grade. CA and WP were assessed near the end of 1(st) grade. Planned regression and commonality analyses indicated that for forecasting CA development, Quantity Discrimination, which accounted for 8.84% of explained variance, was the single most powerful predictor, followed by Test of Mathematics Ability and DA; language and reasoning were not uniquely predictive. By contrast, for predicting WP development, DA was the single most powerful predictor, which accounted for 12.01% of explained variance, with Test of Mathematics Ability, Quantity Discrimination, and language also uniquely predictive. Results suggest that different constellations of cognitive resources are required for CA versus WP development and that DA may be useful in predicting 1(st)-grade mathematics development, especially WP.

Predicting First Graders’ Development of Calculation versus Word-Problem Performance: The Role of Dynamic Assessment

PubMed Central

Seethaler, Pamela M.; Fuchs, Lynn S.; Fuchs, Douglas; Compton, Donald L.

2012-01-01

The purpose of this study was to assess the value of dynamic assessment (DA; degree of scaffolding required to learn unfamiliar mathematics content) for predicting 1st-grade calculations (CA) and word problems (WP) development, while controlling for the role of traditional assessments. Among 184 1st graders, predictors (DA, Quantity Discrimination, Test of Mathematics Ability, language, and reasoning) were assessed near the start of 1st grade. CA and WP were assessed near the end of 1st grade. Planned regression and commonality analyses indicated that for forecasting CA development, Quantity Discrimination, which accounted for 8.84% of explained variance, was the single most powerful predictor, followed by Test of Mathematics Ability and DA; language and reasoning were not uniquely predictive. By contrast, for predicting WP development, DA was the single most powerful predictor, which accounted for 12.01% of explained variance, with Test of Mathematics Ability, Quantity Discrimination, and language also uniquely predictive. Results suggest that different constellations of cognitive resources are required for CA versus WP development and that DA may be useful in predicting 1st-grade mathematics development, especially WP. PMID:22347725

Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration.

PubMed

Tsopelas, Fotios; Konstantopoulos, Dimitris; Kakoulidou, Anna Tsantili

2018-07-26

In the present work, two approaches for the voltammetric fingerprinting of oils and their combination with chemometrics were investigated in order to detect the adulteration of extra virgin olive oil with olive pomace oil as well as the most common seed oils, namely sunflower, soybean and corn oil. In particular, cyclic voltammograms of diluted extra virgin olive oils, regular (pure) olive oils (blends of refined olive oils with virgin olive oils), olive pomace oils and seed oils in presence of dichloromethane and 0.1 M of LiClO 4 in EtOH as electrolyte were recorded at a glassy carbon working electrode. Cyclic voltammetry was also employed in methanolic extracts of olive and seed oils. Datapoints of cyclic voltammograms were exported and submitted to Principal Component Analysis (PCA), Partial Least Square- Discriminant Analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). In diluted oils, PLS-DA provided a clear discrimination between olive oils (extra virgin and regular) and olive pomace/seed oils, while SIMCA showed a clear discrimination of extra virgin olive oil in regard to all other samples. Using methanolic extracts and considering datapoints recorded between 0.6 and 1.3 V, PLS-DA provided more information, resulting in three clusters-extra virgin olive oils, regular olive oils and seed/olive pomace oils-while SIMCA showed inferior performance. For the quantification of extra virgin olive oil adulteration with olive pomace oil or seed oils, a model based on Partial Least Square (PLS) analysis was developed. Detection limit of adulteration in olive oil was found to be 2% (v/v) and the linearity range up to 33% (v/v). Validation and applicability of all models was proved using a suitable test set. In the case of PLS, synthetic oil mixtures with 4 known adulteration levels in the range of 4-26% were also employed as a blind test set. Copyright © 2018 Elsevier B.V. All rights reserved.

Exploring the Effects of Geographical Origin on the Chemical Composition and Quality Grading of Vitis vinifera L. cv. Chardonnay Grapes.

PubMed

Gambetta, Joanna M; Cozzolino, Daniel; Bastian, Susan E P; Jeffery, David W

2017-01-31

The relationship between berry chemical composition, region of origin and quality grade was investigated for Chardonnay grapes sourced from vineyards located in seven South Australian Geographical Indications (GI). Measurements of basic chemical parameters, amino acids, elements, and free and bound volatiles were conducted for grapes collected during 2015 and 2016. Multiple factor analysis (MFA) was used to determine the sets of data that best discriminated each GI and quality grade. Important components for the discrimination of grapes based on GI were 2-phenylethanol, benzyl alcohol and C6 compounds, as well as Cu, Zn, and Mg, titratable acidity (TA), total soluble solids (TSS), and pH. Discriminant analysis (DA) based on MFA results correctly classified 100% of the samples into GI in 2015 and 2016. Classification according to grade was achieved based on the results for elements such as Cu, Na, Fe, volatiles including C6 and aryl alcohols, hydrolytically-released volatiles such as (Z)-linalool oxide and vitispirane, pH, TSS, alanine and proline. Correct classification through DA according to grade was 100% for both vintages. Significant correlations were observed between climate, GI, grade, and berry composition. Climate influenced the synthesis of free and bound volatiles as well as amino acids, sugars, and acids, as a result of higher temperatures and precipitation.

Metabolic Response to XD14 Treatment in Human Breast Cancer Cell Line MCF-7

PubMed Central

Pan, Daqiang; Kather, Michel; Willmann, Lucas; Schlimpert, Manuel; Bauer, Christoph; Lagies, Simon; Schmidtkunz, Karin; Eisenhardt, Steffen U.; Jung, Manfred; Günther, Stefan; Kammerer, Bernd

2016-01-01

XD14 is a 4-acyl pyrrole derivative, which was discovered by a high-throughput virtual screening experiment. XD14 inhibits bromodomain and extra-terminal domain (BET) proteins (BRD2, BRD3, BRD4 and BRDT) and consequently suppresses cell proliferation. In this study, metabolic profiling reveals the molecular effects in the human breast cancer cell line MCF-7 (Michigan Cancer Foundation-7) treated by XD14. A three-day time series experiment with two concentrations of XD14 was performed. Gas chromatography-mass spectrometry (GC-MS) was applied for untargeted profiling of treated and non-treated MCF-7 cells. The gained data sets were evaluated by several statistical methods: analysis of variance (ANOVA), clustering analysis, principle component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Cell proliferation was strongly inhibited by treatment with 50 µM XD14. Samples could be discriminated by time and XD14 concentration using PLS-DA. From the 117 identified metabolites, 67 were significantly altered after XD14 treatment. These metabolites include amino acids, fatty acids, Krebs cycle and glycolysis intermediates, as well as compounds of purine and pyrimidine metabolism. This massive intervention in energy metabolism and the lack of available nucleotides could explain the decreased proliferation rate of the cancer cells. PMID:27783056

Cerebrospinal fluid metabolomic profiling in tuberculous and viral meningitis: Screening potential markers for differential diagnosis.

PubMed

Li, Zihui; Du, Boping; Li, Jing; Zhang, Jinli; Zheng, Xiaojing; Jia, Hongyan; Xing, Aiying; Sun, Qi; Liu, Fei; Zhang, Zongde

2017-03-01

Tuberculous meningitis (TBM) is the most severe and frequent form of central nervous system tuberculosis. The current lack of efficient diagnostic tests makes it difficult to differentiate TBM from other common types of meningitis, especially viral meningitis (VM). Metabolomics is an important tool to identify disease-specific biomarkers. However, little metabolomic information is available on adult TBM. We used 1 H nuclear magnetic resonance-based metabolomics to investigate the metabolic features of the CSF from 18 TBM and 20 VM patients. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) were applied to analyze profiling data. Metabolites were identified using the Human Metabolome Database and pathway analysis was performed with MetaboAnalyst 3.0. The OSC-PLS-DA model could distinguish TBM from VM with high reliability. A total of 25 key metabolites that contributed to their discrimination were identified, including some, such as betaine and cyclohexane, rarely reported before in TBM. Pathway analysis indicated that amino acid and energy metabolism was significantly different in the CSF of TBM compared with VM. Twenty-five key metabolites identified in our study may be potential biomarkers for TBM differential diagnosis and are worthy of further investigation. Copyright © 2017 Elsevier B.V. All rights reserved.

Withdrawal, tolerance, and sensitization to dopamine mediated interoceptive cues in rats trained on a three-lever drug-discrimination task.

PubMed

Barrett, Robert J; Caul, William F; Smith, Randy

2005-05-01

In the present experiment rats were trained on a three-lever, drug-discrimination task to discriminate the cues associated with 0.30 mg/kg of the indirect dopamine (DA) agonist, amphetamine (AMPH), saline (SAL), and 0.03 mg/kg of the DA, D2 receptor antagonist, haloperidol (HAL). Choice behavior determined from tests on 0.30 and 0.15 mg/kg AMPH, SAL 0.03 and 0.015 mg/kg HAL provided a behavioral baseline presumed to represent changes along a continuum of DA mediated, interoceptive cues. Results from separate groups tested on 0.30 and 0.15 mg/kg AMPH, SAL, 0.03 and 0.015 mg/kg HAL, 24 h post-treatment with an acute 7.5 mg/kg dose of AMPH, showed rapid tolerance and withdrawal to the AMPH cue and sensitization to the HAL cue. The same tests 24 h following treatment with 1.0 mg/kg HAL showed rapid tolerance to the HAL cue, sensitization to the AMPH cue, but not AMPH-like withdrawal cues. Analysis of the results showed that tolerance to the AMPH and HAL cues reflected neuroadaptive baseline shifts and not weaker cue properties. These findings are consistent with predictions from opponent process theory of motivation and provide an animal model to study the motivational consequences that aversive symptoms of AMPH withdrawal such as dysphoria and anhedonia can have on drug-taking behavior.

Standoff detection of chemical and biological threats using laser-induced breakdown spectroscopy.

PubMed

Gottfried, Jennifer L; De Lucia, Frank C; Munson, Chase A; Miziolek, Andrzej W

2008-04-01

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for real-time chemical and biological warfare agent detection in the field. We have demonstrated the detection and discrimination of the biological warfare agent surrogates Bacillus subtilis (BG) (2% false negatives, 0% false positives) and ovalbumin (0% false negatives, 1% false positives) at 20 meters using standoff laser-induced breakdown spectroscopy (ST-LIBS) and linear correlation. Unknown interferent samples (not included in the model), samples on different substrates, and mixtures of BG and Arizona road dust have been classified with reasonable success using partial least squares discriminant analysis (PLS-DA). A few of the samples tested such as the soot (not included in the model) and the 25% BG:75% dust mixture resulted in a significant number of false positives or false negatives, respectively. Our preliminary results indicate that while LIBS is able to discriminate biomaterials with similar elemental compositions at standoff distances based on differences in key intensity ratios, further work is needed to reduce the number of false positives/negatives by refining the PLS-DA model to include a sufficient range of material classes and carefully selecting a detection threshold. In addition, we have demonstrated that LIBS can distinguish five different organophosphate nerve agent simulants at 20 meters, despite their similar stoichiometric formulas. Finally, a combined PLS-DA model for chemical, biological, and explosives detection using a single ST-LIBS sensor has been developed in order to demonstrate the potential of standoff LIBS for universal hazardous materials detection.

The application of compound-specific isotope analysis of fatty acids for traceability of sea cucumber (Apostichopus japonicus) in the coastal areas of China.

PubMed

Liu, Yu; Zhang, Xufeng; Li, Ying; Wang, Haixia

2017-11-01

Geographical origin traceability is an important issue for controlling the quality of seafood and safeguarding the interest of consumers. In the present study, a new method of compound-specific isotope analysis (CSIA) of fatty acids was established to evaluate its applicability in establishing the origin traceability of Apostichopus japonicus in the coastal areas of China. Moreover, principal component analysis (PCA) and discriminant analysis (DA) were applied to distinguish between the origins of A. japonicus. The results show that the stable carbon isotope compositions of fatty acids of A. japonicus significantly differ in terms of both season and origin. They also indicate that the stable carbon isotope composition of fatty acids could effectively discriminate between the origins of A. japonicus, except for between Changhai Island and Zhangzi Island in the spring of 2016 because of geographical proximity or the similarity of food sources. The fatty acids that have the highest contribution to identifying the geographical origins of A. japonicus are C22:6n-3, C16:1n-7, C20:5n-3, C18:0 and C23:1n-9, when considering the fatty acid contents, the stable carbon isotope composition of fatty acids and the results of the PCA and DA. We conclude that CSIA of fatty acids, combined with multivariate statistical analysis such as PCA and DA, may be an effective tool for establishing the traceability of A. japonicus in the coastal areas of China. The relevant conclusions of the present study provide a new method for determining the traceability of seafood or other food products. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Identification of the geographical origins of sea cucumber (Apostichopus japonicus) in northern China by using stable isotope ratios and fatty acid profiles.

PubMed

Zhang, Xufeng; Liu, Yu; Li, Ying; Zhao, Xinda

2017-03-01

Geographic traceability is an important issue for food quality and safety control of seafood. In this study,δ 13 C and δ 15 N values, as well as fatty acid (FA) content of 133 samples of A. japonicus from seven sampling points in northern China Sea were determined to evaluate their applicability in the origin traceability of A. japonicus. Principal component analysis (PCA) and discriminant analysis (DA) were applied to different data sets in order to evaluate their performance in terms of classification or predictive ability. δ 13 C and δ 15 N values could effectively discriminate between different origins of A. japonicus. Significant differences in the FA compositions showed the effectiveness of FA composition as a tool for distinguishing between different origins of A. japonicus. The two technologies, combined with multivariate statistical analysis, can be promising methods to discriminate A. japonicus from different geographical areas. Copyright © 2016. Published by Elsevier Ltd.

Traceability of 'Limone di Siracusa PGI' by a multidisciplinary analytical and chemometric approach.

PubMed

Amenta, M; Fabroni, S; Costa, C; Rapisarda, P

2016-11-15

Food traceability is increasingly relevant with respect to safety, quality and typicality issues. Lemon fruits grown in a typical lemon-growing area of southern Italy (Siracusa), have been awarded the PGI (Protected Geographical Indication) recognition as 'Limone di Siracusa'. Due to its peculiarity, consumers have an increasing interest about this product. The detection of potential fraud could be improved by using the tools linking the composition of this production to its typical features. This study used a wide range of analytical techniques, including conventional techniques and analytical approaches, such as spectral (NIR spectra), multi-elemental (Fe, Zn, Mn, Cu, Li, Sr) and isotopic ((13)C/(12)C, (18)O/(16)O) marker investigations, joined with multivariate statistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability system to verify the authenticity of 'Limone di Siracusa' production. The results demonstrated a very good geographical discrimination rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discrimination of Gastrodia elata from Different Geographical Origin for Quality Evaluation Using Newly-Build Near Infrared Spectrum Coupled with Multivariate Analysis.

PubMed

Zuo, Yamin; Deng, Xuehua; Wu, Qing

2018-05-04

Discrimination of Gastrodia elata ( G. elata ) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata , synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R p ²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm −1 ⁻5200 cm −1 , and 5600 cm −1 ⁻6000 cm −1 ) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.

Comparing performance of multinomial logistic regression and discriminant analysis for monitoring access to care for acute myocardial infarction.

PubMed

Hossain, Monir; Wright, Steven; Petersen, Laura A

2002-04-01

One way to monitor patient access to emergent health care services is to use patient characteristics to predict arrival time at the hospital after onset of symptoms. This predicted arrival time can then be compared with actual arrival time to allow monitoring of access to services. Predicted arrival time could also be used to estimate potential effects of changes in health care service availability, such as closure of an emergency department or an acute care hospital. Our goal was to determine the best statistical method for prediction of arrival intervals for patients with acute myocardial infarction (AMI) symptoms. We compared the performance of multinomial logistic regression (MLR) and discriminant analysis (DA) models. Models for MLR and DA were developed using a dataset of 3,566 male veterans hospitalized with AMI in 81 VA Medical Centers in 1994-1995 throughout the United States. The dataset was randomly divided into a training set (n = 1,846) and a test set (n = 1,720). Arrival times were grouped into three intervals on the basis of treatment considerations: <6 hours, 6-12 hours, and >12 hours. One model for MLR and two models for DA were developed using the training dataset. One DA model had equal prior probabilities, and one DA model had proportional prior probabilities. Predictive performance of the models was compared using the test (n = 1,720) dataset. Using the test dataset, the proportions of patients in the three arrival time groups were 60.9% for <6 hours, 10.3% for 6-12 hours, and 28.8% for >12 hours after symptom onset. Whereas the overall predictive performance by MLR and DA with proportional priors was higher, the DA models with equal priors performed much better in the smaller groups. Correct classifications were 62.6% by MLR, 62.4% by DA using proportional prior probabilities, and 48.1% using equal prior probabilities of the groups. The misclassifications by MLR for the three groups were 9.5%, 100.0%, 74.2% for each time interval, respectively. Misclassifications by DA models were 9.8%, 100.0%, and 74.4% for the model with proportional priors and 47.6%, 79.5%, and 51.0% for the model with equal priors. The choice of MLR or DA with proportional priors, or DA with equal priors for monitoring time intervals of predicted hospital arrival time for a population should depend on the consequences of misclassification errors.

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

PubMed

Xiang, Ling-Li; Li, Meng-Hua; Li, Jing-Mingz; Li, Jun-Hui; Zhang, Lu-Da; Zhao, Long-Lian

2014-10-01

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

Discrimination of Aspergillus isolates at the species and strain level by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprinting.

PubMed

Hettick, Justin M; Green, Brett J; Buskirk, Amanda D; Kashon, Michael L; Slaven, James E; Janotka, Erika; Blachere, Francoise M; Schmechel, Detlef; Beezhold, Donald H

2008-09-15

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to generate highly reproducible mass spectral fingerprints for 12 species of fungi of the genus Aspergillus and 5 different strains of Aspergillus flavus. Prior to MALDI-TOF MS analysis, the fungi were subjected to three 1-min bead beating cycles in an acetonitrile/trifluoroacetic acid solvent. The mass spectra contain abundant peaks in the range of 5 to 20kDa and may be used to discriminate between species unambiguously. A discriminant analysis using all peaks from the MALDI-TOF MS data yielded error rates for classification of 0 and 18.75% for resubstitution and cross-validation methods, respectively. If a subset of 28 significant peaks is chosen, resubstitution and cross-validation error rates are 0%. Discriminant analysis of the MALDI-TOF MS data for 5 strains of A. flavus using all peaks yielded error rates for classification of 0 and 5% for resubstitution and cross-validation methods, respectively. These data indicate that MALDI-TOF MS data may be used for unambiguous identification of members of the genus Aspergillus at both the species and strain levels.

Quantification and Discrimination of in Vitro Regeneration Swertia nervosa at Different Growth Periods using the UPLC/UV Coupled with Chemometric Method.

PubMed

Li, Jie; Zhang, Ji; Zuo, Zhitian; Huang, Hengyu; Wang, Yuanzhong

2018-05-09

Background : Swertia nervosa (Wall. ex G. Don) C. B. Clarke, a promising traditional herbal medicine for the treatment of liver disorders, is endangered due to its extensive collection and unsustainable harvesting practices. Objective : The aim of this study is to discuss the diversity of metabolites (loganic acid, sweroside, swertiamarin, and gentiopicroside) at different growth stages and organs of Swertia nervosa using the ultra-high-performance LC (UPLC)/UV coupled with chemometric method. Methods : UPLC data, UV data, and data fusion were treated separately to find more useful information by partial least-squares discriminant analysis (PLS-DA). Hierarchical cluster analysis (HCA), an unsupervised method, was then employed for validating the results from PLS-DA. Results : Three strategies displayed different chemical information associated with the sample discrimination. UV information mainly contributed to the classification of different organs; UPLC information was prominently responsible for both organs and growth periods; the data fusion did not perform with apparent superiority compared with single data analysis, although it provided useful information to differentiate leaves that could not be recognized by UPLC. The quantification result showed that the content of swertiamarin was the highest compared with the other three metabolites, especially in leaves at the rooted stage (19.57 ± 5.34 mg/g). Therefore, we speculated that interactive transformations occurred among these four metabolites, facilitated by root formation. Conclusions : This work will contribute to exploitation of bioactive compounds of S. nervosa , as well as its large-scale propagation. Highlights : The roots formation may influence the distribution and accumulation of metabolites.

A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study.

PubMed

Lee, Yeseung; Khan, Adnan; Hong, Seri; Jee, Sun Ha; Park, Youngja H

2017-05-30

Identifying changes in serum metabolites during cerebral ischemia is an important approach for early diagnosis of thrombotic stroke. Herein, we highlight novel biomarkers for early diagnosis of patients at high risk of thrombotic stroke using high resolution metabolomics (HRM). In this retrospective cohort study, serum samples obtained from patients at risk of thrombotic stroke (n  =  62) and non-risk individuals (n  =  348) were tested using HRM, coupled with LC-MS/MS, to discriminate between metabolic profiles of control and stroke risk patients. Multivariate analysis and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed to determine the top 5% metabolites within 95% group identities, followed by filtering with p-value <0.05 and annotating significant metabolites using a Metlin database. Mapping identified features from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Mummichog resulted in 341 significant features based on OPLS-DA with p-value <0.05. Among these 341 features, nine discriminated the thrombotic stroke risk group from the control group: low levels of N 6 -acetyl-l-lysine, 5-aminopentanoate, cadaverine, 2-oxoglutarate, nicotinamide, l-valine, S-(2-methylpropionyl)-dihydrolipoamide-E and ubiquinone, and elevated levels of homocysteine sulfinic acid. Further analysis showed that these metabolite biomarkers are specifically related to stroke occurrence, and unrelated to other factors such as diabetes or smoking. Lower levels of lysine catabolites in thrombotic stroke risk patients, as compared to the control, supports targeting these compounds as novel biomarkers for early and non-invasive detection of a thrombotic stroke.

Patterns of Circulating Inflammatory Biomarkers in Older Persons with Varying Levels of Physical Performance: A Partial Least Squares-Discriminant Analysis Approach

PubMed Central

Marzetti, Emanuele; Landi, Francesco; Marini, Federico; Cesari, Matteo; Buford, Thomas W.; Manini, Todd M.; Onder, Graziano; Pahor, Marco; Bernabei, Roberto; Leeuwenburgh, Christiaan; Calvani, Riccardo

2014-01-01

Background: Chronic, low-grade inflammation and declining physical function are hallmarks of the aging process. However, previous attempts to correlate individual inflammatory biomarkers with physical performance in older people have produced mixed results. Given the complexity of the inflammatory response, the simultaneous analysis of an array of inflammatory mediators may provide more insights into the relationship between inflammation and age-related physical function decline. This study was designed to explore the association between a panel of inflammatory markers and physical performance in older adults through a multivariate statistical approach. Methods: Community-dwelling older persons were categorized into “normal walkers” (NWs; n = 27) or “slow walkers” (SWs; n = 11) groups using 0.8 m s−1 as the 4-m gait speed cutoff. A panel of 14 circulating inflammatory biomarkers was assayed by multiplex analysis. Partial least squares-discriminant analysis (PLS-DA) was used to identify patterns of inflammatory mediators associated with gait speed categories. Results: The optimal complexity of the PLS-DA model was found to be five latent variables. The proportion of correct classification was 88.9% for NW subjects (74.1% in cross-validation) and 90.9% for SW individuals (81.8% in cross-validation). Discriminant biomarkers in the model were interleukin 8, myeloperoxidase, and tumor necrosis factor alpha (all higher in the SW group), and P-selectin, interferon gamma, and granulocyte–macrophage colony-stimulating factor (all higher in the NW group). Conclusion: Distinct profiles of circulating inflammatory biomarkers characterize older subjects with different levels of physical performance. The dissection of these patterns may provide novel insights into the role played by inflammation in the disabling cascade and possible new targets for interventions. PMID:25593902

Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification.

PubMed

Durán Merás, Isabel; Domínguez Manzano, Jaime; Airado Rodríguez, Diego; Muñoz de la Peña, Arsenio

2018-02-01

Within olive oils, extra virgin olive oil is the highest quality and, in consequence, the most expensive one. Because of that, it is common that some merchants attempt to take economic advantage by mixing it up with other less expensive oils, like olive oil or olive pomace oil. In consequence, the characterization and authentication of extra virgin olive oils is a subject of great interest, both for industry and consumers. This paper reports the potential of front-face total fluorescence spectroscopy combined with second-order chemometric methods for the detection of extra virgin olive oils adulteration with other olive oils. Excitation-emission matrices (EEMs) of extra virgin olive oils and extra virgin olive oils adulterated with olive oils or with olive pomace oils were recorded using front-face fluorescence spectroscopy. The full information content in these fluorescence images was analyzed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA-PARAFAC), and discriminant unfolded partial least-squares (DA-UPLS). The discriminant ability of LDA-PARAFAC was studied through the tridimensional plots of the canonical vectors, defining a surface separating the established categories. For DA-UPLS, the discriminant ability was established through the bidimensional plots of predicted values of calibration and validation samples, in order to assign each sample to a given class. The models demonstrated the possibility of detecting adulterations of extra virgin olive oils with percentages of around 15% and 3% of olive and olive pomace oils, respectively. Also, UPLS regression was used to quantify the adulteration level of extra virgin olive oils with olive oils or with olive pomace oils. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice.

PubMed

Schönig, Sarah; Recke, Andreas; Hirose, Misa; Ludwig, Ralf J; Seeger, Karsten

2013-06-26

Epidermolysis bullosa acquisita (EBA) is a rare skin blistering disease with a prevalence of 0.2/ million people. EBA is characterized by autoantibodies against type VII collagen. Type VII collagen builds anchoring fibrils that are essential for the dermal-epidermal junction. The pathogenic relevance of antibodies against type VII collagen subdomains has been demonstrated both in vitro and in vivo. Despite the multitude of clinical and immunological data, no information on metabolic changes exists. We used an animal model of EBA to obtain insights into metabolomic changes during EBA. Sera from mice with immunization-induced EBA and control mice were obtained and metabolites were isolated by filtration. Proton nuclear magnetic resonance (NMR) spectra were recorded and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) and random forest. The metabolic pattern of immunized mice and control mice could be clearly distinguished with PCA and PLS-DA. Metabolites that contribute to the discrimination could be identified via random forest. The observed changes in the metabolic pattern of EBA sera, i.e. increased levels of amino acid, point toward an increased energy demand in EBA. Knowledge about metabolic changes due to EBA could help in future to assess the disease status during treatment. Confirming the metabolic changes in patients needs probably large cohorts.

Dopamine D3 Receptors Mediate the Discriminative Stimulus Effects of Quinpirole in Free-Feeding Rats

PubMed Central

Baladi, Michelle G.; Newman, Amy H.

2010-01-01

The discriminative stimulus effects of dopamine (DA) D3/D2 receptor agonists are thought to be mediated by D2 receptors. To maintain responding, access to food is often restricted, which can alter neurochemical and behavioral effects of drugs acting on DA systems. This study established stimulus control with quinpirole in free-feeding rats and tested the ability of agonists to mimic and antagonists to attenuate the effects of quinpirole. The same antagonists were studied for their ability to attenuate quinpirole-induced yawning and hypothermia. DA receptor agonists apomorphine and lisuride, but not amphetamine and morphine, occasioned responding on the quinpirole lever. The discriminative stimulus effects of quinpirole were attenuated by the D3 receptor-selective antagonist N-{4-[4-(2,3-dichlorophenyl)-piperazin-1-yl]-trans-but-2-enyl}-4-pyridine-2-yl-benzamide HCl (PG01037) and the nonselective D3/D2 receptor antagonist raclopride, but not by the D2 receptor-selective antagonist 3-[4-(4-chlorophenyl)-4-hydroxypiperidin-1-yl]methyl-1H-indole (L-741,626); the potencies of PG01037 and raclopride to antagonize this effect of quinpirole paralleled their potencies to antagonize the ascending limb of the quinpirole yawning dose-response curve (thought to be mediated by D3 receptors). L-741,626 selectively antagonized the descending limb of the quinpirole yawning dose-response curve, and both L-741,626 and raclopride, but not PG01037, antagonized the hypothermic effects of quinpirole (thought to be mediated by D2 receptors). Food restriction (10 g/day/7 days) significantly decreased quinpirole-induced yawning without affecting the quinpirole discrimination. Many discrimination studies on DA receptor agonists use food-restricted rats; together with those studies, the current experiment using free-feeding rats suggests that feeding conditions affecting the behavioral effects of direct-acting DA receptor agonists might also have an impact on the effects of indirect-acting agonists such as cocaine and amphetamine. PMID:19797621

Dopamine D3 receptors mediate the discriminative stimulus effects of quinpirole in free-feeding rats.

PubMed

Baladi, Michelle G; Newman, Amy H; France, Charles P

2010-01-01

The discriminative stimulus effects of dopamine (DA) D3/D2 receptor agonists are thought to be mediated by D2 receptors. To maintain responding, access to food is often restricted, which can alter neurochemical and behavioral effects of drugs acting on DA systems. This study established stimulus control with quinpirole in free-feeding rats and tested the ability of agonists to mimic and antagonists to attenuate the effects of quinpirole. The same antagonists were studied for their ability to attenuate quinpirole-induced yawning and hypothermia. DA receptor agonists apomorphine and lisuride, but not amphetamine and morphine, occasioned responding on the quinpirole lever. The discriminative stimulus effects of quinpirole were attenuated by the D3 receptor-selective antagonist N-{4-[4-(2,3-dichlorophenyl)-piperazin-1-yl]-trans-but-2-enyl}-4-pyridine-2-yl-benzamide HCl (PG01037) and the nonselective D3/D2 receptor antagonist raclopride, but not by the D2 receptor-selective antagonist 3-[4-(4-chlorophenyl)-4-hydroxypiperidin-1-yl]methyl-1H-indole (L-741,626); the potencies of PG01037 and raclopride to antagonize this effect of quinpirole paralleled their potencies to antagonize the ascending limb of the quinpirole yawning dose-response curve (thought to be mediated by D3 receptors). L-741,626 selectively antagonized the descending limb of the quinpirole yawning dose-response curve, and both L-741,626 and raclopride, but not PG01037, antagonized the hypothermic effects of quinpirole (thought to be mediated by D2 receptors). Food restriction (10 g/day/7 days) significantly decreased quinpirole-induced yawning without affecting the quinpirole discrimination. Many discrimination studies on DA receptor agonists use food-restricted rats; together with those studies, the current experiment using free-feeding rats suggests that feeding conditions affecting the behavioral effects of direct-acting DA receptor agonists might also have an impact on the effects of indirect-acting agonists such as cocaine and amphetamine.

[Validity and reproducibility of Escala de Evaluación da Insatisfación Corporal para Adolescentes].

PubMed

Conti, Maria Aparecida; Slater, Betzabeth; Latorre, Maria do Rosário Dias de Oliveira

2009-06-01

To validate a body dissatisfaction scale for adolescents. The study included 386 female and male adolescents aged 10 to 17 years enrolled in a private elementary and middle school in the city of São Bernardo do Campo, southeastern Brazil, in 2006. 'Escala de Evaluación da Insatisfación Corporal para Adolescentes' (body dissatisfaction scale for adolescents) was translated and culturally adapted. The Portuguese instrument was evaluated for internal consistency using Cronbach's alpha, factor analysis with Varimax rotation, discriminant validity by comparing score means according to nutritional status (low weight, normal weight, and at risk of overweight and obesity) using the Kruskal-Wallis test. Concurrent validity was assessed using Spearman's rank correlation coefficient between scores and body mass index, waist-hip ratio and waist circumference. Reproducibility was evaluated using Wilcoxon test, and intraclass correlation coefficient. The translated and back-translated scale showed good agreement with the original one. The translated scale had good internal consistency in all subgroups studied (males and females in early and intermediate adolescence) and was able to discriminate adolescents according to their nutritional status. In the concurrent analysis, all three measures were correlated, except for males in early adolescence. Its reproducibility was ascertained. The 'Escala de Evaluación da Insatisfación Corporal para Adolescentes' was successfully translated into Portuguese and adapted to the Brazilian background and showed good results. It is recommended for the evaluation of the attitudinal component of body image in adolescents.

Determination of the Characteristics and Classification of Near-Infrared Spectra of Patchouli Oil (Pogostemon Cablin Benth.) from Different Origin

NASA Astrophysics Data System (ADS)

Diego, M. C. R.; Purwanto, Y. A.; Sutrisno; Budiastra, I. W.

2018-05-01

Research related to the non-destructive method of near-infrared (NIR) spectroscopy in aromatic oil is still in development in Indonesia. The objectives of the study were to determine the characteristics of the near-infrared spectra of patchouli oil and classify it based on its origin. The samples were selected from seven different places in Indonesia (Bogor and Garut from West Java, Aceh, and Jambi from Sumatra and Konawe, Masamba and Kolaka from Sulawesi Island). The spectral data of patchouli oil was obtained by FT-NIR spectrometer at the wavelength of 1000-2500 nm, and after that, the samples were subjected to composition analysis using Gas Chromatography-Mass Spectrometry. The transmittance and absorbance spectra were analyzed and then principal component analysis (PCA) was carried out. Discriminant analysis (DA) of the principal component was developed to classify patchouli oil based on its origin. The result shows that the data of both spectra (transmittance and absorbance spectra) by the PC analysis give a similar result for discriminating the seven types of patchouli oil due to their distribution and behavior. The DA of the three principal component in both data processed spectra could classify patchouli oil accurately. This result exposed that NIR spectroscopy can be successfully used as a correct method to classify patchouli oil based on its origin.

Mass Spectrometry and Fourier Transform Infrared Spectroscopy for Analysis of Biological Materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Timothy J.

Time-of-flight mass spectrometry along with statistical analysis was utilized to study metabolic profiles among rats fed resistant starch (RS) diets. Fischer 344 rats were fed four starch diets consisting of 55% (w/w, dbs) starch. A control starch diet consisting of corn starch was compared against three RS diets. The RS diets were high-amylose corn starch (HA7), HA7 chemically modified with octenyl succinic anhydride, and stearic-acid-complexed HA7 starch. A subgroup received antibiotic treatment to determine if perturbations in the gut microbiome were long lasting. A second subgroup was treated with azoxymethane (AOM), a carcinogen. At the end of the eight weekmore » study, cecal and distal-colon contents samples were collected from the sacrificed rats. Metabolites were extracted from cecal and distal colon samples into acetonitrile. The extracts were then analyzed on an accurate-mass time-of-flight mass spectrometer to obtain their metabolic profile. The data were analyzed using partial least-squares discriminant analysis (PLS-DA). The PLS-DA analysis utilized a training set and verification set to classify samples within diet and treatment groups. PLS-DA could reliably differentiate the diet treatments for both cecal and distal colon samples. The PLS-DA analyses of the antibiotic and no antibiotic treated subgroups were well classified for cecal samples and modestly separated for distal-colon samples. PLS-DA analysis had limited success separating distal colon samples for rats given AOM from those not treated; the cecal samples from AOM had very poor classification. Mass spectrometry profiling coupled with PLS-DA can readily classify metabolite differences among rats given RS diets.« less

Speech Perception Deficits in Poor Readers: Auditory Processing or Phonological Coding?

ERIC Educational Resources Information Center

Mody, Maria; And Others

1997-01-01

Forty second-graders, 20 good and 20 poor readers, completed a /ba/-/da/ temporal order judgment (TOJ) task. The groups did not differ in TOJ when /ba/ and /da/ were paired with more easily discriminated syllables. Poor readers' difficulties with /ba/-/da/ reflected perceptual confusion between phonetically similar syllables rather than difficulty…

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

PubMed

Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

2016-01-22

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Using color histograms and SPA-LDA to classify bacteria.

PubMed

de Almeida, Valber Elias; da Costa, Gean Bezerra; de Sousa Fernandes, David Douglas; Gonçalves Dias Diniz, Paulo Henrique; Brandão, Deysiane; de Medeiros, Ana Claudia Dantas; Véras, Germano

2014-09-01

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

Non-destructive fraud detection in rosehip oil by MIR spectroscopy and chemometrics.

PubMed

Santana, Felipe Bachion de; Gontijo, Lucas Caixeta; Mitsutake, Hery; Mazivila, Sarmento Júnior; Souza, Leticia Maria de; Borges Neto, Waldomiro

2016-10-15

Rosehip oil (Rosa eglanteria L.) is an important oil in the food, pharmaceutical and cosmetic industries. However, due to its high added value, it is liable to adulteration with other cheaper or lower quality oils. With this perspective, this work provides a new simple, fast and accurate methodology using mid-infrared (MIR) spectroscopy and partial least squares discriminant analysis (PLS-DA) as a means to discriminate authentic rosehip oil from adulterated rosehip oil containing soybean, corn and sunflower oils in different proportions. The model showed excellent sensitivity and specificity with 100% correct classification. Therefore, the developed methodology is a viable alternative for use in the laboratory and industry for standard quality analysis of rosehip oil since it is fast, accurate and non-destructive. Copyright © 2016 Elsevier Ltd. All rights reserved.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods

NASA Astrophysics Data System (ADS)

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-01

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation.

Noninvasive Recognition and Biomarkers of Early Allergic Asthma in Cats Using Multivariate Statistical Analysis of NMR Spectra of Exhaled Breath Condensate

PubMed Central

Fulcher, Yan G.; Fotso, Martial; Chang, Chee-Hoon; Rindt, Hans; Reinero, Carol R.

2016-01-01

Asthma is prevalent in children and cats, and needs means of noninvasive diagnosis. We sought to distinguish noninvasively the differences in 53 cats before and soon after induction of allergic asthma, using NMR spectra of exhaled breath condensate (EBC). Statistical pattern recognition was improved considerably by preprocessing the spectra with probabilistic quotient normalization and glog transformation. Classification of the 106 preprocessed spectra by principal component analysis and partial least squares with discriminant analysis (PLS-DA) appears to be impaired by variances unrelated to eosinophilic asthma. By filtering out confounding variances, orthogonal signal correction (OSC) PLS-DA greatly improved the separation of the healthy and early asthmatic states, attaining 94% specificity and 94% sensitivity in predictions. OSC enhancement of multi-level PLS-DA boosted the specificity of the prediction to 100%. OSC-PLS-DA of the normalized spectra suggest the most promising biomarkers of allergic asthma in cats to include increased acetone, metabolite(s) with overlapped NMR peaks near 5.8 ppm, and a hydroxyphenyl-containing metabolite, as well as decreased phthalate. Acetone is elevated in the EBC of 74% of the cats with early asthma. The noninvasive detection of early experimental asthma, biomarkers in EBC, and metabolic perturbation invite further investigation of the diagnostic potential in humans. PMID:27764146

Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA).

PubMed

Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder; Chong, Ving Ching

2016-01-01

Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.

Metabolomic Profile of Ards by Nuclear Magnetic Resonance Spectroscopy in Patients with H1N1 Influenza Virus Pneumonia.

PubMed

Izquierdo-Garcia, Jose L; Nin, Nicolas; Jimenez-Clemente, Jorge; Horcajada, Juan P; Arenas-Miras, Maria Del Mar; Gea, Joaquim; Esteban, Andres; Ruiz-Cabello, Jesus; Lorente, Jose A

2017-12-29

The integrated analysis of changes in the metabolic profile could be critical for the discovery of biomarkers of lung injury, and also for generating new pathophysiological hypotheses and designing novel therapeutic targets for the acute respiratory distress syndrome (ARDS). This study aimed at developing a Nuclear Magnetic Resonance (NMR)-based approach for the identification of the metabolomic profile of ARDS in patients with H1N1 influenza virus pneumonia. Serum samples from 30 patients (derivation set) diagnosed of H1N1 influenza virus pneumonia were analysed by unsupervised Principal Component Analysis (PCA) to identify metabolic differences between patients with and without ARDS by NMR-spectroscopy. A predictive model of partial least squares discriminant analysis (PLS-DA) was developed for the identification of ARDS. PLS-DA was trained with the derivation set and tested in another set of samples from 26 patients also diagnosed of H1N1 influenza virus pneumonia (validation set). Decreased serum glucose, alanine, glutamine, methylhistidine and fatty acids concentrations, and elevated serum phenylalanine and methylguanidine concentrations, discriminated patients with ARDS versus patients without ARDS. PLS-DA model successfully identified the presence of ARDS in the validation set with a success rate of 92% (sensitivity 100% and specificity 91%). The classification functions showed a good correlation with the Sequential Organ Failure Assessment (SOFA) score (R = 0.74, p < 0.0001) and the Pa02/Fi02 ratio (R = 0.41, p = 0.03). The serum metabolomic profile is sensitive and specific to identify ARDS in patients with H1N1 influenza A pneumonia. Future studies are needed to determine the role of NMR-spectroscopy as a biomarker of ARDS.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

NMR-based plasma metabolomic discrimination for male fertility assessment of rats treated with Eurycoma longifolia extracts.

PubMed

Ebrahimi, Forough; Ibrahim, Baharudin; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Chan, Kit-Lam

2017-06-01

Male infertility is one of the leading causes of infertility which affects many couples worldwide. Semen analysis is a routine examination of male fertility status which is usually performed on semen samples obtained through masturbation that may be inconvenient to patients. Eurycoma longifolia (Tongkat Ali, TA), native to Malaysia, has been traditionally used as a remedy to boost male fertility. In our recent studies in rats, upon the administration of high-quassinoid content extracts of TA including TA water (TAW), quassinoid-rich TA (TAQR) extracts, and a low-quassinoid content extract including quassinoid-poor TA (TAQP) extract, sperm count (SC) increased in TAW- and TAQR-treated rats when compared to the TAQP-treated and control groups. Consequently, the rats were divided into normal- (control and TAQP-treated) and high- (TAW- and TAQR-treated) SC groups [Ebrahimi et al. 2016]. Post-treatment rat plasma was collected. An optimized plasma sample preparation method was developed with respect to the internal standards sodium 3- (trimethylsilyl) propionate- 2,2,3,3- d4 (TSP) and deuterated 4-dimethyl-4-silapentane-1-ammonium trifluoroacetate (DSA). Carr-Purcell-Meibum-Gill (CPMG) experiments combined with orthogonal partial least squares discriminant analysis (OPLS-DA) was employed to evaluate plasma metabolomic changes in normal- and high-SC rats. The potential biomarkers associated with SC increase were investigated to assess fertility by capturing the metabolomic profile of plasma. DSA was selected as the optimized internal standard for plasma analysis due to its significantly smaller half-height line width (W h/2 ) compared to that of TSP. The validated OPLS-DA model clearly discriminated the CPMG profiles in regard to the SC level. Plasma profiles of the high-SC group contained higher levels of alanine, lactate, and histidine, while ethanol concentration was significantly higher in the normal-SC group. This approach might be a new alternative applicable to the fertility assessment in humans through the quantitative metabolomic analysis of plasma without requiring semen. TA: Tongkat Ali; LOD: limit of detection; LOQ: limit of quantification; HPLC-UV: high performance liquid chromatography-ultrviolet; PDA: photodiode array; NMR: nuclear magnetic resonance; FID: free induction decay; LC-MS: liquid chromatography-mass spectrometry; GC-MS: gas chromatography-mass spectrometry; HSQC: heteronuclear single quantum coherence; CPMG: Carr-Purcell-Meibum-Gill; VLDL: very low density lipoprotein; HDL: high density lipoprotein; EDTA: ethylenediaminetetraacetic acid; ANOVA: analysis of variance; AMIX: analysis of mixtures; SIMCA: soft independent modeling of class analogy; PCA: principal components analysis; OPLS-DA: orthogonal partial least-squares discriminant analysis; VIP: variable importance plot; AUROC: area under the receiver operating characteristic; TSP: sodium 3-(trimethylsilyl) propionate- 2,2,3,3- d4; DSA: deuterated 4-dimethyl-4-silapentane-1-ammonium trifluoroacetate; ESI: electrospray ionization; TCA: trichloroacetic acid; ACN: acetonitrile; dd H 2 O: distilled deionized water; FSH: follicle-stimulating hormone; LH: luteinizing hormone; OECD: Organisation for Economic Co-operation and Development.

Metabolomic analysis of avocado fruits by GC-APCI-TOF MS: effects of ripening degrees and fruit varieties.

PubMed

Hurtado-Fernández, E; Pacchiarotta, T; Mayboroda, O A; Fernández-Gutiérrez, A; Carrasco-Pancorbo, A

2015-01-01

In order to investigate avocado fruit ripening, nontargeted GC-APCI-TOF MS metabolic profiling analyses were carried out. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to explore the metabolic profiles from fruit samples of 13 varieties at two different ripening degrees. Mannoheptulose; pentadecylfuran; aspartic, malic, stearic, citric and pantothenic acids; mannitol; and β-sitosterol were some of the metabolites found as more influential for the PLS-DA model. The similarities among genetically related samples (putative mutants of "Hass") and their metabolic differences from the rest of the varieties under study have also been evaluated. The achieved results reveal new insights into avocado fruit composition and metabolite changes, demonstrating therefore the value of metabolomics as a functional genomics tool in characterizing the mechanism of fruit ripening development, a key developmental stage in most economically important fruit crops.

Effects of Stigmasterol and β-Sitosterol on Nonalcoholic Fatty Liver Disease in a Mouse Model: A Lipidomic Analysis.

PubMed

Feng, Simin; Gan, Ling; Yang, Chung S; Liu, Anna B; Lu, Wenyun; Shao, Ping; Dai, Zhuqing; Sun, Peilong; Luo, Zisheng

2018-04-04

To study the effects of stigmasterol and β-sitosterol on high-fat Western diet (HFWD)-induced nonalcoholic fatty liver disease (NAFLD), lipidomic analyses were conducted in liver samples collected after 33 weeks of the treatment. Principal component analysis showed these phytosterols were effective in protecting against HFWD-induced NAFLD. Orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and S-plots showed that triacylglycerols (TGs), phosphatidylcholines, cholesteryl esters, diacylglycerols, and free fatty acids (FFAs) were the major lipid species contributing to these discriminations. The alleviation of NAFLD is mainly associated with decreases in hepatic cholesterol, TGs with polyunsaturated fatty acids, and alterations of free hepatic FFA. In conclusion, phytosterols, at a dose comparable to that suggested for humans by the FDA for the reduction of plasma cholesterol levels, are shown to protect against NAFLD in this long-term (33-week) study.

Identification of anisodamine tablets by Raman and near-infrared spectroscopy with chemometrics.

PubMed

Li, Lian; Zang, Hengchang; Li, Jun; Chen, Dejun; Li, Tao; Wang, Fengshan

2014-06-05

Vibrational spectroscopy including Raman and near-infrared (NIR) spectroscopy has become an attractive tool for pharmaceutical analysis. In this study, effective calibration models for the identification of anisodamine tablet and its counterfeit and the distinguishment of manufacturing plants, based on Raman and NIR spectroscopy, were built, respectively. Anisodamine counterfeit tablets were identified by Raman spectroscopy with correlation coefficient method, and the results showed that the predictive accuracy was 100%. The genuine anisodamine tablets from 5 different manufacturing plants were distinguished by NIR spectroscopy using partial least squares discriminant analysis (PLS-DA) models based on interval principal component analysis (iPCA) method. And the results showed the recognition rate and rejection rate were 100% respectively. In conclusion, Raman spectroscopy and NIR spectroscopy combined with chemometrics are feasible and potential tools for rapid pharmaceutical tablet discrimination. Copyright © 2014 Elsevier B.V. All rights reserved.

High-resolution time-of-flight mass spectrometry fingerprinting of metabolites from cecum and distal colon contents of rats fed resistant starch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Timothy J.; Jones, Roger W.; Ai, Yongfeng

Time-of-flight mass spectrometry along with statistical analysis was utilized to study metabolic profiles among rats fed resistant starch (RS) diets. Fischer 344 rats were fed four starch diets consisting of 55 % (w/w, dbs) starch. A control starch diet consisting of corn starch was compared against three RS diets. The RS diets were high-amylose corn starch (HA7), HA7 chemically modified with octenyl succinic anhydride, and stearic-acid-complexed HA7 starch. A subgroup received antibiotic treatment to determine if perturbations in the gut microbiome were long lasting. A second subgroup was treated with azoxymethane (AOM), a carcinogen. At the end of the 8-weekmore » study, cecal and distal colon content samples were collected from the sacrificed rats. Metabolites were extracted from cecal and distal colon samples into acetonitrile. The extracts were then analyzed on an accurate-mass time-of-flight mass spectrometer to obtain their metabolic profile. The data were analyzed using partial least-squares discriminant analysis (PLS-DA). The PLS-DA analysis utilized a training set and verification set to classify samples within diet and treatment groups. PLS-DA could reliably differentiate the diet treatments for both cecal and distal colon samples. The PLS-DA analyses of the antibiotic and no antibiotic-treated subgroups were well classified for cecal samples and modestly separated for distal colon samples. PLS-DA analysis had limited success separating distal colon samples for rats given AOM from those not treated; the cecal samples from AOM had very poor classification. Mass spectrometry profiling coupled with PLS-DA can readily classify metabolite differences among rats given RS diets.« less

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

PubMed

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-01-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

NASA Astrophysics Data System (ADS)

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-10-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

PubMed

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of the Trace Elements and Active Components of Lonicera japonica flos and Lonicera flos Using ICP-MS and HPLC-PDA.

PubMed

Zhao, Yueran; Dou, Deqiang; Guo, Yueqiu; Qi, Yue; Li, Jun; Jia, Dong

2018-06-01

Thirteen trace elements and active constituents of 40 batches of Lonicera japonica flos and Lonicera flos were comparatively studied using inductively coupled plasma mass-spectrometry (ICP-MS) and high-performance liquid chromatography-photodiode array (HPLC-PDA). The trace elements were 24 Mg, 52 Cr, 55 Mn, 57 Fe, 60 Ni, 63 Cu, 66 Zn, 75 As, 82 Se, 98 Mo, 114 Cd, 202 Hg, and 208 Pb, and the active compounds were chlorogenic acid, 3,5-O-dicaffeoylquinc acid, 4,5-O-dicaffeoylquinc acid, luteolin-7-O-glucoside, and 4-O-caffeoylquinic acid. The data of 18 variables were statistically processed using principal component analysis (PCA) and discriminate analysis (DA) to classify L. japonica flos and L. flos. The validated method was developed to divide the 40 samples into two groups based on the PCA in terms of 18 variables. Furthermore, the species of Lonicera was better discriminated by using DA with 12 variables. These results suggest that the method and statistical analysis of the contents of trace elements and chemical components can classify the L. japonica flos and L. flos using 12 variables, such as 3,5-O-dicaffeoylquincacid, luteolin-7-O-glucoside, Cd, Mn, Hg, Pb, Ni, 4-O-caffeoyl-quinic acid, 4,5-O-dicaffeoylquinc acid, Fe, Mg, and Cr.

Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice

PubMed Central

2013-01-01

Background Epidermolysis bullosa acquisita (EBA) is a rare skin blistering disease with a prevalence of 0.2/ million people. EBA is characterized by autoantibodies against type VII collagen. Type VII collagen builds anchoring fibrils that are essential for the dermal-epidermal junction. The pathogenic relevance of antibodies against type VII collagen subdomains has been demonstrated both in vitro and in vivo. Despite the multitude of clinical and immunological data, no information on metabolic changes exists. Methods We used an animal model of EBA to obtain insights into metabolomic changes during EBA. Sera from mice with immunization-induced EBA and control mice were obtained and metabolites were isolated by filtration. Proton nuclear magnetic resonance (NMR) spectra were recorded and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) and random forest. Results The metabolic pattern of immunized mice and control mice could be clearly distinguished with PCA and PLS-DA. Metabolites that contribute to the discrimination could be identified via random forest. The observed changes in the metabolic pattern of EBA sera, i.e. increased levels of amino acid, point toward an increased energy demand in EBA. Conclusions Knowledge about metabolic changes due to EBA could help in future to assess the disease status during treatment. Confirming the metabolic changes in patients needs probably large cohorts. PMID:23800341

Rapid detection of Pseudomonas aeruginosa biomarkers in biological fluids using surface-enhanced Raman scattering

NASA Astrophysics Data System (ADS)

Wu, Xiaomeng; Chen, Jing; Zhao, Yiping; Zughaier, Susu M.

2014-05-01

Pseudomonas aeruginosa (PA) is an opportunistic pathogen that causes major infection not only in Cystic Fibrosis patients but also in chronic obstructive pulmonary disease and in critically ill patients in intensive care units. Successful antibiotic treatment of the infection relies on accurate and rapid identification of the infectious agents. Conventional microbiological detection methods usually take more than 3 days to obtain accurate results. We have developed a rapid diagnostic technique based on surface-enhanced Raman scattering to directly identify PA from biological fluids. P. aeruginosa strains, PAO1 and PA14, are cultured in lysogeny broth, and the SERS spectra of the broth show the signature Raman peaks from pyocyanin and pyoverdine, two major biomarkers that P. aeruginosa secretes during its growth, as well as lipopolysaccharides. This provides the evidence that the presence of these biomarkers can be used to indicate P. aeruginosa infection. A total of 22 clinical exhaled breath condensates (EBC) samples were obtained from subjects with CF disease and from non-CF healthy donors. SERS spectra of these EBC samples were obtained and further analyzed by both principle component analysis and partial least square-discriminant analysis (PLS-DA). PLS-DA can discriminate the samples with P. aeruginosa infection and the ones without P. aeruginosa infection at 99.3% sensitivity and 99.6% specificity. In addition, this technique can also discriminate samples from subject with CF disease and healthy donor with 97.5% sensitivity and 100% specificity. These results demonstrate the potential of using SERS of EBC samples as a rapid diagnostic tool to detect PA infection.

Discrimination of speech and non-speech sounds following theta-burst stimulation of the motor cortex.

PubMed

Rogers, Jack C; Möttönen, Riikka; Boyles, Rowan; Watkins, Kate E

2014-01-01

Perceiving speech engages parts of the motor system involved in speech production. The role of the motor cortex in speech perception has been demonstrated using low-frequency repetitive transcranial magnetic stimulation (rTMS) to suppress motor excitability in the lip representation and disrupt discrimination of lip-articulated speech sounds (Möttönen and Watkins, 2009). Another form of rTMS, continuous theta-burst stimulation (cTBS), can produce longer-lasting disruptive effects following a brief train of stimulation. We investigated the effects of cTBS on motor excitability and discrimination of speech and non-speech sounds. cTBS was applied for 40 s over either the hand or the lip representation of motor cortex. Motor-evoked potentials recorded from the lip and hand muscles in response to single pulses of TMS revealed no measurable change in motor excitability due to cTBS. This failure to replicate previous findings may reflect the unreliability of measurements of motor excitability related to inter-individual variability. We also measured the effects of cTBS on a listener's ability to discriminate: (1) lip-articulated speech sounds from sounds not articulated by the lips ("ba" vs. "da"); (2) two speech sounds not articulated by the lips ("ga" vs. "da"); and (3) non-speech sounds produced by the hands ("claps" vs. "clicks"). Discrimination of lip-articulated speech sounds was impaired between 20 and 35 min after cTBS over the lip motor representation. Specifically, discrimination of across-category ba-da sounds presented with an 800-ms inter-stimulus interval was reduced to chance level performance. This effect was absent for speech sounds that do not require the lips for articulation and non-speech sounds. Stimulation over the hand motor representation did not affect discrimination of speech or non-speech sounds. These findings show that stimulation of the lip motor representation disrupts discrimination of speech sounds in an articulatory feature-specific way.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rapid characterization of chemical markers for discrimination of Moutan Cortex and its processed products by direct injection-based mass spectrometry profiling and metabolomic method.

PubMed

Li, Chao-Ran; Li, Meng-Ning; Yang, Hua; Li, Ping; Gao, Wen

2018-06-01

Processing of herbal medicines is a characteristic pharmaceutical technique in Traditional Chinese Medicine, which can reduce toxicity and side effect, improve the flavor and efficacy, and even change the pharmacological action entirely. It is significant and crucial to perform a method to find chemical markers for differentiating herbal medicines in different processed degrees. The aim of this study was to perform a rapid and reasonable method to discriminate Moutan Cortex and its processed products, and to reveal the characteristics of chemical components depend on chemical markers. Thirty batches of Moutan Cortex and its processed products, including 11 batches of Raw Moutan Cortex (RMC), 9 batches of Moutan Cortex Tostus (MCT) and 10 batches of Moutan Cortex Carbonisatus (MCC), were directly injected in electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF MS) for rapid analysis in positive and negative mode. Without chromatographic separation, each run was completed within 3 min. The raw MS data were automatically extracted by background deduction and molecular feature (MF) extraction algorithm. In negative mode, a total of 452 MFs were obtained and then pretreated by data filtration and differential analysis. After that, the filtered 85 MFs were treated by principal component analysis (PCA) to reduce the dimensions. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was constructed for differentiation and chemical markers detection of Moutan Cortex in different processed degrees. The positive mode data were treated as same as those in negative mode. RMC, MCT and MCC were successfully classified. Moreover, 14 and 3 chemical markers from negative and positive mode respectively, were screened by the combination of their relative peak areas and the parameter variable importance in the projection (VIP) values in PLS-DA model. The content changes of these chemical markers were employed in order to illustrate chemical changes of Moutan Cortex after processed. These results showed that the proposed method which combined non-targeted metabolomics analysis with multivariate statistics analysis is reasonable and effective. It could not only be applied to discriminate herbal medicines and their processing products, but also to reveal the characteristics of chemical components during processing. Copyright © 2018. Published by Elsevier GmbH.

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

NASA Astrophysics Data System (ADS)

Cao, Yingjie; Tang, Changyuan; Song, Xianfang; Liu, Changming; Zhang, Yinghua

2016-06-01

Two multivariate statistical technologies, factor analysis (FA) and discriminant analysis (DA), are applied to study the river and groundwater hydrochemistry and its controlling processes in the Sanjiang Plain of the northeast China. Factor analysis identifies five factors which account for 79.65 % of the total variance in the dataset. Four factors bearing specific meanings as the river and groundwater hydrochemistry controlling processes are divided into two groups, the "natural hydrochemistry evolution" group and the "pollution" group. The "natural hydrochemistry evolution" group includes the salinity factor (factor 1) caused by rock weathering and the residence time factor (factor 2) reflecting the groundwater traveling time. The "pollution" group represents the groundwater quality deterioration due to geogenic pollution caused by elevated Fe and Mn (factor 3) and elevated nitrate (NO3 -) introduced by human activities such as agriculture exploitations (factor 5). The hydrochemical difference and hydraulic connection among rivers (surface water, SW), shallow groundwater (SG) and deep groundwater (DG) group are evaluated by the factor scores obtained from FA and DA (Fisher's method). It is showed that the river water is characterized as low salinity and slight pollution, and the shallow groundwater has the highest salinity and severe pollution. The SW is well separated from SG and DG by Fisher's discriminant function, but the SG and DG can not be well separated showing their hydrochemical similarities, and emphasize hydraulic connections between SG and DG.

Rational Design of QCM-D Virtual Sensor Arrays Based on Film Thickness, Viscoelasticity, and Harmonics for Vapor Discrimination.

PubMed

Speller, Nicholas C; Siraj, Noureen; Regmi, Bishnu P; Marzoughi, Hassan; Neal, Courtney; Warner, Isiah M

2015-01-01

Herein, we demonstrate an alternative strategy for creating QCM-based sensor arrays by use of a single sensor to provide multiple responses per analyte. The sensor, which simulates a virtual sensor array (VSA), was developed by depositing a thin film of ionic liquid, either 1-octyl-3-methylimidazolium bromide ([OMIm][Br]) or 1-octyl-3-methylimidazolium thiocyanate ([OMIm][SCN]), onto the surface of a QCM-D transducer. The sensor was exposed to 18 different organic vapors (alcohols, hydrocarbons, chlorohydrocarbons, nitriles) belonging to the same or different homologous series. The resulting frequency shifts (Δf) were measured at multiple harmonics and evaluated using principal component analysis (PCA) and discriminant analysis (DA) which revealed that analytes can be classified with extremely high accuracy. In almost all cases, the accuracy for identification of a member of the same class, that is, intraclass discrimination, was 100% as determined by use of quadratic discriminant analysis (QDA). Impressively, some VSAs allowed classification of all 18 analytes tested with nearly 100% accuracy. Such results underscore the importance of utilizing lesser exploited properties that influence signal transduction. Overall, these results demonstrate excellent potential of the virtual sensor array strategy for detection and discrimination of vapor phase analytes utilizing the QCM. To the best of our knowledge, this is the first report on QCM VSAs, as well as an experimental sensor array, that is based primarily on viscoelasticity, film thickness, and harmonics.

The Discriminant Value of Phase-Dependent Local Dynamic Stability of Daily Life Walking in Older Adult Community-Dwelling Fallers and Nonfallers

PubMed Central

Ihlen, Espen A. F.; Weiss, Aner; Helbostad, Jorunn L.; Hausdorff, Jeffrey M.

2015-01-01

The present study compares phase-dependent measures of local dynamic stability of daily life walking with 35 conventional gait features in their ability to discriminate between community-dwelling older fallers and nonfallers. The study reanalyzes 3D-acceleration data of 3-day daily life activity from 39 older people who reported less than 2 falls during one year and 31 who reported two or more falls. Phase-dependent local dynamic stability was defined for initial perturbation at 0%, 20%, 40%, 60%, and 80% of the step cycle. A partial least square discriminant analysis (PLS-DA) was used to compare the discriminant abilities of phase-dependent local dynamic stability with the discriminant abilities of 35 conventional gait features. The phase-dependent local dynamic stability λ at 0% and 60% of the step cycle discriminated well between fallers and nonfallers (AUC = 0.83) and was significantly larger (p < 0.01) for the nonfallers. Furthermore, phase-dependent λ discriminated as well between fallers and nonfallers as all other gait features combined. The present result suggests that phase-dependent measures of local dynamic stability of daily life walking might be of importance for further development in early fall risk screening tools. PMID:26491669

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods.

PubMed

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-15

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation. Copyright © 2016 Elsevier B.V. All rights reserved.

Indonesian palm civet coffee discrimination using UV-visible spectroscopy and several chemometrics methods

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2017-05-01

Indonesian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a simple and inexpensive method to discriminate between civet and non-civet coffee. The discrimination between civet and non-civet coffee in ground roasted (powder) samples is very challenging since it is very difficult to distinguish between the two by using conventional method. In this research, the use of UV-Visible spectra combined with two chemometric methods, SIMCA and PLS-DA, was evaluated to discriminate civet and non-civet ground coffee samples. The spectral data of civet and non-civet coffee were acquired using UV-Vis spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, USA). The result shows that using both supervised discrimination methods: SIMCA and PLS-DA, all samples were correctly classified into their corresponding classes with 100% rate for accuracy, sensitivity and specificity, respectively.

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.

Screening method for rapid classification of psychoactive substances in illicit tablets using mid infrared spectroscopy and PLS-DA.

PubMed

Pereira, Leandro S A; Lisboa, Fernanda L C; Coelho Neto, José; Valladão, Frederico N; Sena, Marcelo M

2018-05-09

Several new psychoactive substances (NPS) have reached the illegal drug market in recent years, and ecstasy-like tablets are one of the forms affected by this change. Cathinones and tryptamines have increasingly been found in ecstasy-like seized samples as well as other amphetamine type stimulants. A presumptive method for identifying different drugs in seized ecstasy tablets (n=92) using ATR-FTIR (attenuated total reflectance - Fourier transform infrared spectroscopy) and PLS-DA (partial least squares discriminant analysis) was developed. A hierarchical strategy of sequential modeling was performed with PLS-DA. The main model discriminated four classes: 5-MeO-MIPT, methylenedioxyamphetamines (MDMA and MDA), methamphetamine, and cathinones. Two submodels were built to identify drugs present in MDs and cathinones classes. Models were validated through the estimate of figures of merit. The average reliability rate (RLR) of the main model was 96.8% and accordance (ACC) was 100%. For the submodels, RLR and ACC were 100%. The reliability of the models was corroborated through their spectral interpretation. Thus, spectral assignments were performed by associating informative vectors of each specific modeled class to the respective drugs. The developed method is simple, fast, and can be applied to the forensic laboratory routine, leading to objective results reports useful for forensic scientists and law enforcement. Copyright © 2018 Elsevier B.V. All rights reserved.

In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques.

PubMed

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J H; Ilancheran, A; Huang, Zhiwei

2011-10-21

This study aimed to evaluate the clinical utility of applying near-infrared (NIR) Raman spectroscopy and genetic algorithm-partial least squares-discriminant analysis (GA-PLS-DA) to identify biomolecular changes of cervical tissues associated with dysplastic transformation during colposcopic examination. A total of 105 in vivo Raman spectra were measured from 57 cervical sites (35 normal and 22 precancer sites) of 29 patients recruited, in which 65 spectra were from normal sites, while 40 spectra were from cervical precancerous lesions (i.e., 7 low-grade CIN and 33 high-grade CIN). The GA feature selection technique incorporated with PLS was utilized to study the significant biochemical Raman bands for differentiation between normal and precancer cervical tissues. The GA-PLS-DA algorithm with double cross-validation (dCV) identified seven diagnostically significant Raman bands in the ranges of 925-935, 979-999, 1080-1090, 1240-1260, 1320-1340, 1400-1420, and 1625-1645 cm(-1) related to proteins, nucleic acids and lipids in tissue, and yielded a diagnostic accuracy of 82.9% (sensitivity of 72.5% (29/40) and specificity of 89.2% (58/65)) for precancer detection. The results of this exploratory study suggest that Raman spectroscopy in conjunction with GA-PLS-DA and dCV methods has the potential to provide clinically significant discrimination between normal and precancer cervical tissues at the molecular level.

Geographical provenance of palm oil by fatty acid and volatile compound fingerprinting techniques.

PubMed

Tres, A; Ruiz-Samblas, C; van der Veer, G; van Ruth, S M

2013-04-15

Analytical methods are required in addition to administrative controls to verify the geographical origin of vegetable oils such as palm oil in an objective manner. In this study the application of fatty acid and volatile organic compound fingerprinting in combination with chemometrics have been applied to verify the geographical origin of crude palm oil (continental scale). For this purpose 94 crude palm oil samples were collected from South East Asia (55), South America (11) and Africa (28). Partial least squares discriminant analysis (PLS-DA) was used to develop a hierarchical classification model by combining two consecutive binary PLS-DA models. First, a PLS-DA model was built to distinguish South East Asian from non-South East Asian palm oil samples. Then a second model was developed, only for the non-Asian samples, to discriminate African from South American crude palm oil. Models were externally validated by using them to predict the identity of new authentic samples. The fatty acid fingerprinting model revealed three misclassified samples. The volatile compound fingerprinting models showed an 88%, 100% and 100% accuracy for the South East Asian, African and American class, respectively. The verification of the geographical origin of crude palm oil is feasible by fatty acid and volatile compound fingerprinting. Further research is required to further validate the approach and to increase its spatial specificity to country/province scale. Copyright © 2012 Elsevier Ltd. All rights reserved.

Descriptive analysis of bacon smoked with Brazilian woods from reforestation: methodological aspects, statistical analysis, and study of sensory characteristics.

PubMed

Saldaña, Erick; Castillo, Luiz Saldarriaga; Sánchez, Jorge Cabrera; Siche, Raúl; de Almeida, Marcio Aurélio; Behrens, Jorge H; Selani, Miriam Mabel; Contreras-Castillo, Carmen J

2018-06-01

The aim of this study was to perform a descriptive analysis (DA) of bacons smoked with woods from reforestation and liquid smokes in order to investigate their sensory profile. Six samples of bacon were selected: three smoked bacons with different wood species (Eucalyptus citriodora, Acacia mearnsii, and Bambusa vulgaris), two artificially smoked bacon samples (liquid smoke) and one negative control (unsmoked bacon). Additionally, a commercial bacon sample was also evaluated. DA was developed successfully, presenting a good performance in terms of discrimination, consensus and repeatability. The study revealed that the smoking process modified the sensory profile by intensifying the "saltiness" and differentiating the unsmoked from the smoked samples. The results from the current research represent the first methodological development of descriptive analysis of bacon and may be used by food companies and other stakeholders to understand the changes in sensory characteristics of bacon due to traditional smoking process. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of Fourier transform infrared spectroscopy and chemometrics for differentiation of Salmonella enterica serovar Enteritidis phage types.

PubMed

Preisner, Ornella; Guiomar, Raquel; Machado, Jorge; Menezes, José Cardoso; Lopes, João Almeida

2010-06-01

Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques were used to discriminate five closely related Salmonella enterica serotype Enteritidis phage types, phage type 1 (PT1), PT1b, PT4b, PT6, and PT6a. Intact cells and outer membrane protein (OMP) extracts from bacterial cell membranes were subjected to FT-IR analysis in transmittance mode. Spectra were collected over a wavenumber range from 4,000 to 600 cm(-1). Partial least-squares discriminant analysis (PLS-DA) was used to develop calibration models based on preprocessed FT-IR spectra. The analysis based on OMP extracts provided greater separation between the Salmonella Enteritidis PT1-PT1b, PT4b, and PT6-PT6a groups than the intact cell analysis. When these three phage type groups were considered, the method based on OMP extract FT-IR spectra was 100% accurate. Moreover, complementary local models that considered only the PT1-PT1b and PT6-PT6a groups were developed, and the level of discrimination increased. PT1 and PT1b isolates were differentiated successfully with the local model using the entire OMP extract spectrum (98.3% correct predictions), whereas the accuracy of discrimination between PT6 and PT6a isolates was 86.0%. Isolates belonging to different phage types (PT19, PT20, and PT21) were used with the model to test its robustness. For the first time it was demonstrated that FT-IR analysis of OMP extracts can be used for construction of robust models that allow fast and accurate discrimination of different Salmonella Enteritidis phage types.

Application and Comparison of Laboratory Parameters for Forecasting Severe Hand-Foot-Mouth Disease Using Logistic Regression, Discriminant Analysis and Decision Tree.

PubMed

Sui, Meili; Huang, Xueyong; Li, Yi; Ma, Xiaomei; Zhang, Chao; Li, Xingle; Chen, Zhijuan; Feng, Huifen; Ren, Jingchao; Wang, Fang; Xu, Bianli; Duan, Guangcai

2016-01-01

In recent years, the prevalence of hand-foot-mouth disease (HFMD) in China and some other countries has caused worldwide concern. Mild cases tend to recover within a week, while severe cases may progress rapidly and tend to have bad outcome. Since there is no vaccine for HFMD and anti-inflammatory treatment is not ideal. In this study, we aimed to establish a valid forecasting model for severe HFMD using common laboratory parameters. Retrospectively, 77 severe HFMD cases from Zhengzhou Children's hospital in the peaking period between years 2013 to 2015 were collected, with 77 mild HFMD cases in the same area. The study recorded common laboratory parameters to assist in establishment of the severe HFMD model. After screening the important variables using Mann-Whitney U test, the study also matched the logistic regression (LR), discriminant analysis (DA), and decision tree (DT) to make a comparison. Compared with that of the mild group, serum levels of WBC, PLT, PCT, MCV, MCH, LCR, SCR, LCC, GLO, CK-MB, K, S100, and B in the severe group were higher (p < 0.05), while MCR, EOR, BASOR, SCC, MCC, EO, BASO, NA, CL, T, Th, and Th/Ts were lower (p < 0.05). Five indicators including MCR, LCC, Th, CK-MB, and CL were screened out by LR and the same for DA, and five variables including EO, LCC, CL, GLO, and MCC screened out by DT. The area under the curve (AUC) of LR, DA, and DT was 0.805, 0.779 and 0.864, respectively. The findings were that common laboratory indexes were effectively used to distinguish the mild HFMD cases and severe HFMD cases by LR, DA, and DT, and DT had the best classification effect with an AUC of 0.864.

Non-targeted analyses of animal plasma: betaine and choline represent the nutritional and metabolic status.

PubMed

Katayama, K; Sato, T; Arai, T; Amao, H; Ohta, Y; Ozawa, T; Kenyon, P R; Hickson, R E; Tazaki, H

2013-02-01

Simple liquid chromatography-mass spectrometry (LC-MS) was applied to non-targeted metabolic analyses to discover new metabolic markers in animal plasma. Principle component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA) were used to analyse LC-MS multivariate data. PCA clearly generated two separate clusters for artificially induced diabetic mice and healthy control mice. PLS-DA of time-course changes in plasma metabolites of chicks after feeding generated three clusters (pre- and immediately after feeding, 0.5-3 h after feeding and 4 h after feeding). Two separate clusters were also generated for plasma metabolites of pregnant Angus heifers with differing live-weight change profiles (gaining or losing). The accompanying PLS-DA loading plot detailed the metabolites that contribute the most to the cluster separation. In each case, the same highly hydrophilic metabolite was strongly correlated to the group separation. The metabolite was identified as betaine by LC-MS/MS. This result indicates that betaine and its metabolic precursor, choline, may be useful biomarkers to evaluate the nutritional and metabolic status of animals. © 2011 Blackwell Verlag GmbH.

1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas.

PubMed

Son, Hong-Seok; Kim, Ki Myong; van den Berg, Frans; Hwang, Geum-Sook; Park, Won-Mok; Lee, Cherl-Ho; Hong, Young-Shick

2008-09-10

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

Atmospheric pressure chemical ionisation mass spectrometry analysis linked with chemometrics for food classification - a case study: geographical provenance and cultivar classification of monovarietal clarified apple juices.

PubMed

Gan, Heng-Hui; Soukoulis, Christos; Fisk, Ian

2014-03-01

In the present work, we have evaluated for first time the feasibility of APCI-MS volatile compound fingerprinting in conjunction with chemometrics (PLS-DA) as a new strategy for rapid and non-destructive food classification. For this purpose 202 clarified monovarietal juices extracted from apples differing in their botanical and geographical origin were used for evaluation of the performance of APCI-MS as a classification tool. For an independent test set PLS-DA analyses of pre-treated spectral data gave 100% and 94.2% correct classification rate for the classification by cultivar and geographical origin, respectively. Moreover, PLS-DA analysis of APCI-MS in conjunction with GC-MS data revealed that masses within the spectral ACPI-MS data set were related with parent ions or fragments of alkyesters, carbonyl compounds (hexanal, trans-2-hexenal) and alcohols (1-hexanol, 1-butanol, cis-3-hexenol) and had significant discriminating power both in terms of cultivar and geographical origin. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Plasma metabonomics study on toxicity biomarker in rats treated with Euphorbia fischeriana based on LC-MS.

PubMed

Wang, Yingfeng; Man, Hongxue; Gao, Jian; Liu, Xinfeng; Ren, Xiaolei; Chen, Jianxin; Zhang, Jiayu; Gao, Kuo; Li, Zhongfeng; Zhao, Baosheng

2016-09-01

Lang-du (LD) has been traditionally used to treat human diseases in China. Plasma metabolic profiling was applied in this study based on LC-MS to elucidate the toxicity in rats induced by injected ethanol extract of LD. LD injection was given by intraperitoneal injection at doses of 0.1, 0.05, 0.025 and 0 g kg(-1) body weight per day to rats. The blood biochemical levels of alanine aminotransferase, direct bilirubin, creatinine, serum β2-microglobulin and low-density lipoprotein increased in LD-injected rats, and the levels of total protein and albumin decreased in these groups. The metabolic profiles of the samples were analyzed by multivariate statistics analysis, including principal component analysis, partial least squares discriminant analysis and orthogonal projection to latent structures discriminate analysis (OPLS-DA). The metabolic characters in rats injected with LD were perturbed in a dose-dependent manner. By OPLS-DA, 18 metabolites were served as the potential toxicity biomarkers. Moreover, LD treatment resulted in an increase in the p-cresol, p-cresol sulfate, lysophosphatidylethanolamine (LPE) (18:0), LPE (16:0), lysophosphatidylcholine (16:0) and 12-HETE concentrations, and a decrease in hippuric acid, cholic acid and N-acetyl-l-phenylalanine. These results suggested that chronic exposure to LD could cause a disturbance in lipids metabolism and amino acids metabolism, etc. Therefore, an analysis of the metabolic profiles can contribute to a better understanding of the adverse effects of LD. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Assessment of SWE data assimilation for ensemble streamflow predictions

NASA Astrophysics Data System (ADS)

Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

2014-11-01

An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa

PubMed Central

Ji, Guoli; Ye, Pengchao; Shi, Yijian; Yuan, Leiming; Chen, Xiaojing; Yuan, Mingshun; Zhu, Dehua; Chen, Xi; Hu, Xinyu; Jiang, Jing

2017-01-01

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments. PMID:29149053

The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics.

PubMed

Gil Solsona, R; Boix, C; Ibáñez, M; Sancho, J V

2018-03-01

The aim of this study was to use an untargeted UHPLC-HRMS-based metabolomics approach allowing discrimination between almonds based on their origin and variety. Samples were homogenised, extracted with ACN:H 2 O (80:20) containing 0.1% HCOOH and injected in a UHPLC-QTOF instrument in both positive and negative ionisation modes. Principal component analysis (PCA) was performed to ensure the absence of outliers. Partial least squares - discriminant analysis (PLS-DA) was employed to create and validate the models for country (with five different compounds) and variety (with 20 features), showing more than 95% accuracy. Additional samples were injected and the model was evaluated with blind samples, with more than 95% of samples being correctly classified using both models. MS/MS experiments were carried out to tentatively elucidate the highlighted marker compounds (pyranosides, peptides or amino acids, among others). This study has shown the potential of high-resolution mass spectrometry to perform and validate classification models, also providing information concerning the identification of the unexpected biomarkers which showed the highest discriminant power.

Accumulation of Carotenoids and Metabolic Profiling in Different Cultivars of Tagetes Flowers.

PubMed

Park, Yun Ji; Park, Soo-Yun; Valan Arasu, Mariadhas; Al-Dhabi, Naif Abdullah; Ahn, Hyung-Geun; Kim, Jae Kwang; Park, Sang Un

2017-02-18

Species of Tagetes , which belong to the family Asteraceae show different characteristics including, bloom size, shape, and color; plant size; and leaf shape. In this study, we determined the differences in primary metabolites and carotenoid yields among six cultivars from two Tagetes species, T. erecta and T. patula . In total, we detected seven carotenoids in the examined cultivars: violaxanthin, lutein, zeaxanthin, α-carotene, β-carotene, 9- cis -β-carotene, and 13- cis -β-carotene. In all the cultivars, lutein was the most abundant carotenoid. Furthermore, the contents of each carotenoid in flowers varied depending on the cultivar. Principal component analysis (PCA) facilitated metabolic discrimination between Tagetes cultivars, with the exception of Inca Yellow and Discovery Orange. Moreover, PCA and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) results provided a clear discrimination between T. erecta and T. patula . Primary metabolites, including xylose, citric acid, valine, glycine, and galactose were the main components facilitating separation of the species. Positive relationships were apparent between carbon-rich metabolites, including those of the TCA cycle and sugar metabolism, and carotenoids.

Aroma volatiles obtained at harvest by HS-SPME/GC-MS and INDEX/MS-E-nose fingerprint discriminate climacteric behaviour in melon fruit.

PubMed

Chaparro-Torres, Libia A; Bueso, María C; Fernández-Trujillo, Juan P

2016-05-01

Melon aroma volatiles were extracted at harvest from juice of a climacteric near-isogenic line (NIL) SC3-5-1 with two quantitative trait loci (QTLs) introgressed which produced climacteric behaviour and its non-climacteric parental (PS) using two methodologies of analysis: static headspace solid phase micro-extraction (HS-SPME) by gas chromatography-mass spectrometry (GC-MS) and inside needle dynamic extraction (INDEX) by MS-based electronic nose (MS-E-nose). Of the 137 volatiles compounds identified, most were found at significantly higher concentrations in SC3-5-1 than in PS in both seasons. These volatiles were mostly esters, alcohols, sulfur-derived esters and even some aldehydes and others. The number of variables with high correlation values was reduced by using correlation network analysis. Partial least squares-discriminant analysis (PLS-DA) achieved the correct classification of PS and SC3-5-1. The ions m/z 74, 91, 104, 105, 106 and 108, mainly volatile derivatives precursor phenylalanine, were the most discriminant in SC3-5-1 and PS. As many as 104 QTLs were mapped in season 1 and at least 78 QTLs in each season with an effect above the PS mean. GC-MS gave better discrimination than E-nose. Most of the QTLs that mapped in both seasons enhanced aroma volatiles associated with climacteric behaviour. © 2015 Society of Chemical Industry.

6C.04: INTEGRATED SNP ANALYSIS AND METABOLOMIC PROFILES OF METABOLIC SYNDROME.

PubMed

Marrachelli, V; Monleon, D; Morales, J M; Rentero, P; Martínez, F; Chaves, F J; Martin-Escudero, J C; Redon, J

2015-06-01

Metabolic syndrome (MS) has become a health and financial burden worldwide. Susceptibility of genetically determined metabotype of MS has not yet been investigated. We aimed to identify a distinctive metabolic profile of blood serum which might correlates to the early detection of the development of MS associated to genetic polymorphism. We applied high resolution NMR spectroscopy to profile blood serum from patients without MS (n = 945) or with (n = 291). Principal component analysis (PCA) and projection to latent structures for discriminant analysis (PLS-DA) were applied to NMR spectral datasets. Results were cross-validated using the Venetian Blinds approach. Additionally, five SNPs previously associated with MS were genotyped with SNPlex and tested for associations between the metabolic profiles and the genetic variants. Statistical analysis was performed using in-house MATLAB scripts and the PLS Toolbox statistical multivariate analysis library. Our analysis provided a PLS-DA Metabolic Syndrome discrimination model based on NMR metabolic profile (AUC = 0.86) with 84% of sensitivity and 72% specificity. The model identified 11 metabolites differentially regulated in patients with MS. Among others, fatty acids, glucose, alanine, hydroxyisovalerate, acetone, trimethylamine, 2-phenylpropionate, isobutyrate and valine, significantly contributed to the model. The combined analysis of metabolomics and SNP data revealed an association between the metabolic profile of MS and genes polymorphism involved in the adiposity regulation and fatty acids metabolism: rs2272903_TT (TFAP2B), rs3803_TT (GATA2), rs174589_CC (FADS2) and rs174577_AA (FADS2). In addition, individuals with the rs2272903-TT genotype seem to develop MS earlier than general population. Our study provides new insights on the metabolic alterations associated with a MS high-risk genotype. These results could help in future development of risk assessment and predictive models for subclinical cardiovascular disease.

Statistical analysis of fragmentation patterns of electron ionization mass spectra of enolized-trimethylsilylated anabolic androgenic steroids

NASA Astrophysics Data System (ADS)

Fragkaki, A. G.; Angelis, Y. S.; Tsantili-Kakoulidou, A.; Koupparis, M.; Georgakopoulos, C.

2009-08-01

Anabolic androgenic steroids (AAS) are included in the List of prohibited substances of the World Anti-Doping Agency (WADA) as substances abused to enhance athletic performance. Gas chromatography coupled to mass spectrometry (GC-MS) plays an important role in doping control analyses identifying AAS as their enolized-trimethylsilyl (TMS)-derivatives using the electron ionization (EI) mode. This paper explores the suitability of complementary GC-MS mass spectra and statistical analysis (principal component analysis, PCA and partial least squares-discriminant analysis, PLS-DA) to differentiate AAS as a function of their structural and conformational features expressed by their fragment ions. The results obtained showed that the application of PCA yielded a classification among the AAS molecules which became more apparent after applying PLS-DA to the dataset. The application of PLS-DA yielded a clear separation among the AAS molecules which were, thus, classified as: 1-ene-3-keto, 3-hydroxyl with saturated A-ring, 1-ene-3-hydroxyl, 4-ene-3-keto, 1,4-diene-3-keto and 3-keto with saturated A-ring anabolic steroids. The study of this paper also presents structurally diagnostic fragment ions and dissociation routes providing evidence for the presence of unknown AAS or chemically modified molecules known as designer steroids.

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis.

PubMed

Hohmann, Monika; Monakhova, Yulia; Erich, Sarah; Christoph, Norbert; Wachter, Helmut; Holzgrabe, Ulrike

2015-11-04

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Quantitative Determination of Cannabinoids in Cannabis and Cannabis Products Using Ultra-High-Performance Supercritical Fluid Chromatography and Diode Array/Mass Spectrometric Detection.

PubMed

Wang, Mei; Wang, Yan-Hong; Avula, Bharathi; Radwan, Mohamed M; Wanas, Amira S; Mehmedic, Zlatko; van Antwerp, John; ElSohly, Mahmoud A; Khan, Ikhlas A

2017-05-01

Ultra-high-performance supercritical fluid chromatography (UHPSFC) is an efficient analytical technique and has not been fully employed for the analysis of cannabis. Here, a novel method was developed for the analysis of 30 cannabis plant extracts and preparations using UHPSFC/PDA-MS. Nine of the most abundant cannabinoids, viz. CBD, ∆ 8 -THC, THCV, ∆ 9 -THC, CBN, CBG, THCA-A, CBDA, and CBGA, were quantitatively determined (RSDs < 6.9%). Unlike GC methods, no derivatization or decarboxylation was required prior to UHPSFC analysis. The UHPSFC chromatographic separation of cannabinoids displayed an inverse elution order compared to UHPLC. Combining with PDA-MS, this orthogonality is valuable for discrimination of cannabinoids in complex matrices. The developed method was validated, and the quantification results were compared with a standard UHPLC method. The RSDs of these two methods were within ±13.0%. Finally, chemometric analysis including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to differentiate between cannabis samples. © 2016 American Academy of Forensic Sciences.

HPLC-based metabolic profiling and quality control of leaves of different Panax species

PubMed Central

Yang, Seung-Ok; Lee, Sang Won; Kim, Young Ock; Sohn, Sang-Hyun; Kim, Young Chang; Hyun, Dong Yoon; Hong, Yoon Pyo; Shin, Yu Su

2013-01-01

Leaves from Panax ginseng Meyer (Korean origin and Chinese origin of Korean ginseng) and P. quinquefolius (American ginseng) were harvested in Haenam province, Korea, and were analyzed to investigate patterns in major metabolites using HPLC-based metabolic profiling. Partial least squares discriminant analysis (PLS-DA) was used to analyze the HPLC chromatogram data. There was a clear separation between Panax species and/or origins from different countries in the PLS-DA score plots. The ginsenoside compounds of Rg1, Re, Rg2, Rb2, Rb3, and Rd in Korean leaves were higher than in Chinese and American ginseng leaves, and the Rb1 level in P. quinquefolius leaves was higher than in P. ginseng (Korean origin or Chinese origin). HPLC chromatogram data coupled with multivariate statistical analysis can be used to profile the metabolite content and undertake quality control of Panax products. PMID:23717177

Statistical mapping of zones of focused groundwater/surface-water exchange using fiber-optic distributed temperature sensing

USGS Publications Warehouse

Mwakanyamale, Kisa; Day-Lewis, Frederick D.; Slater, Lee D.

2013-01-01

Fiber-optic distributed temperature sensing (FO-DTS) increasingly is used to map zones of focused groundwater/surface-water exchange (GWSWE). Previous studies of GWSWE using FO-DTS involved identification of zones of focused GWSWE based on arbitrary cutoffs of FO-DTS time-series statistics (e.g., variance, cross-correlation between temperature and stage, or spectral power). New approaches are needed to extract more quantitative information from large, complex FO-DTS data sets while concurrently providing an assessment of uncertainty associated with mapping zones of focused GSWSE. Toward this end, we present a strategy combining discriminant analysis (DA) and spectral analysis (SA). We demonstrate the approach using field experimental data from a reach of the Columbia River adjacent to the Hanford 300 Area site. Results of the combined SA/DA approach are shown to be superior to previous results from qualitative interpretation of FO-DTS spectra alone.

Discrimination of magnoliae officinalis cortex based on the quantitative profiles of magnolosides by two-channel liquid chromatography with electrochemical detection.

PubMed

Xue, Zhenzhen; Kotani, Akira; Yang, Bin; Hakamata, Hideki

2018-05-31

A two-channel liquid chromatography with electrochemical detection system (2LC-ECD) was newly designed for the simultaneous determination of magnolosides A, B, F, H, and L in the first channel and other magnolosides D and M in the second channel, respectively. Peak heights had linear relationships to the magnoloside concentrations in a range of 0.02-16 μmol/L for H, 0.01-12 μmol/L for A, 0.02-12 μmol/L for F and L, 0.01-8 μmol/L for B, 0.002-6 μmol/L for D, and 0.002-4 μmol/L for M, respectively. Seven magnolosides in magnoliae officinalis cortex (MOC) were determined by the 2LC-ECD, and the obtained quantitative profiles of magnolosides were applied to the discrimination between the MOC samples harvested from Hubei and Sichuan (called Chuan po) and from Zhejiang and Fujian (called Wen po). By principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) based on the quantitative profiles of the magnolosides, Chuan po were clearly discriminated from Wen po on the plots obtained from our multivariable analyses. Copyright © 2018 Elsevier B.V. All rights reserved.

Metabolomics combined with chemometric tools (PCA, HCA, PLS-DA and SVM) for screening cassava (Manihot esculenta Crantz) roots during postharvest physiological deterioration.

PubMed

Uarrota, Virgílio Gavicho; Moresco, Rodolfo; Coelho, Bianca; Nunes, Eduardo da Costa; Peruch, Luiz Augusto Martins; Neubert, Enilto de Oliveira; Rocha, Miguel; Maraschin, Marcelo

2014-10-15

Cassava roots are an important source of dietary and industrial carbohydrates and suffer markedly from postharvest physiological deterioration (PPD). This paper deals with metabolomics combined with chemometric tools for screening the chemical and enzymatic composition in several genotypes of cassava roots during PPD. Metabolome analyses showed increases in carotenoids, flavonoids, anthocyanins, phenolics, reactive scavenging species, and enzymes (superoxide dismutase family, hydrogen peroxide, and catalase) until 3-5days postharvest. PPD correlated negatively with phenolics and carotenoids and positively with anthocyanins and flavonoids. Chemometric tools such as principal component analysis, partial least squares discriminant analysis, and support vector machines discriminated well cassava samples and enabled a good prediction of samples. Hierarchical clustering analyses grouped samples according to their levels of PPD and chemical compositions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Detection and identification of multiple adulterants in plant food supplements using attenuated total reflectance-Infrared spectroscopy.

PubMed

Deconinck, E; Aouadi, C; Bothy, J L; Courselle, P

2018-04-15

Due to the rising popularity of dietary supplements, especially plant food supplements, and alternative herbal medicines, a whole market developed and these products became freely available through internet. Though several searches revealed that at least a part of these products, especially the ones obtained from websites disclosing their physical identity, are aldulterated with pharmaceutical compounds. This causes a threat for public health, since these compounds are not declared and therefore adverse effects will not immediately be related to the product. The more the adulterants can interfere with other medicinal treatments. Since the present active pharmaceutical ingredients are not declared on the package and the products are sold as 100% natural or herbal in nature, it is very difficult for custom personnel to discriminate between products to be confiscated or not. Therefore easy to apply analytical approaches to discriminate between adulterated and non-adulterated products are necessary. This paper presents an approach based on infrared spectroscopy combined with attenuated total reflectance (ATR) and partial least squares- discriminant analysis (PLS-DA) to easily differentiate between adulterated and non- adulterated plant food supplements and to get a first idea of the nature of the adulterant present. The performance of PLS-DA models based on Mid-IR and NIR data were compared as well as models based on the combined data. Further three preprocessing strategies were compared. The best performance was obtained for a PLS-DA model using Mid-IR data with the second derivative as preprocessing method. This model showed a correct classification rate of 98.3% for an external test set. Also eight real samples were screened using the model and for seven of these samples a correct classification was obtained. Generally it could be concluded that the obtained model and the presented approach could be used at customs to discriminate between adulterated and non-adulterated herbal food supplements and even get a first idea of the nature of the adulterant present. The more the presented approach hardly needs sample preparation. Copyright © 2018 Elsevier B.V. All rights reserved.

HR-MAS MR Spectroscopy of Breast Cancer Tissue Obtained with Core Needle Biopsy: Correlation with Prognostic Factors

PubMed Central

Choi, Ji Soo; Baek, Hyeon-Man; Kim, Suhkmann; Kim, Min Jung; Youk, Ji Hyun; Moon, Hee Jung; Kim, Eun-Kyung; Han, Kyung Hwa; Kim, Dong-hyun; Kim, Seung Il; Koo, Ja Seung

2012-01-01

The purpose of this study was to examine the correlation between high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopy using core needle biopsy (CNB) specimens and histologic prognostic factors currently used in breast cancer patients. After institutional review board approval and informed consent were obtained for this study, CNB specimens were collected from 36 malignant lesions in 34 patients. Concentrations and metabolic ratios of various choline metabolites were estimated by HR-MAS MR spectroscopy using CNB specimens. HR-MAS spectroscopic values were compared according to histopathologic variables [tumor size, lymph node metastasis, histologic grade, status of estrogens receptor (ER), progesterone receptor (PR), HER2 (a receptor for human epidermal growth factor), and Ki-67, and triple negativity]. Multivariate analysis was performed with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA). HR-MAS MR spectroscopy quantified and discriminated choline metabolites in all CNB specimens of the 36 breast cancers. Several metabolite markers [free choline (Cho), phosphocholine (PC), creatine (Cr), taurine, myo-inositol, scyllo-inositol, total choline (tCho), glycine, Cho/Cr, tCho/Cr, PC/Cr] on HR-MAS MR spectroscopy were found to correlate with histologic prognostic factors [ER, PR, HER2, histologic grade, triple negativity, Ki-67, poor prognosis]. OPLS-DA multivariate models were generally able to discriminate the status of histologic prognostic factors (ER, PR, HER2, Ki-67) and prognosis groups. Our study suggests that HR-MAS MR spectroscopy using CNB specimens can predict tumor aggressiveness prior to surgery in breast cancer patients. In addition, it may be helpful in the detection of reliable markers for breast cancer characterization. PMID:23272149

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Urinary Metabolomic Profiling to Identify Potential Biomarkers for the Diagnosis of Behcet's Disease by Gas Chromatography/Time-of-Flight-Mass Spectrometry.

PubMed

Ahn, Joong Kyong; Kim, Jungyeon; Hwang, Jiwon; Song, Juhwan; Kim, Kyoung Heon; Cha, Hoon-Suk

2017-11-02

Diagnosing Behcet's disease (BD) is challenging because of the lack of a diagnostic biomarker. The purposes of this study were to investigate distinctive metabolic changes in urine samples of BD patients and to identify urinary metabolic biomarkers for diagnosis of BD using gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS). Metabolomic profiling of urine samples from 44 BD patients and 41 healthy controls (HC) were assessed using GC/TOF-MS, in conjunction with multivariate statistical analysis. A total of 110 urinary metabolites were identified. The urine metabolite profiles obtained from GC/TOF-MS analysis could distinguish BD patients from the HC group in the discovery set. The parameter values of the orthogonal partial least squared-discrimination analysis (OPLS-DA) model were R ² X of 0.231, R ² Y of 0.804, and Q ² of 0.598. A biomarker panel composed of guanine, pyrrole-2-carboxylate, 3-hydroxypyridine, mannose, l-citrulline, galactonate, isothreonate, sedoheptuloses, hypoxanthine, and gluconic acid lactone were selected and adequately validated as putative biomarkers of BD (sensitivity 96.7%, specificity 93.3%, area under the curve 0.974). OPLS-DA showed clear discrimination of BD and HC groups by a biomarker panel of ten metabolites in the independent set (accuracy 88%). We demonstrated characteristic urinary metabolic profiles and potential urinary metabolite biomarkers that have clinical value in the diagnosis of BD using GC/TOF-MS.

Chemometric classification of morphologically similar Umbelliferae medicinal herbs by DART-TOF-MS fingerprint.

PubMed

Lee, Sang Min; Kim, Hye-Jin; Jang, Young Pyo

2012-01-01

It needs many years of special training to gain expertise on the organoleptic classification of botanical raw materials and, even for those experts, discrimination among Umbelliferae medicinal herbs remains an intricate challenge due to their morphological similarity. To develop a new chemometric classification method using a direct analysis in real time-time of flight-mass spectrometry (DART-TOF-MS) fingerprinting for Umbelliferae medicinal herbs and to provide a platform for its application to the discrimination of other herbal medicines. Angelica tenuissima, Angelica gigas, Angelica dahurica and Cnidium officinale were chosen for this study and ten samples of each species were purchased from various Korean markets. DART-TOF-MS was employed on powdered raw materials to obtain a chemical fingerprint of each sample and the orthogonal partial-least squares method in discriminant analysis (OPLS-DA) was used for multivariate analysis. All samples of collected species were successfully discriminated from each other according to their characteristic DART-TOF-MS fingerprint. Decursin (or decursinol angelate) and byakangelicol were identified as marker molecules for Angelica gigas and A. dahurica, respectively. Using the OPLS method for discriminant analysis, Angelica tenuissima and Cnidium officinale were clearly separated into two groups. Angelica tenuissima was characterised by the presence of ligustilide and unidentified molecular ions of m/z 239 and 283, while senkyunolide A together with signals with m/z 387 and 389 were the marker compounds for Cnidium officinale. Elaborating with chemoinformatics, DART-TOF-MS fingerprinting with chemoinformatic tools results in a powerful method for the classification of morphologically similar Umbelliferae medicinal herbs and quality control of medicinal herbal products, including the extracts of these crude drugs. Copyright © 2012 John Wiley & Sons, Ltd.

Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shao, Yongni; He, Yong; Hu, Xingyue

2007-12-01

An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.

MALDI-TOF Mass Spectrometry for the Detection and Differentiation of Entamoeba histolytica and Entamoeba dispar

PubMed Central

Calderaro, Adriana; Piergianni, Maddalena; Buttrini, Mirko; Montecchini, Sara; Piccolo, Giovanna; Gorrini, Chiara; Rossi, Sabina; Chezzi, Carlo; Arcangeletti, Maria Cristina; Medici, Maria Cristina; De Conto, Flora

2015-01-01

Detection of Entamoeba histolytica and its differentiation from Entamoeba dispar is an important goal of the clinical parasitology laboratory. The aim of this study was the identification and differentiation of E. histolytica and E. dispar by MALDI-TOF MS, in order to evaluate the application of this technique in routine diagnostic practice. MALDI-TOF MS was applied to 3 amebic reference strains and to 14 strains isolated from feces that had been differentiated by molecular methods in our laboratory. Protein extracts from cultures of these strains (axenic cultures for the 3 reference strains and monoxenic cultures for the 14 field isolates) were analyzed by MALDI-TOF MS and the spectra obtained were analyzed by statistical software. Five peaks discriminating between E. histolytica and E. dispar reference strains were found by protein profile analysis: 2 peaks (8,246 and 8,303 Da) specific for E. histolytica and 3 (4,714; 5,541; 8,207 Da) for E. dispar. All clinical isolates except one showed the discriminating peaks expected for the appropriate species. For 2 fecal samples from which 2 strains (1 E. histolytica and 1 E. dispar) out of the 14 included in this study were isolated, the same discriminating peaks found in the corresponding isolated amebic strains were detected after only 12h (E. histolytica) and 24h (E. dispar) of incubation of the fecal samples in Robinson’s medium without serum. Our study shows that MALDI-TOF MS can be used to discriminate between E. histolytica and E. dispar using in vitro xenic cultures and it also could have potential for the detection of these species in clinical samples. PMID:25874612

Selected Gray Matter Volumes and Gender but Not Basal Ganglia nor Cerebellum Gyri Discriminate Left Versus Right Cerebral Hemispheres: Multivariate Analyses in human Brains at 3T.

PubMed

Roldan-Valadez, Ernesto; Suarez-May, Marcela A; Favila, Rafael; Aguilar-Castañeda, Erika; Rios, Camilo

2015-07-01

Interest in the lateralization of the human brain is evident through a multidisciplinary number of scientific studies. Understanding volumetric brain asymmetries allows the distinction between normal development stages and behavior, as well as brain diseases. We aimed to evaluate volumetric asymmetries in order to select the best gyri able to classify right- versus left cerebral hemispheres. A cross-sectional study performed in 47 right-handed young-adults healthy volunteers. SPM-based software performed brain segmentation, automatic labeling and volumetric analyses for 54 regions involving the cerebral lobes, basal ganglia and cerebellum from each cerebral hemisphere. Multivariate discriminant analysis (DA) allowed the assembling of a predictive model. DA revealed one discriminant function that significantly differentiated left vs. right cerebral hemispheres: Wilks' λ = 0.008, χ(2) (9) = 238.837, P < 0.001. The model explained 99.20% of the variation in the grouping variable and depicted an overall predictive accuracy of 98.8%. With the influence of gender; the selected gyri able to discriminate between hemispheres were middle orbital frontal gyrus (g.), angular g., supramarginal g., middle cingulum g., inferior orbital frontal g., calcarine g., inferior parietal lobule and the pars triangularis inferior frontal g. Specific brain gyri are able to accurately classify left vs. right cerebral hemispheres by using a multivariate approach; the selected regions correspond to key brain areas involved in attention, internal thought, vision and language; our findings favored the concept that lateralization has been evolutionary favored by mental processes increasing cognitive efficiency and brain capacity. © 2015 Wiley Periodicals, Inc.

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-04-15

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

PubMed Central

Guo, Lei; Liu, Lei; Wen, Jingran; Xu, Lu; Yan, Min; Li, Zuofeng; Zhang, Xiaoyan; Nan, Peng; Jiang, Jinling; Ji, Jun; Zhang, Jianian; Cai, Wei; Zhuang, Huisheng; Wang, Yan; Zhu, Zhenggang; Yu, Yingyan

2016-01-01

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening. PMID:27589838

Exploring high-resolution magic angle spinning (HR-MAS) NMR spectroscopy for metabonomic analysis of apples.

PubMed

Vermathen, Martina; Marzorati, Mattia; Vermathen, Peter

2012-01-01

Classical liquid-state high-resolution (HR) NMR spectroscopy has proved a powerful tool in the metabonomic analysis of liquid food samples like fruit juices. In this paper the application of (1)H high-resolution magic angle spinning (HR-MAS) NMR spectroscopy to apple tissue is presented probing its potential for metabonomic studies. The (1)H HR-MAS NMR spectra are discussed in terms of the chemical composition of apple tissue and compared to liquid-state NMR spectra of apple juice. Differences indicate that specific metabolic changes are induced by juice preparation. The feasibility of HR-MAS NMR-based multivariate analysis is demonstrated by a study distinguishing three different apple cultivars by principal component analysis (PCA). Preliminary results are shown from subsequent studies comparing three different cultivation methods by means of PCA and partial least squares discriminant analysis (PLS-DA) of the HR-MAS NMR data. The compounds responsible for discriminating organically grown apples are discussed. Finally, an outlook of our ongoing work is given including a longitudinal study on apples.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

Application of Neutral Networks to Seismic Signal Discrimination

DTIC Science & Technology

1993-05-15

AD-A276 626 PL-TR-93-2154 Application of Neural Networks to Seismic Signal Discrimination James A. Cercone V. Shane Foster W. Mike Clark Larry... Networks to Seismic Signal Discrimination PE 61101E PR 1DMO TA DA WU AA .AUTHOR(S) Stephen Goodman John Martin C James A. Cercone Don J. Smith G...of Technology Applications of Neural Networks to Seismic Classification project. The first year of research focused on identification and collection

A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: A case study of Panax ginseng: Metabolomics for the authenticity of herbal medicine.

PubMed

Nguyen, Huy Truong; Lee, Dong-Kyu; Choi, Young-Geun; Min, Jung-Eun; Yoon, Sang Jun; Yu, Yun-Hyun; Lim, Johan; Lee, Jeongmi; Kwon, Sung Won; Park, Jeong Hill

2016-05-30

Ginseng, the root of Panax ginseng has long been the subject of adulteration, especially regarding its origins. Here, 60 ginseng samples from Korea and China initially displayed similar genetic makeup when investigated by DNA-based technique with 23 chloroplast intergenic space regions. Hence, (1)H NMR-based metabolomics with orthogonal projections on the latent structure-discrimination analysis (OPLS-DA) were applied and successfully distinguished between samples from two countries using seven primary metabolites as discrimination markers. Furthermore, to recreate adulteration in reality, 21 mixed samples of numerous Korea/China ratios were tested with the newly built OPLS-DA model. The results showed satisfactory separation according to the proportion of mixing. Finally, a procedure for assessing mixing proportion of intentionally blended samples that achieved good predictability (adjusted R(2)=0.8343) was constructed, thus verifying its promising application to quality control of herbal foods by pointing out the possible mixing ratio of falsified samples. Copyright © 2016 Elsevier B.V. All rights reserved.

In vivo study for the discrimination of cancerous and normal skin using fibre probe-based Raman spectroscopy.

PubMed

Schleusener, Johannes; Gluszczynska, Patrycja; Reble, Carina; Gersonde, Ingo; Helfmann, Jürgen; Fluhr, Joachim W; Lademann, Jürgen; Röwert-Huber, Joachim; Patzelt, Alexa; Meinke, Martina C

2015-10-01

Raman spectroscopy has proved its capability as an objective, non-invasive tool for the detection of various melanoma and non-melanoma skin cancers (NMSC) in a number of studies. Most publications are based on a Raman microspectroscopic ex vivo approach. In this in vivo clinical evaluation, we apply Raman spectroscopy using a fibre-coupled probe that allows access to a multitude of affected body sites. The probe design is optimized for epithelial sensitivity, whereby a large part of the detected signal originates from within the epidermal layer's depth down to the basal membrane where early stages of skin cancer develop. Data analysis was performed on measurements of 104 subjects scheduled for excision of lesions suspected of being malignant melanoma (MM) (n = 36), basal cell carcinoma (BCC) (n = 39) and squamous cell carcinoma (SCC) (n = 29). NMSC were discriminated from normal skin with a balanced accuracy of 73% (BCC) and 85% (SCC) using partial least squares discriminant analysis (PLS-DA). Discriminating MM and pigmented nevi (PN) resulted in a balanced accuracy of 91%. These results lie within the range of comparable in vivo studies and the accuracies achieved by trained dermatologists using dermoscopy. Discrimination proved to be unsuccessful between cancerous lesions and suspicious lesions that had been histopathologically verified as benign by dermoscopy. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Speech Perception Deficits in Poor Readers: A Reply to Denenberg's Critique.

ERIC Educational Resources Information Center

Studdert-Kennedy, Michael; Mody, Maria; Brady, Susan

2000-01-01

This rejoinder to a critique of the authors' research on speech perception deficits in poor readers answers the specific criticisms and reaffirms their conclusion that the difficulty some poor readers have with rapid /ba/-/da/ discrimination does not stem from difficulty in discriminating the rapid spectral transitions at stop-vowel syllable…

Direct infusion MS-based lipid profiling reveals the pharmacological effects of compound K-reinforced ginsenosides in high-fat diet induced obese mice.

PubMed

Shon, Jong Cheol; Shin, Hwa-Soo; Seo, Yong Ki; Yoon, Young-Ran; Shin, Heungsop; Liu, Kwang-Hyeon

2015-03-25

The serum lipid metabolites of lean and obese mice fed normal or high-fat diets were analyzed via direct infusion nanoelectrospray-ion trap mass spectrometry followed by multivariate analysis. In addition, lipidomic biomarkers responsible for the pharmacological effects of compound K-reinforced ginsenosides (CK), thus the CK fraction, were evaluated in mice fed high-fat diets. The obese and lean groups were clearly discriminated upon principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) score plot, and the major metabolites contributing to such discrimination were triglycerides (TGs), cholesteryl esters (CEs), phosphatidylcholines (PCs), and lysophosphatidylcholines (LPCs). TGs with high total carbon number (>50) and low total carbon number (<50) were negatively and positively associated with high-fat diet induced obesity in mice, respectively. When the CK fraction was fed to obese mice that consumed a high-fat diet, the levels of certain lipids including LPCs and CEs became similar to those of mice fed a normal diet. Such metabolic markers can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, changes in the levels of such metabolites can be employed to assess the risk of obesity and the therapeutic effects of obesity management.

Non-invasive characterization of colorants by portable diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Barberis, Elettra; Aceto, Maurizio; Marengo, Emilio

2017-06-01

During the last years the need for non-invasive and non-destructive analytical methods brought to the development and application of new instrumentation and analytical methods for the in-situ analysis of cultural heritage objects. In this work we present the application of a portable diffuse reflectance infrared Fourier transform (DRIFT) method for the non-invasive characterization of colorants prepared according to ancient recipes and using egg white and Gum Arabic as binders. Approximately 50 colorants were analyzed with the DRIFT spectroscopy: we were able to identify and discriminate the most used yellow (i.e. yellow ochres, Lead-tin Yellow, Orpiment, etc.), red (i.e. red ochres, Hematite) and blue (i.e. Lapis Lazuli, Azurite, indigo) colorants, creating a complete DRIFT spectral library. The Principal Component Analysis-Discriminant Analysis (PCA-DA) was then employed for the colorants classification according to the chemical/mineralogical composition. The DRIFT analysis was also performed on a gouache painting of the artist Sutherland; and the colorants used by the painter were identified directly in-situ and in a non-invasive manner.

Data fusion for food authentication. Combining rare earth elements and trace metals to discriminate "Fava Santorinis" from other yellow split peas using chemometric tools.

PubMed

Drivelos, Spiros A; Higgins, Kevin; Kalivas, John H; Haroutounian, Serkos A; Georgiou, Constantinos A

2014-12-15

"Fava Santorinis", is a protected designation of origin (PDO) yellow split pea species growing only in the island of Santorini in Greece. Due to its nutritional quality and taste, it has gained a high monetary value. Thus, it is prone to adulteration with other yellow split peas. In order to discriminate "Fava Santorinis" from other yellow split peas, four classification methods utilising rare earth elements (REEs) measured through inductively coupled plasma-mass spectrometry (ICP-MS) are studied. The four classification processes are orthogonal projection analysis (OPA), Mahalanobis distance (MD), partial least squares discriminant analysis (PLS-DA) and k nearest neighbours (KNN). Since it is known that trace elements are often useful to determine geographical origin of food products, we further quantitated for trace elements using ICP-MS. Presented in this paper are results using the four classification processes based on the fusion of the REEs data with the trace element data. Overall, the OPA method was found to perform best with up to 100% accuracy using the fused data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

PubMed Central

Afendi, Farit M.; Ono, Naoaki; Nakamura, Yukiko; Nakamura, Kensuke; Darusman, Latifah K.; Kibinge, Nelson; Morita, Aki Hirai; Tanaka, Ken; Horai, Hisayuki; Altaf-Ul-Amin, Md.; Kanaya, Shigehiko

2013-01-01

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology. PMID:24688691

A procedure for the sensory evaluation of Salama da sugo, a typical fermented sausage produced in the Emilia Romagna Region, Italy.

PubMed

Coloretti, Fabio; Grazia, Luigi; Gardini, Fausto; Lanciotti, Rosalba; Montanari, Chiara; Tabanelli, Giulia; Chiavari, Cristiana

2015-03-30

Salama da sugo is a fermented sausage from the Ferrara tradition (Italy, Emilia-Romagna region), subjected to a long ripening period (4-6 months) and characterised by a high content of wine and spices in the mixture. It can be consumed after cooking and it is served with its sugo, i.e. the liquid extracted by cooking process. The aim of this work was to set up a method for the sensory profile of Salama da sugo as a request for Protected Geographical Indication (PGI) has been made to the European Commission. A system of sample preparation that provides for the precooking in an autoclave and cooking in boiling water was set up. A specific sheet for sensory evaluation of Salama da sugo has been created and reports 23 descriptors identified during the lexicon development. The differences in sensory profile of four samples were evaluated and principal component analysis highlighted the more discriminant parameters, i.e. odour intensity, wine odour, spicy aroma, fat/lean connection, sweet, bitter, juiciness, chewiness and pricking. The proposed method allows the standardisation of sensory profiling of Salama da sugo, and is also to verify compliance with the specification PGI. © 2014 Society of Chemical Industry.

Mechanistic consideration of the photochemical transformation of domoic acid (algal toxin) in DOM-Rich brackish water.

PubMed

Jin, Hangxing; Lian, Lushi; Zhou, Huaxi; Yan, Shuwen; Song, Weihua

2018-06-14

Domoic acid (DA) is a neurotoxin generated by several diatom species in harmful algae blooms (HABs). We report the photo-induced transformation products (TPs) and degradation mechanisms of DA in dissolved organic matter (DOM)-rich freshwater and brackish water. High-resolution quadrupole time-of-flight mass spectrometry (QTOF-MS) and the multivariate statistical strategy orthogonal partial least-squares discriminant analysis (OPLS-DA) identified 36 and 23 potential TPs in DOM-rich freshwater and brackish water, respectively. The main reactive sites of DA are the conjugated double bond and proline ring. Isomerization is the predominant transformation pathway induced by excited-state triplet DOM ( 3 DOM ∗ ). The second-order rate constant of the isomerization reaction was measured as (3.8 ± 0.2) × 10 8  M -1  s -1 . The inverse correlation between the dissolved oxygen (DO) concentration and the rate of photo-induced DA isomerization was revealed. Furthermore, under halide-present conditions, halide radicals are mainly responsible for the differentiation of products by quenching hydroxyl radicals and generating unique organic peroxide products. Our results indicated that halide radicals could be important in the photochemical transformation of organic contaminants in high saline environments. Copyright © 2018 Elsevier Ltd. All rights reserved.

Gait characteristics and their discriminative power in geriatric patients with and without cognitive impairment.

PubMed

Kikkert, Lisette H J; Vuillerme, Nicolas; van Campen, Jos P; Appels, Bregje A; Hortobágyi, Tibor; Lamoth, Claudine J C

2017-08-15

A detailed gait analysis (e.g., measures related to speed, self-affinity, stability, and variability) can help to unravel the underlying causes of gait dysfunction, and identify cognitive impairment. However, because geriatric patients present with multiple conditions that also affect gait, results from healthy old adults cannot easily be extrapolated to geriatric patients. Hence, we (1) quantified gait outcomes based on dynamical systems theory, and (2) determined their discriminative power in three groups: healthy old adults, geriatric patients with- and geriatric patients without cognitive impairment. For the present cross-sectional study, 25 healthy old adults recruited from community (65 ± 5.5 years), and 70 geriatric patients with (n = 39) and without (n = 31) cognitive impairment from the geriatric dayclinic of the MC Slotervaart hospital in Amsterdam (80 ± 6.6 years) were included. Participants walked for 3 min during single- and dual-tasking at self-selected speed while 3D trunk accelerations were registered with an IPod touch G4. We quantified 23 gait outcomes that reflect multiple gait aspects. A multivariate model was built using Partial Least Square- Discriminant Analysis (PLS-DA) that best modelled participant group from gait outcomes. For single-task walking, the PLS-DA model consisted of 4 Latent Variables that explained 63 and 41% of the variance in gait outcomes and group, respectively. Outcomes related to speed, regularity, predictability, and stability of trunk accelerations revealed with the highest discriminative power (VIP > 1). A high proportion of healthy old adults (96 and 93% for single- and dual-task, respectively) was correctly classified based on the gait outcomes. The discrimination of geriatric patients with and without cognitive impairment was poor, with 57% (single-task) and 64% (dual-task) of the patients misclassified. While geriatric patients vs. healthy old adults walked slower, and less regular, predictable, and stable, we found no differences in gait between geriatric patients with and without cognitive impairment. The effects of multiple comorbidities on geriatric patients' gait possibly causes a 'floor-effect', with no room for further deterioration when patients develop cognitive impairment. An accurate identification of cognitive status thus necessitates a multifactorial approach.

Comparing two metabolic profiling approaches (liquid chromatography and gas chromatography coupled to mass spectrometry) for extra-virgin olive oil phenolic compounds analysis: A botanical classification perspective.

PubMed

Bajoub, Aadil; Pacchiarotta, Tiziana; Hurtado-Fernández, Elena; Olmo-García, Lucía; García-Villalba, Rocío; Fernández-Gutiérrez, Alberto; Mayboroda, Oleg A; Carrasco-Pancorbo, Alegría

2016-01-08

Over the last decades, the phenolic compounds from virgin olive oil (VOO) have become the subject of intensive research because of their biological activities and their influence on some of the most relevant attributes of this interesting matrix. Developing metabolic profiling approaches to determine them in monovarietal virgin olive oils could help to gain a deeper insight into olive oil phenolic compounds composition as well as to promote their use for botanical origin tracing purposes. To this end, two approaches were comparatively investigated (LC-ESI-TOF MS and GC-APCI-TOF MS) to evaluate their capacity to properly classify 25 olive oil samples belonging to five different varieties (Arbequina, Cornicabra, Hojiblanca, Frantoio and Picual), using the entire chromatographic phenolic profiles combined to chemometrics (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)). The application of PCA to LC-MS and GC-MS data showed the natural clustering of the samples, seeing that 2 varieties were dominating the models (Arbequina and Frantoio), suppressing any possible discrimination among the other cultivars. Afterwards, PLS-DA was used to build four different efficient predictive models for varietal classification of the samples under study. The varietal markers pointed out by each platform were compared. In general, with the exception of one GC-MS model, all exhibited proper quality parameters. The models constructed by using the LC-MS data demonstrated superior classification ability. Copyright © 2015 Elsevier B.V. All rights reserved.

Magnetic resonance metabolic profiling of estrogen receptor-positive breast cancer: correlation with currently used molecular markers

PubMed Central

Koo, Ja Seung; Kim, Siwon; Park, Vivian Youngjean; Kim, Eun-Kyung; Kim, Suhkmann; Kim, Min Jung

2017-01-01

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P<0.01, and P <0.01, respectively). The high Ki-67 group showed higher levels of glutamate than the low Ki-67 group without statistical significance. Luminal B cancers showed higher levels of glycine (P=0.01) than luminal A cancers. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the subgroups according to HER2 and Ki-67 status, and luminal type. This study showed that the metabolic profiles of CNB samples assessed by HR-MAS MRS can be used to detect potential prognostic biomarkers as well as to understand the difference in metabolic mechanism among subtypes of ER-positive breast cancer. PMID:28969000

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Dynamic assessment of narrative ability in English accurately identifies language impairment in English language learners.

PubMed

Peña, Elizabeth D; Gillam, Ronald B; Bedore, Lisa M

2014-12-01

To assess the identification accuracy of dynamic assessment (DA) of narrative ability in English for children learning English as a 2nd language. A DA task was administered to 54 children: 18 Spanish-English-speaking children with language impairment (LI); 18 age-, sex-, IQ- and language experience-matched typical control children; and an additional 18 age- and language experience-matched comparison children. A variety of quantitative and qualitative measures were collected in the pretest phase, the mediation phase, and the posttest phase of the study. Exploratory discriminant analysis was used to determine the set of measures that best differentiated among this group of children with and without LI. A combination of examiner ratings of modifiability (compliance, metacognition, and task orientation), DA story scores (setting, dialogue, and complexity of vocabulary), and ungrammaticality (derived from the posttest narrative sample) classified children with 80.6% to 97.2% accuracy. DA conducted in English provides a systematic means for measuring learning processes and learning outcomes, resulting in a clinically useful procedure for identifying LIs in bilingual children who are in the process of learning English as a second language.

Juniperus phoenicea var. turbinata (Guss.) Parl. Leaf Essential Oil Variability in the Balkans.

PubMed

Rajčević, Nemanja F; Labus, Marina G; Dodoš, Tanja Z; Novaković, Jelica J; Marin, Petar D

2018-06-15

In the present work leaf essential oil from 97 individuals of Juniperus phoenicea var. turbinata (GUSS.) PARL. from the Balkan Peninsula was analysed. The essential oil was dominated by monoterpene hydrocarbons (45.5 - 71.8%), of which α-pinene was the most abundant in almost all of the samples (38.2 - 55.8%). Several other monoterpenes and sesquiterpenes were also present in relatively high abundances in samples such as myrcene, δ-3-carene, ß-phellandrene, α-terpinyl acetate, (E)-caryophyllene and germacrene D. Multivariate statistical analysis suggested the existence of three possible chemotypes based on the abundance of the four components. Even though the intrapopulation variability was high, discriminant analysis (DA) was able to separate populations. DA showed high separation between western and eastern populations but also grouped geographically closer populations along the west Balkan shoreline. The potential influence of the climate on the composition of the essential oil was also studied. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Preliminary antifungal and cytotoxic evaluation of synthetic cycloalkyl[b]thiophene derivatives with PLS-DA analysis.

PubMed

Souza, Beatriz C C; De Oliveira, Tiago B; Aquino, Thiago M; de Lima, Maria C A; Pitta, Ivan R; Galdino, Suely L; Lima, Edeltrudes O; Gonçalves-Silva, Teresinha; Militão, Gardênia C G; Scotti, Luciana; Scotti, Marcus T; Mendonça, Francisco J B

2012-06-01

A series of 2-[(arylidene)amino]-cycloalkyl[b]thiophene-3-carbonitriles (2a-x) was synthesized by incorporation of substituted aromatic aldehydes in Gewald adducts (1a-c). The title compounds were screened for their antifungal activity against Candida krusei and Criptococcus neoformans and for their antiproliferative activity against a panel of 3 human cancer cell lines (HT29, NCI H-292 and HEP). For antiproliferative activity, the partial least squares (PLS) methodology was applied. Some of the prepared compounds exhibited promising antifungal and proliferative properties. The most active compounds for antifungal activity were cyclohexyl[b]thiophene derivatives, and for antiproliferative activity cycloheptyl[b]thiophene derivatives, especially 2-[(1H-indol-2-yl-methylidene)amino]- 5,6,7,8-tetrahydro-4H-cyclohepta[b]thiophene-3-carbonitrile (2r), which inhibited more than 97 % growth of the three cell lines. The PLS discriminant analysis (PLS-DA) applied generated good exploratory and predictive results and showed that the descriptors having shape characteristics were strongly correlated with the biological data.

Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques.

PubMed

Nosrati, Kazem

2013-04-01

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

PubMed

Ma, Xiao; Yuan, Hong-fu; Song, Chun-feng; Hu, Ai-qin; Li, Xiao-yu; Zhao, Zhong; Li, Xiu-qin; Guo Zhen; Zhu, Zhi-qiang

2015-07-01

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

[Analysis on component difference in Citrus reticulata before and after being processed with salt by UPLC-Q-TOF/MS].

PubMed

Zeng, Rui; Fu, Juan; Wu, La-Bin; Huang, Lin-Fang

2013-07-01

To analyze components of Citrus reticulata and salt-processed C. reticulata by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS), and compared the changes in components before and after being processed with salt. Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) were adopted to analyze the difference in fingerprint between crude and processed C. reticulata, showing increased content of eriocitrin, limonin, nomilin and obacunone increase in salt-processed C. reticulata. Potential chemical markers were identified as limonin, obacunone and nomilin, which could be used for distinguishing index components of crude and processed C. reticulata.

Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

PubMed

Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

2007-11-01

Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

Dynamic elementary mode modelling of non-steady state flux data.

PubMed

Folch-Fortuny, Abel; Teusink, Bas; Hoefsloot, Huub C J; Smilde, Age K; Ferrer, Alberto

2018-06-18

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Quantitative dermatoglyphic asymmetry: a comparative study between schizophrenic patients and control groups of West Bengal, India.

PubMed

Karmakar, B; Sengupta, M

2012-01-01

Quantitative Fluctuating (FA) and Directional asymmetry (DA) of dermatoglyphics on digito-palmar complex were analyzed in a group of 111 patients (males: 61, females: 50) with schizophrenia (SZ), and compared to an ethnically matched phenotypically healthy control (males: 60, females: 60) through MANOVA, ANOVA and canonical Discriminant analyses. With few exceptions, asymmetries are higher among patients, and this is more prominent in FA than DA. Statistically significant differences were observed between patient and control groups, especially in males. In both sexes, FA of combined dermatoglyphic traits (e.g. total finger ridge count, total palmar pattern ridge count) are found to be a strong discriminator between the two groups with a correct classification of over 83% probability.

Detecting bugweed (Solanum mauritianum) abundance in plantation forestry using multisource remote sensing

NASA Astrophysics Data System (ADS)

Peerbhay, Kabir; Mutanga, Onisimo; Lottering, Romano; Bangamwabo, Victor; Ismail, Riyad

2016-11-01

The invasive weed Solanum mauritianum (bugweed) has infested large areas of plantation forests in KwaZulu-Natal, South Africa. Bugweed often forms dense infestations and rapidly capitalises on available natural resources hindering the production of forest resources. Precise assessment of bugweed canopy cover, especially at low abundance cover, is essential to an effective weed management strategy. In this study, the utility of AISA Eagle airborne hyperspectral data (393-994 nm) with the new generation Worldview-2 multispectral sensor (427-908 nm) was compared to detect the abundance of bugweed cover within the Hodgsons Sappi forest plantation. Using sparse partial least squares discriminant analysis (SPLS-DA), the best detection results were obtained when performing discrimination using the remotely sensing images combined with LiDAR. Overall classification accuracies subsequently improved by 10% and 11.67% for AISA and Worldview-2 respectively, with improved detection accuracies for bugweed cover densities as low as 5%. The incorporation of LiDAR worked well within the SPLS-DA framework for detecting the abundance of bugweed cover using remotely sensed data. In addition, the algorithm performed simultaneous dimension reduction and variable selection successfully whereby wavelengths in the visible (393-670 nm) and red-edge regions (725-734 nm) of the spectrum were the most effective.

Quality Evaluation of Juniperus rigida Sieb. et Zucc. Based on Phenolic Profiles, Bioactivity, and HPLC Fingerprint Combined with Chemometrics

PubMed Central

Liu, Zehua; Wang, Dongmei; Li, Dengwu; Zhang, Shuai

2017-01-01

Juniperus rigida (J. rigida) which is endemic to East Asia, has traditionally been used as an ethnomedicinal plant in China. This study was undertaken to evaluate the quality of J. rigida samples derived from 11 primary regions in China. Ten phenolic compounds were simultaneously quantified using reversed-phase high-performance liquid chromatography (RP-HPLC), and chlorogenic acid, catechin, podophyllotoxin, and amentoflavone were found to be the main compounds in J. rigida needles, with the highest contents detected for catechin and podophyllotoxin. J. rigida from Jilin (S9, S10) and Liaoning (S11) exhibited the highest contents of phenolic profiles (total phenolics, total flavonoids and 10 phenolic compounds) and the strongest antioxidant and antibacterial activities, followed by Shaanxi (S2, S3). A similarity analysis (SA) demonstrated substantial similarities in fingerprint chromatograms, from which 14 common peaks were selected. The similarity values varied from 0.85 to 0.98. Chemometrics techniques, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and discriminant analysis (DA), were further applied to facilitate accurate classification and quantification of the J. rigida samples derived from the 11 regions. The results supported HPLC data showing that all J. rigida samples exhibit considerable variations in phenolic profiles, and the samples were further clustered into three major groups coincident with their geographical regions of origin. In addition, two discriminant functions with a 100% discrimination ratio were constructed to further distinguish and classify samples with unknown membership on the basis of eigenvalues to allow optimal discrimination among the groups. Our comprehensive findings on matching phenolic profiles and bioactivities along with data from fingerprint chromatograms with chemometrics provide an effective tool for screening and quality evaluation of J. rigida and related medicinal preparations. PMID:28469573

Rapid detection of milk adulteration using intact protein flow injection mass spectrometric fingerprints combined with chemometrics.

PubMed

Du, Lijuan; Lu, Weiying; Cai, Zhenzhen Julia; Bao, Lei; Hartmann, Christoph; Gao, Boyan; Yu, Liangli Lucy

2018-02-01

Flow injection mass spectrometry (FIMS) combined with chemometrics was evaluated for rapidly detecting economically motivated adulteration (EMA) of milk. Twenty-two pure milk and thirty-five counterparts adulterated with soybean, pea, and whey protein isolates at 0.5, 1, 3, 5, and 10% (w/w) levels were analyzed. The principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and support vector machine (SVM) classification models indicated that the adulterated milks could successfully be classified from the pure milks. FIMS combined with chemometrics might be an effective method to detect possible EMA in milk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolic analysis of elicited cell suspension cultures of Cannabis sativa L. by (1)H-NMR spectroscopy.

PubMed

Pec, Jaroslav; Flores-Sanchez, Isvett Josefina; Choi, Young Hae; Verpoorte, Robert

2010-07-01

Cannabis sativa L. plants produce a diverse array of secondary metabolites. Cannabis cell cultures were treated with jasmonic acid (JA) and pectin as elicitors to evaluate their effect on metabolism from two cell lines using NMR spectroscopy and multivariate data analysis. According to principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA), the chloroform extract of the pectin-treated cultures were more different than control and JA-treated cultures; but in the methanol/water extract the metabolome of the JA-treated cells showed clear differences with control and pectin-treated cultures. Tyrosol, an antioxidant metabolite, was detected in cannabis cell cultures. The tyrosol content increased after eliciting with JA.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

PubMed

Kusumaningrum, Dewi; Lee, Hoonsoo; Lohumi, Santosh; Mo, Changyeun; Kim, Moon S; Cho, Byoung-Kwan

2018-03-01

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Metabolic dependence of green tea on plucking positions revisited: a metabolomic study.

PubMed

Lee, Jang-Eun; Lee, Bum-Jin; Hwang, Jeong-Ah; Ko, Kwang-Sup; Chung, Jin-Oh; Kim, Eun-Hee; Lee, Sang-Jun; Hong, Young-Shick

2011-10-12

The dependence of global green tea metabolome on plucking positions was investigated through (1)H nuclear magnetic resonance (NMR) analysis coupled with multivariate statistical data set. Pattern recognition methods, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), were employed for a finding metabolic discrimination among fresh green tea leaves plucked at different positions from young to old leaves. In addition to clear metabolic discrimination among green tea leaves, elevations in theanine, caffeine, and gallic acid levels but reductions in catechins, such as epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epigallocatechin-3-gallate (EGCG), glucose, and sucrose levels were observed, as the green tea plant grows up. On the other hand, the younger the green tea leaf is, the more theanine, caffeine, and gallic acid but the lesser catechins accumlated in the green tea leaf, revealing a reverse assocation between theanine and catechins levels due to incorporaton of theanine into catechins with growing up green tea plant. Moreover, as compared to the tea leaf, the observation of marked high levels of theanine and low levels of catechins in green tea stems exhibited a distinct tea plant metabolism between the tea leaf and the stem. This metabolomic approach highlights taking insight to global metabolic dependence of green tea leaf on plucking position, thereby providing distinct information on green tea production with specific tea quality.

Gender differences in autism spectrum disorders: Divergence among specific core symptoms.

PubMed

Beggiato, Anita; Peyre, Hugo; Maruani, Anna; Scheid, Isabelle; Rastam, Maria; Amsellem, Frederique; Gillberg, Carina I; Leboyer, Marion; Bourgeron, Thomas; Gillberg, Christopher; Delorme, Richard

2017-04-01

Community-based studies have consistently shown a sex ratio heavily skewed towards males in autism spectrum disorders (ASD). The factors underlying this predominance of males are largely unknown, but the way girls score on standardized categorical diagnostic tools might account for the underrecognition of ASD in girls. Despite the existence of different norms for boys and girls with ASD on several major screening tests, the algorithm of the Autism Diagnosis Interview-Revised (ADI-R) has not been reformulated. The aim of our study was to investigate which ADI-R items discriminate between males and females, and to evaluate their weighting in the final diagnosis of autism. We then conducted discriminant analysis (DA) on a sample of 594 probands including 129 females with ASD, recruited by the Paris Autism Research International Sibpair (PARIS) Study. A replication analysis was run on an independent sample of 1716 probands including 338 females with ASD, recruited through the Autism Genetics Resource Exchange (AGRE) program. Entering the raw scores for all ADI-R items as independent variables, the DA correctly classified 78.9% of males and 72.9% of females (P < 0.001) in the PARIS cohort, and 72.2% of males and 68.3% of females (P < 0.0001) in the AGRE cohort. Among the items extracted by the stepwise DA, four belonged to the ADI-R algorithm used for the final diagnosis of ASD. In conclusion, several items of the ADI-R that are taken into account in the diagnosis of autism significantly differentiates between males and females. The potential gender bias thus induced may participate in the underestimation of the prevalence of ASD in females. Autism Res 2016,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 680-689. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Classification of tree species based on longwave hyperspectral data from leaves, a case study for a tropical dry forest

NASA Astrophysics Data System (ADS)

Harrison, D.; Rivard, B.; Sánchez-Azofeifa, A.

2018-04-01

Remote sensing of the environment has utilized the visible, near and short-wave infrared (IR) regions of the electromagnetic (EM) spectrum to characterize vegetation health, vigor and distribution. However, relatively little research has focused on the use of the longwave infrared (LWIR, 8.0-12.5 μm) region for studies of vegetation. In this study LWIR leaf reflectance spectra were collected in the wet seasons (May through December) of 2013 and 2014 from twenty-six tree species located in a high species diversity environment, a tropical dry forest in Costa Rica. A continuous wavelet transformation (CWT) was applied to all spectra to minimize noise and broad amplitude variations attributable to non-compositional effects. Species discrimination was then explored with Random Forest classification and accuracy improved was observed with preprocessing of reflectance spectra with continuous wavelet transformation. Species were found to share common spectral features that formed the basis for five spectral types that were corroborated with linear discriminate analysis. The source of most of the observed spectral features is attributed to cell wall or cuticle compounds (cellulose, cutin, matrix glycan, silica and oleanolic acid). Spectral types could be advantageous for the analysis of airborne hyperspectral data because cavity effects will lower the spectral contrast thus increasing the reliance of classification efforts on dominant spectral features. Spectral types specifically derived from leaf level data are expected to support the labeling of spectral classes derived from imagery. The results of this study and that of Ribeiro Da Luz (2006), Ribeiro Da Luz and Crowley (2007, 2010), Ullah et al. (2012) and Rock et al. (2016) have now illustrated success in tree species discrimination across a range of ecosystems using leaf-level spectral observations. With advances in LWIR sensors and concurrent improvements in their signal to noise, applications to large-scale species detection from airborne imagery appear feasible.

Relations between Cardiac and Visual Phenotypes in Diabetes: A Multivariate Approach.

PubMed

Oliveiros, Bárbara; Sanches, Mafalda; Quendera, Bruno; Graça, Bruno; Guelho, Daniela; Gomes, Leonor; Carrilho, Francisco; Caseiro-Alves, Filipe; Castelo-Branco, Miguel

2016-01-01

Cardiovascular disease and diabetes represent a major public health concern. The former is the most frequent cause of death and disability in patients with type 2 diabetes, where left ventricular dysfunction is highly prevalent. Moreover, diabetic retinopathy is becoming a dominant cause of visual impairment and blindness. The complex relation between cardiovascular disease and diabetic retinopathy as a function of ageing, obesity and hypertension remains to be clarified. Here, we investigated such relations in patients with diabetes type 2, in subjects with neither overt heart disease nor advanced proliferative diabetic retinopathy. We studied 47 patients and 50 controls, aged between 45 and 65 years, equally distributed according to gender. From the 36 measures regarding visual structure and function, and the 11 measures concerning left ventricle function, we performed data reduction to obtain eight new derived variables, seven of which related to the eye, adjusted for age, gender, body mass index and high blood pressure using both discriminant analysis (DA) and logistic regression (LR). We found moderate to strong correlation between left ventricle function and the eye constructs: minimum correlation was found for psychophysical motion thresholds (DA: 0.734; LR: 0.666), while the maximum correlation was achieved with structural volume density in the neural retina (DA: 0.786; LR: 0.788). Controlling the effect of pairwise correlated visual constructs, the parameters that were most correlated to left ventricle function were volume density in retina and thickness of the retinal nerve fiber layers (adjusted multiple R2 is 0.819 and 0.730 for DA and LR), with additional contribution of psychophysical loss in achromatic contrast discrimination. We conclude that visual structural and functional changes in type 2 diabetes are related to heart dysfunction, when the effects of clinical, demographic and associated risk factors are taken into account, revealing a genuine relation between cardiac and retinal diabetic phenotypes.

TOFSIMS-P: a web-based platform for analysis of large-scale TOF-SIMS data.

PubMed

Yun, So Jeong; Park, Ji-Won; Choi, Il Ju; Kang, Byeongsoo; Kim, Hark Kyun; Moon, Dae Won; Lee, Tae Geol; Hwang, Daehee

2011-12-15

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87 138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.

Characterizing hydrochemical properties of springs in Taiwan based on their geological origins.

PubMed

Jang, Cheng-Shin; Chen, Jui-Sheng; Lin, Yun-Bin; Liu, Chen-Wuing

2012-01-01

This study was performed to characterize hydrochemical properties of springs based on their geological origins in Taiwan. Stepwise discriminant analysis (DA) was used to establish a linear classification model of springs using hydrochemical parameters. Two hydrochemical datasets-ion concentrations and relative proportions of equivalents per liter of major ions-were included to perform prediction of the geological origins of springs. Analyzed results reveal that DA using relative proportions of equivalents per liter of major ions yields a 95.6% right assignation, which is superior to DA using ion concentrations. This result indicates that relative proportions of equivalents of major hydrochemical parameters in spring water are more highly associated with the geological origins than ion concentrations do. Low percentages of Na(+) equivalents are common properties of springs emerging from acid-sulfate and neutral-sulfate igneous rock. Springs emerging from metamorphic rock show low percentages of Cl( - ) equivalents and high percentages of HCO[Formula: see text] equivalents, and springs emerging from sedimentary rock exhibit high Cl( - )/SO(2-)(4) ratios.

Rapid classification of pharmaceutical ingredients with Raman spectroscopy using compressive detection strategy with PLS-DA multivariate filters.

PubMed

Cebeci Maltaş, Derya; Kwok, Kaho; Wang, Ping; Taylor, Lynne S; Ben-Amotz, Dor

2013-06-01

Identifying pharmaceutical ingredients is a routine procedure required during industrial manufacturing. Here we show that a recently developed Raman compressive detection strategy can be employed to classify various widely used pharmaceutical materials using a hybrid supervised/unsupervised strategy in which only two ingredients are used for training and yet six other ingredients can also be distinguished. More specifically, our liquid crystal spatial light modulator (LC-SLM) based compressive detection instrument is trained using only the active ingredient, tadalafil, and the excipient, lactose, but is tested using these and various other excipients; microcrystalline cellulose, magnesium stearate, titanium (IV) oxide, talc, sodium lauryl sulfate and hydroxypropyl cellulose. Partial least squares discriminant analysis (PLS-DA) is used to generate the compressive detection filters necessary for fast chemical classification. Although the filters used in this study are trained on only lactose and tadalafil, we show that all the pharmaceutical ingredients mentioned above can be differentiated and classified using PLS-DA compressive detection filters with an accumulation time of 10ms per filter. Copyright © 2013 Elsevier B.V. All rights reserved.

An integrated analysis for determining the geographical origin of medicinal herbs using ICP-AES/ICP-MS and (1)H NMR analysis.

PubMed

Kwon, Yong-Kook; Bong, Yeon-Sik; Lee, Kwang-Sik; Hwang, Geum-Sook

2014-10-15

ICP-MS and (1)H NMR are commonly used to determine the geographical origin of food and crops. In this study, data from multielemental analysis performed by ICP-AES/ICP-MS and metabolomic data obtained from (1)H NMR were integrated to improve the reliability of determining the geographical origin of medicinal herbs. Astragalus membranaceus and Paeonia albiflora with different origins in Korea and China were analysed by (1)H NMR and ICP-AES/ICP-MS, and an integrated multivariate analysis was performed to characterise the differences between their origins. Four classification methods were applied: linear discriminant analysis (LDA), k-nearest neighbour classification (KNN), support vector machines (SVM), and partial least squares-discriminant analysis (PLS-DA). Results were compared using leave-one-out cross-validation and external validation. The integration of multielemental and metabolomic data was more suitable for determining geographical origin than the use of each individual data set alone. The integration of the two analytical techniques allowed diverse environmental factors such as climate and geology, to be considered. Our study suggests that an appropriate integration of different types of analytical data is useful for determining the geographical origin of food and crops with a high degree of reliability. Copyright © 2014 Elsevier Ltd. All rights reserved.

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

Investigation of crimson-dyed fibres for a new approach on the characterization of cochineal and kermes dyes in historical textiles.

PubMed

Serrano, Ana; van den Doel, Andre; van Bommel, Maarten; Hallett, Jessica; Joosten, Ineke; van den Berg, Klaas J

2015-10-15

The colorant behaviour of cochineal and kermes insect dyes in 141 experimentally-dyed and 28 artificially-aged samples of silk and wool was investigated using ultra-high performance liquid chromatography coupled to photodiode array detector (UHPLC-PDA), liquid chromatography electrospray ionisation mass spectrometry (LC-ESI-MS) and image scanning electron microscopy - energy dispersive X-ray spectroscopy (SEM-EDX). Partial-least squares discriminant analysis (PLS-DA) was then used to model the acquired UHPLC-PDA data and assess the possibility of discriminating cochineal insect species, as well as their correspondent dyed and aged reference fibres. The resulting models helped to characterize a set of 117 red samples from 95 historical textiles, in which UHPLC-PDA analyses have reported the presence of cochineal and kermes insect dyes. Analytical investigation of the experimentally-dyed and artificially-aged fibres has demonstrated that the ratio of compounds in the insects dye composition can change, depending on the dyeing conditions applied and the type of fibres used. Similarities were observed when comparing the UHPLC-MS and SEM-EDX results from the dyed and aged references with the historical samples. This was verified with PLS-DA models of the chromatographic data, facilitating the classification of the cochineal species present in the historical samples. The majority of these samples were identified to contain American cochineal, which is in agreement with historical and dye identification literature that describe the impact of this dyestuff into European and Asian dyeing practices, after the Iberian Expansion in the 16th century. The analytical results emphasize the importance of using statistical data interpretation for the discrimination of cochineal dyes, besides qualitative and quantitative evaluation of chromatograms. Hence, the combination of UHPLC-PDA with a statistical classification method, such as PLS-DA, has been demonstrated to be an advisable approach in future investigations to assess closely related species of natural dyes in historical textile samples. This is particularly important when aiming to achieve more accurate interpretations about the history of works of art, or the application of natural dyes in old textile production. Copyright © 2015 Elsevier B.V. All rights reserved.

Infant speech-sound discrimination testing: effects of stimulus intensity and procedural model on measures of performance.

PubMed

Nozza, R J

1987-06-01

Performance of infants in a speech-sound discrimination task (/ba/ vs /da/) was measured at three stimulus intensity levels (50, 60, and 70 dB SPL) using the operant head-turn procedure. The procedure was modified so that data could be treated as though from a single-interval (yes-no) procedure, as is commonly done, as well as if from a sustained attention (vigilance) task. Discrimination performance changed significantly with increase in intensity, suggesting caution in the interpretation of results from infant discrimination studies in which only single stimulus intensity levels within this range are used. The assumptions made about the underlying methodological model did not change the performance-intensity relationships. However, infants demonstrated response decrement, typical of vigilance tasks, which supports the notion that the head-turn procedure is represented best by the vigilance model. Analysis then was done according to a method designed for tasks with undefined observation intervals [C. S. Watson and T. L. Nichols, J. Acoust. Soc. Am. 59, 655-668 (1976)]. Results reveal that, while group data are reasonably well represented across levels of difficulty by the fixed-interval model, there is a variation in performance as a function of time following trial onset that could lead to underestimation of performance in some cases.

A Novel Spectroscopically Determined Pharmacodynamic Biomarker for Skin Toxicity in Cancer Patients Treated with Targeted Agents.

PubMed

Azan, Antoine; Caspers, Peter J; Bakker Schut, Tom C; Roy, Séverine; Boutros, Céline; Mateus, Christine; Routier, Emilie; Besse, Benjamin; Planchard, David; Seck, Atmane; Kamsu Kom, Nyam; Tomasic, Gorana; Koljenović, Senada; Noordhoek Hegt, Vincent; Texier, Matthieu; Lanoy, Emilie; Eggermont, Alexander M M; Paci, Angelo; Robert, Caroline; Puppels, Gerwin J; Mir, Lluis M

2017-01-15

Raman spectroscopy is a noninvasive and label-free optical technique that provides detailed information about the molecular composition of a sample. In this study, we evaluated the potential of Raman spectroscopy to predict skin toxicity due to tyrosine kinase inhibitors treatment. We acquired Raman spectra of skin of patients undergoing treatment with MEK, EGFR, or BRAF inhibitors, which are known to induce severe skin toxicity; for this pilot study, three patients were included for each inhibitor. Our algorithm, based on partial least squares-discriminant analysis (PLS-DA) and cross-validation by bootstrapping, discriminated to variable degrees spectra from patient suffering and not suffering cutaneous adverse events. For MEK and EGFR inhibitors, discriminative power was more than 90% in the viable epidermis skin layer; whereas for BRAF inhibitors, discriminative power was 71%. There was a 81.5% correlation between blood drug concentration and Raman signature of skin in the case of EGFR inhibitors and viable epidermis skin layer. Our results demonstrate the power of Raman spectroscopy to detect apparition of skin toxicity in patients treated with tyrosine kinase inhibitors at levels not detectable via dermatological inspection and histological evaluation. Cancer Res; 77(2); 557-65. ©2016 AACR. ©2016 American Association for Cancer Research.

GC–MS-Based Metabonomic Profiling Displayed Differing Effects of Borna Disease Virus Natural Strain Hu-H1 and Laboratory Strain V Infection in Rat Cortical Neurons

PubMed Central

Liu, Siwen; Bode, Liv; Zhang, Lujun; He, Peng; Huang, Rongzhong; Sun, Lin; Chen, Shigang; Zhang, Hong; Guo, Yujie; Zhou, Jingjing; Fu, Yuying; Zhu, Dan; Xie, Peng

2015-01-01

Borna disease virus (BDV) persists in the central nervous systems of a wide variety of vertebrates and causes behavioral disorders. Previous studies have revealed that metabolic perturbations are associated with BDV infection. However, the pathophysiological effects of different viral strains remain largely unknown. Rat cortical neurons infected with human strain BDV Hu-H1, laboratory BDV Strain V, and non-infected control (CON) cells were cultured in vitro. At day 12 post-infection, a gas chromatography coupled with mass spectrometry (GC–MS) metabonomic approach was used to differentiate the metabonomic profiles of 35 independent intracellular samples from Hu-H1-infected cells (n = 12), Strain V-infected cells (n = 12), and CON cells (n = 11). Partial least squares discriminant analysis (PLS-DA) was performed to demonstrate discrimination between the three groups. Further statistical testing determined which individual metabolites displayed significant differences between groups. PLS-DA demonstrated that the whole metabolic pattern enabled statistical discrimination between groups. We identified 31 differential metabolites in the Hu-H1 and CON groups (21 decreased and 10 increased in Hu-H1 relative to CON), 35 differential metabolites in the Strain V and CON groups (30 decreased and 5 increased in Strain V relative to CON), and 21 differential metabolites in the Hu-H1 and Strain V groups (8 decreased and 13 increased in Hu-H1 relative to Strain V). Comparative metabonomic profiling revealed divergent perturbations in key energy and amino acid metabolites between natural strain Hu-H1 and laboratory Strain V of BDV. The two BDV strains differentially alter metabolic pathways of rat cortical neurons in vitro. Their systematic classification provides a valuable template for improved BDV strain definition in future studies. PMID:26287181

[Identification of two varieties of Citri Fructus by fingerprint and chemometrics].

PubMed

Su, Jing-hua; Zhang, Chao; Sun, Lei; Gu, Bing-ren; Ma, Shuang-cheng

2015-06-01

Citri Fructus identification by fingerprint and chemometrics was investigated in this paper. Twenty-three Citri Fructus samples were collected which referred to two varieties as Cirtus wilsonii and C. medica recorded in Chinese Pharmacopoeia. HPLC chromatograms were obtained. The components were partly identified by reference substances, and then common pattern was established for chemometrics analysis. Similarity analysis, principal component analysis (PCA) , partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis heatmap were applied. The results indicated that C. wilsonii and C. medica could be ideally classified with common pattern contained twenty-five characteristic peaks. Besides, preliminary pattern recognition had verified the chemometrics analytical results. Absolute peak area (APA) was used for relevant quantitative analysis, results showed the differences between two varieties and it was valuable for further quality control as selection of characteristic components.

Metabolomic Analysis of Oxidative and Glycolytic Skeletal Muscles by Matrix-Assisted Laser Desorption/IonizationMass Spectrometric Imaging (MALDI MSI)

NASA Astrophysics Data System (ADS)

Tsai, Yu-Hsuan; Garrett, Timothy J.; Carter, Christy S.; Yost, Richard A.

2015-06-01

Skeletal muscles are composed of heterogeneous muscle fibers that have different physiological, morphological, biochemical, and histological characteristics. In this work, skeletal muscles extensor digitorum longus, soleus, and whole gastrocnemius were analyzed by matrix-assisted laser desorption/ionization mass spectrometry to characterize small molecule metabolites of oxidative and glycolytic muscle fiber types as well as to visualize biomarker localization. Multivariate data analysis such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to extract significant features. Different metabolic fingerprints were observed from oxidative and glycolytic fibers. Higher abundances of biomolecules such as antioxidant anserine as well as acylcarnitines were observed in the glycolytic fibers, whereas taurine and some nucleotides were found to be localized in the oxidative fibers.

Assessment of the discrimination of animal fat by FT-Raman spectroscopy

NASA Astrophysics Data System (ADS)

Abbas, O.; Fernández Pierna, J. A.; Codony, R.; von Holst, C.; Baeten, V.

2009-04-01

In recent years, there has been an increased attention towards the composition of feeding fats. In the aftermath of the BSE crisis all animal by-products utilised in animal nutrition have been subjected to close scrutiny. Regulation requires that the material belongs to the category of animal by-products fit for human consumption. This implies the use of reliable techniques in order to insure the safety of products. The feasibility of using rapid and non-destructive methods, to control the composition of feedstuffs on animal fats has been studied. Fourier Transform Raman spectroscopy has been chosen for its advantage to give detailed structural information. Data were treated using chemometric methods as PCA and PLS-DA which have permitted to separate well the different classes of animal fats. The same methodology was applied on fats from various types of feedstock and production technology processes. PLS-DA model for the discrimination of animal fats from the other categories presents a sensitivity and a specificity of 0.958 and 0.914, respectively. These results encourage the use of FT-Raman spectroscopy to discriminate animal fats.

Automatic processing of tones and speech stimuli in children with specific language impairment.

PubMed

Uwer, Ruth; Albrecht, Ronald; von Suchodoletz, W

2002-08-01

It is well known from behavioural experiments that children with specific language impairment (SLI) have difficulties discriminating consonant-vowel (CV) syllables such as /ba/, /da/, and /ga/. Mismatch negativity (MMN) is an auditory event-related potential component that represents the outcome of an automatic comparison process. It could, therefore, be a promising tool for assessing central auditory processing deficits for speech and non-speech stimuli in children with SLI. MMN is typically evoked by occasionally occurring 'deviant' stimuli in a sequence of identical 'standard' sounds. In this study MMN was elicited using simple tone stimuli, which differed in frequency (1000 versus 1200 Hz) and duration (175 versus 100 ms) and to digitized CV syllables which differed in place of articulation (/ba/, /da/, and /ga/) in children with expressive and receptive SLI and healthy control children (n=21 in each group, 46 males and 17 females; age range 5 to 10 years). Mean MMN amplitudes between groups were compared. Additionally, the behavioural discrimination performance was assessed. Children with SLI had attenuated MMN amplitudes to speech stimuli, but there was no significant difference between the two diagnostic subgroups. MMN to tone stimuli did not differ between the groups. Children with SLI made more errors in the discrimination task, but discrimination scores did not correlate with MMN amplitudes. The present data suggest that children with SLI show a specific deficit in automatic discrimination of CV syllables differing in place of articulation, whereas the processing of simple tone differences seems to be unimpaired.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

PubMed

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-03-08

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds

PubMed Central

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-01-01

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400–1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides’ spectra of every seed), and mixture datasets (two sides’ spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner. PMID:29517991

Detection of residues from explosive manipulation by near infrared hyperspectral imaging: a promising forensic tool.

PubMed

Fernández de la Ossa, Mª Ángeles; Amigo, José Manuel; García-Ruiz, Carmen

2014-09-01

In this study near infrared hyperspectral imaging (NIR-HSI) is used to provide a fast, non-contact, non-invasive and non-destructive method for the analysis of explosive residues on human handprints. Volunteers manipulated individually each of these explosives and after deposited their handprints on plastic sheets. For this purpose, classical explosives, potentially used as part of improvised explosive devices (IEDs) as ammonium nitrate, blackpowder, single- and double-base smokeless gunpowders and dynamite were studied. A partial-least squares discriminant analysis (PLS-DA) model was built to detect and classify the presence of explosive residues in handprints. High levels of sensitivity and specificity for the PLS-DA classification model created to identify ammonium nitrate, blackpowder, single- and double-base smokeless gunpowders and dynamite residues were obtained, allowing the development of a preliminary library and facilitating the direct and in situ detection of explosives by NIR-HSI. Consequently, this technique is showed as a promising forensic tool for the detection of explosive residues and other related samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An integrated approach utilising chemometrics and GC/MS for classification of chamomile flowers, essential oils and commercial products.

PubMed

Wang, Mei; Avula, Bharathi; Wang, Yan-Hong; Zhao, Jianping; Avonto, Cristina; Parcher, Jon F; Raman, Vijayasankar; Zweigenbaum, Jerry A; Wylie, Philip L; Khan, Ikhlas A

2014-01-01

As part of an ongoing research program on authentication, safety and biological evaluation of phytochemicals and dietary supplements, an in-depth chemical investigation of different types of chamomile was performed. A collection of chamomile samples including authenticated plants, commercial products and essential oils was analysed by GC/MS. Twenty-seven authenticated plant samples representing three types of chamomile, viz. German chamomile, Roman chamomile and Juhua were analysed. This set of data was employed to construct a sample class prediction (SCP) model based on stepwise reduction of data dimensionality followed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The model was cross-validated with samples including authenticated plants and commercial products. The model demonstrated 100.0% accuracy for both recognition and prediction abilities. In addition, 35 commercial products and 11 essential oils purported to contain chamomile were subsequently predicted by the validated PLS-DA model. Furthermore, tentative identification of the marker compounds correlated with different types of chamomile was explored. Copyright © 2013 Elsevier Ltd. All rights reserved.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Ajal, El Amine; Cuadros-Rodríguez, Luis; Monasterio, Romina Paula; Vercammen, Joeri; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2018-04-01

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H 3 O + , NO + and O 2 + as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90-100%) and prediction accuracy (96.70-100% accuracy for cross validation and 97.30-100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases). Copyright © 2017 Elsevier Ltd. All rights reserved.

Differentiation of Crataegus spp. guided by nuclear magnetic resonance spectrometry with chemometric analyses.

PubMed

Lund, Jensen A; Brown, Paula N; Shipley, Paul R

2017-09-01

For compliance with US Current Good Manufacturing Practice regulations for dietary supplements, manufacturers must provide identity of source plant material. Despite the popularity of hawthorn as a dietary supplement, relatively little is known about the comparative phytochemistry of different hawthorn species, and in particular North American hawthorns. The combination of NMR spectrometry with chemometric analyses offers an innovative approach to differentiating hawthorn species and exploring the phytochemistry. Two European and two North American species, harvested from a farm trial in late summer 2008, were analyzed by standard 1D 1 H and J-resolved (JRES) experiments. The data were preprocessed and modelled by principal component analysis (PCA). A supervised model was then generated by partial least squares-discriminant analysis (PLS-DA) for classification and evaluated by cross validation. Supervised random forests models were constructed from the dataset to explore the potential of machine learning for identification of unique patterns across species. 1D 1 H NMR data yielded increased differentiation over the JRES data. The random forests results correlated with PLS-DA results and outperformed PLS-DA in classification accuracy. In all of these analyses differentiation of the Crataegus spp. was best achieved by focusing on the NMR spectral region that contains signals unique to plant phenolic compounds. Identification of potentially significant metabolites for differentiation between species was approached using univariate techniques including significance analysis of microarrays and Kruskall-Wallis tests. Copyright © 2017 Elsevier Ltd. All rights reserved.

Forensic evaluation and population genetic study of 30 insertion/deletion polymorphisms in a Chinese Yi group.

PubMed

Zhang, Yu-Dang; Shen, Chun-Mei; Jin, Rui; Li, Ya-Ni; Wang, Bo; Ma, Li-Xia; Meng, Hao-Tian; Yan, Jiang-Wei; Dan Wang, Hong-; Yang, Ze-Long; Zhu, Bo-Feng

2015-05-01

Insertion/deletion polymorphisms have become a research hot spot in forensic science due to their tremendous potential in recent years. In the present study, we investigated 30 indel loci in a Chinese Yi ethnic group. The allele frequencies of the short allele of the 30 indel loci were in the range of 0.1025-0.9221. The power of discrimination values were observed ranging from to 0.2630 (HLD111 locus) to 0.6607 (HLD70 locus) and probability of exclusion values ranged from 0.0189 (HLD111 locus) to 0.2343 (HLD56 locus). The combined power of discrimination and power of exclusion for 30 loci in the studied Yi group were 0.99999999995713 and 0.97746, respectively, which showed tremendous potential for forensic personal identification in the Yi group. Moreover, the DA distances, phylogenetic tree, principal component analysis, and cluster analysis showed the Yi group had close genetic relationships with the Tibetan, South Korean, Chinese Han, and She groups. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

PubMed

Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

2015-12-01

The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. © 2015 Institute of Food Technologists®

Feasibility of laser-induced breakdown spectroscopy (LIBS) for classification of sea salts.

PubMed

Tan, Man Minh; Cui, Sheng; Yoo, Jonghyun; Han, Song-Hee; Ham, Kyung-Sik; Nam, Sang-Ho; Lee, Yonghoon

2012-03-01

We have investigated the feasibility of laser-induced breakdown spectroscopy (LIBS) as a fast, reliable classification tool for sea salts. For 11 kinds of sea salts, potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al), concentrations were measured by inductively coupled plasma-atomic emission spectroscopy (ICP-AES), and the LIBS spectra were recorded in the narrow wavelength region between 760 and 800 nm where K (I), Mg (I), Ca (II), Al (I), and cyanide (CN) band emissions are observed. The ICP-AES measurements revealed that the K, Mg, Ca, and Al concentrations varied significantly with the provenance of each salt. The relative intensities of the K (I), Mg (I), Ca (II), and Al (I) peaks observed in the LIBS spectra are consistent with the results using ICP-AES. The principal component analysis of the LIBS spectra provided the score plot with quite a high degree of clustering. This indicates that classification of sea salts by chemometric analysis of LIBS spectra is very promising. Classification models were developed by partial least squares discriminant analysis (PLS-DA) and evaluated. In addition, the Al (I) peaks enabled us to discriminate between different production methods of the salts. © 2012 Society for Applied Spectroscopy

Modification method to reduce the impact of blood vessel on noncontact discrimination of human blood based on ;M+N; theory

NASA Astrophysics Data System (ADS)

Zhang, Linna; Ding, Hongyan; Lin, Ling; Wang, Yimin; Guo, Xin

2018-01-01

Noncontact discriminating human blood is significantly crucial for import-export ports and inspection and quarantine departments. We had already demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize noncontact human blood discrimination. However, the circulated blood vessels may be produced with different materials. The use of various kinds of blood tubes may have a negative effect on the discrimination, based on ;M+N; theory (Li et al., 2016). In this research, we explored the impact of different material of blood vessels, such as glass tube and plastic tube, on the prediction ability of the discrimination model. Furthermore, we searched for the modification method to reduce the influence from the blood tubes. Our work indicated that generalized diffuse reflectance method can greatly improve the discrimination accuracy. This research can greatly facilitate the application of noncontact discrimination method based on visible and near-infrared diffuse reflectance spectroscopy.

Comprehensive analysis of serum metabolites in gestational diabetes mellitus by UPLC/Q-TOF-MS.

PubMed

Liu, Tianhu; Li, Jiaxun; Xu, Fengcheng; Wang, Mengni; Ding, Shijia; Xu, Hongbing; Dong, Fang

2016-02-01

Gestational diabetes mellitus (GDM) refers to the first sign or onset of diabetes mellitus during pregnancy rather than progestation. In recent decades, more and more research has focused on the etiology and pathogenesis of GDM in order to further understand GDM progress and recovery. Using an advanced metabolomics platform based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), we explored the changes in serum metabolites between women with GDM and healthy controls during and after pregnancy. Some significant differences were discovered using multivariate analysis including partial least-squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA). The dysregulated metabolites were further compared and verified in several databases to understand how these compounds might function as potential biomarkers. Analyses of the metabolic pathways associated with these potential biomarkers were subsequently explored. A total of 35 metabolites were identified, contributing to GDM progress to some extent. The identified biomarkers were involved in some important metabolic pathways including glycine, serine, and threonine metabolism; steroid hormone biosynthesis; tyrosine metabolism; glycerophospholipid metabolism; and fatty acid metabolism. The above mentioned metabolic pathways mainly participate in three major metabolic cycles in humans, including lipid metabolism, carbohydrate metabolism, and amino acid metabolism. In this pilot study, the valuable comprehensive analysis gave us further insight into the etiology and pathophysiology of GDM, which might benefit the feasibility of a rapid, accurate diagnosis and reasonable treatment as soon as possible but also prevent GDM and its related short- and long-term complications.

Variation of δ2H, δ18O & δ13C in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter.

PubMed

Muhammad, Syahidah Akmal; Seow, Eng-Keng; Mohd Omar, A K; Rodhi, Ainolsyakira Mohd; Mat Hassan, Hasnuri; Lalung, Japareng; Lee, Sze-Chi; Ibrahim, Baharudin

2018-01-01

A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ 13 C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ 2 H and δ 18 O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ 2 H, δ 18 O and δ 13 C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ 18 O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-01

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis.

PubMed

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-05

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms. Copyright © 2018 Elsevier B.V. All rights reserved.

Classification of type 2 diabetes rats based on urine amino acids metabolic profiling by liquid chromatography coupled with tandem mass spectrometry.

PubMed

Wang, Chunyan; Zhu, Hongbin; Pi, Zifeng; Song, Fengrui; Liu, Zhiqiang; Liu, Shuying

2013-09-15

An analytical method for quantifying underivatized amino acids (AAs) in urine samples of rats was developed by using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Classification of type 2 diabetes rats was based on urine amino acids metabolic profiling. LC-MS/MS analysis was applied through chromatographic separation and multiple reactions monitoring (MRM) transitions of MS/MS. Multivariate profile-wide predictive models were constructed using partial least squares discriminant analysis (PLS-DA) by SIMAC-P 11.5 version software package and hierarchical cluster analysis (HCA) by SPSS 18.0 version software. Some amino acids in urine of rats have significant change. The results of the present study prove that this method could perform the quantification of free AAs in urine of rats by using LC-MS/MS. In summary, the PLS-DA and HCA statistical analysis in our research were preferable to differentiate healthy rats and type 2 diabetes rats by the quantification of AAs in their urine samples. In addition, comparing with health group the seven increased amino acids in urine of type 2 rats were returned to normal under the treatment of acarbose. Copyright © 2013 Elsevier B.V. All rights reserved.

HPLC-ESI-MS(n) Analysis, Fed-Batch Cultivation Enhances Bioactive Compound Biosynthesis and Immune-Regulative Effect of Adventitious Roots in Pseudostellaria heterophylla.

PubMed

Wang, Juan; Li, Jing; Li, Hongfa; Wu, Xiaolei; Gao, Wenyuan

2015-09-01

A electrospray ionization tandem mass spectrometry (ESI-MS(n)) analysis was performed in order to identify the active composition in Pseudostellaria heterophylla adventitious roots. Pseudostellarin A, C, D, and G were identified from P. heterophylla adventitious roots on the basis of LC-MS(n) analysis. The culture conditions of adventitious roots were optimized, and datasets were subjected to a partial least squares discriminant analysis (PLS-DA), in which the growth ratio and some compounds showed a positive correlation with an aeration volume of 0.3 vvm and inoculum density of 0.15 %. Fed-batch cultivation enhanced the contents of total saponin, polysaccharides, and specific oxygen uptaker rate (SOUR). The maximum dry root weight (4.728 g l(-1)) was achieved in the 3/4 Murashige and Skoog (MS) medium group. PLS-DA showed that polysaccharides contributed significantly to the clustering of different groups and showed a positive correlation in the MS medium group. The delayed-type hypersensitivity (DTH) reaction on the mice induced by 2,4-dinitrofluorobenzene (DNFB) was applied to compare the immunocompetence effects of adventitious roots (AR) with field native roots (NR) of P. heterophylla. As a result, AR possessed a similar immunoregulation function as NR.

Ultraviolet spectroscopy combined with ultra-fast liquid chromatography and multivariate statistical analysis for quality assessment of wild Wolfiporia extensa from different geographical origins.

PubMed

Li, Yan; Zhang, Ji; Jin, Hang; Liu, Honggao; Wang, Yuanzhong

2016-08-05

A quality assessment system comprised of a tandem technique of ultraviolet (UV) spectroscopy and ultra-fast liquid chromatography (UFLC) aided by multivariate analysis was presented for the determination of geographic origin of Wolfiporia extensa collected from five regions in Yunnan Province of China. Characteristic UV spectroscopic fingerprints of samples were determined based on its methanol extract. UFLC was applied for the determination of pachymic acid (a biomarker) presented in individual test samples. The spectrum data matrix and the content of pachymic acid were integrated and analyzed by partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The results showed that chemical properties of samples were clearly dominated by the epidermis and inner part as well as geographical origins. The relationships among samples obtained from these five regions have been also presented. Moreover, an interesting finding implied that geographical origins had much greater influence on the chemical properties of epidermis compared with that of the inner part. This study demonstrated that a rapid tool for accurate discrimination of W. extensa by UV spectroscopy and UFLC could be available for quality control of complicated medicinal mushrooms. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of Ultra-performance Liquid Chromatography with Time-of-Flight Mass Spectrometry for the Rapid Analysis of Constituents and Metabolites from the Extracts of Acanthopanax senticosus Harms Leaf

PubMed Central

Zhang, Yingzhi; Zhang, Aihua; Zhang, Ying; Sun, Hui; Meng, Xiangcai; Yan, Guangli; Wang, Xijun

2016-01-01

Acanthopanax senticosus (Rupr and Maxim) Harms (AS), a member of Araliaceae family, is a typical folk medicinal herb, which is widely distributed in the Northeastern part of China. Due to lack of this resource caused by the extensive use of its root, this work studied the chemical constituents of leaves of this plant with the purpose of looking for an alternative resource. In this work, a fast and optimized ultra-performance liquid chromatography method with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) has been developed for the analysis of constituents in leaves extracts. A total of 131 compounds were identified or tentatively characterized including triterpenoid saponins, phenols, flavonoids, lignans, coumarins, polysaccharides, and other compounds based on their fragmentation behaviors. Besides, a total of 21 metabolites were identified in serum in rats after oral administration, among which 12 prototypes and 9 metabolites through the metabolic pathways of reduction, methylation, sulfate conjugation, sulfoxide to thioether and deglycosylation. The coupling of UPLC-QTOF-MS led to the in-depth characterization of the leaves extracts of AS both in vitro and in vivo on the basis of retention time, mass accuracy, and tandem MS/MS spectra. It concluded that this analytical tool was very valuable in the study of complex compounds in medicinal herb. HIGHLIGHT OF PAPER A fast UPLC-QTOF-MS has been developed for analysis of constituents in leaves extractsA total of 131 compounds were identified in leaves extractsA total of 21 metabolites including 12 prototypes and 9 metabolites were identified in vivo. SUMMARY Constituent’s analysis of Acanthopanax senticosus Harms leaf by ultra-performance liquid chromatography method with quadrupole time-of-flight mass spectrometry. Abbreviations used: AS: Acanthopanax senticosus (Rupr and Maxim) Harms, TCHM: Traditional Chinese herbal medicine, UPLC-QTOF-MS: Ultra-performance liquid chromatography method with time-of-flight mass spectrometry, MS/MS: Tandem mass spectrometry, PCA: Principal component analysis, PLS-DA: Partial least squared discriminant analysis, OPLS-DA: Orthogonal projection to latent structure-discriminant analysis. PMID:27076752

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods.

PubMed

Marschner, C B; Kokla, M; Amigo, J M; Rozanski, E A; Wiinberg, B; McEvoy, F J

2017-07-11

Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry.

PubMed

Ahn, Joong Kyong; Kim, Jungyeon; Hwang, Jiwon; Song, Juhwan; Kim, Kyoung Heon; Cha, Hoon-Suk

2018-05-01

Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R 2 X of 0.246, R 2 Y of 0.913 and Q 2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS. Copyright © 2017. Published by Elsevier SAS.

Noncontact discrimination of animal and human blood with vacuum blood vessel and factors affect the discrimination

NASA Astrophysics Data System (ADS)

Zhang, Linna; Zhang, Shengzhao; Sun, Meixiu; Li, Hongxiao; Li, Yingxin; Fu, Zhigang; Guan, Yang; Li, Gang; Lin, Ling

2017-03-01

Discrimination of human and nonhuman blood is crucial for import-export ports and inspection and quarantine departments. Current methods are usually destructive, complicated and time-consuming. We had previously demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize human blood discrimination. In that research, the spectra were measured with the fiber probe under the surface of blood samples. However, open sampling may pollute the blood samples. Virulence factors in blood samples can also endanger inspectors. In this paper, we explored the classification effect with the blood samples measured in the original containers-vacuum blood vessel. Furthermore, we studied the impact of different conditions of blood samples, such as coagulation and hemolysis, on the prediction ability of the discrimination model. The calibration model built with blood samples in different conditions displayed a satisfactory prediction result. This research demonstrated that visible and near-infrared diffuse reflectance spectroscopy method was potential for noncontact discrimination of human blood.

1H-NMR based metabonomic profiling of human esophageal cancer tissue

PubMed Central

2013-01-01

Background The biomarker identification of human esophageal cancer is critical for its early diagnosis and therapeutic approaches that will significantly improve patient survival. Specially, those that involves in progression of disease would be helpful to mechanism research. Methods In the present study, we investigated the distinguishing metabolites in human esophageal cancer tissues (n = 89) and normal esophageal mucosae (n = 26) using a 1H nuclear magnetic resonance (1H-NMR) based assay, which is a highly sensitive and non-destructive method for biomarker identification in biological systems. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant anlaysis (OPLS-DA) were applied to analyse 1H-NMR profiling data to identify potential biomarkers. Results The constructed OPLS-DA model achieved an excellent separation of the esophageal cancer tissues and normal mucosae. Excellent separation was obtained between the different stages of esophageal cancer tissues (stage II = 28; stage III = 45 and stage IV = 16) and normal mucosae. A total of 45 metabolites were identified, and 12 of them were closely correlated with the stage of esophageal cancer. The downregulation of glucose, AMP and NAD, upregulation of formate indicated the large energy requirement due to accelerated cell proliferation in esophageal cancer. The increases in acetate, short-chain fatty acid and GABA in esophageal cancer tissue revealed the activation of fatty acids metabolism, which could satisfy the need for cellular membrane formation. Other modified metabolites were involved in choline metabolic pathway, including creatinine, creatine, DMG, DMA and TMA. These 12 metabolites, which are involved in energy, fatty acids and choline metabolism, may be associated with the progression of human esophageal cancer. Conclusion Our findings firstly identify the distinguishing metabolites in different stages of esophageal cancer tissues, indicating the attribution of metabolites disturbance to the progression of esophageal cancer. The potential biomarkers provide a promising molecular diagnostic approach for clinical diagnosis of human esophageal cancer and a new direction for the mechanism study. PMID:23556477

Metabolic Analysis of Medicinal Dendrobium officinale and Dendrobium huoshanense during Different Growth Years

PubMed Central

Jin, Qing; Jiao, Chunyan; Sun, Shiwei; Song, Cheng; Cai, Yongping; Lin, Yi; Fan, Honghong; Zhu, Yanfang

2016-01-01

Metabolomics technology has enabled an important method for the identification and quality control of Traditional Chinese Medical materials. In this study, we isolated metabolites from cultivated Dendrobium officinale and Dendrobium huoshanense stems of different growth years in the methanol/water phase and identified them using gas chromatography coupled with mass spectrometry (GC-MS). First, a metabolomics technology platform for Dendrobium was constructed. The metabolites in the Dendrobium methanol/water phase were mainly sugars and glycosides, amino acids, organic acids, alcohols. D. officinale and D. huoshanense and their growth years were distinguished by cluster analysis in combination with multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Eleven metabolites that contributed significantly to this differentiation were subjected to t-tests (P<0.05) to identify biomarkers that discriminate between D. officinale and D. huoshanense, including sucrose, glucose, galactose, succinate, fructose, hexadecanoate, oleanitrile, myo-inositol, and glycerol. Metabolic profiling of the chemical compositions of Dendrobium species revealed that the polysaccharide content of D. huoshanense was higher than that of D. officinale, indicating that the D. huoshanense was of higher quality. Based on the accumulation of Dendrobium metabolites, the optimal harvest time for Dendrobium was in the third year. This initial metabolic profiling platform for Dendrobium provides an important foundation for the further study of secondary metabolites (pharmaceutical active ingredients) and metabolic pathways. PMID:26752292

Metabolic Analysis of Medicinal Dendrobium officinale and Dendrobium huoshanense during Different Growth Years.

PubMed

Jin, Qing; Jiao, Chunyan; Sun, Shiwei; Song, Cheng; Cai, Yongping; Lin, Yi; Fan, Honghong; Zhu, Yanfang

2016-01-01

Metabolomics technology has enabled an important method for the identification and quality control of Traditional Chinese Medical materials. In this study, we isolated metabolites from cultivated Dendrobium officinale and Dendrobium huoshanense stems of different growth years in the methanol/water phase and identified them using gas chromatography coupled with mass spectrometry (GC-MS). First, a metabolomics technology platform for Dendrobium was constructed. The metabolites in the Dendrobium methanol/water phase were mainly sugars and glycosides, amino acids, organic acids, alcohols. D. officinale and D. huoshanense and their growth years were distinguished by cluster analysis in combination with multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Eleven metabolites that contributed significantly to this differentiation were subjected to t-tests (P<0.05) to identify biomarkers that discriminate between D. officinale and D. huoshanense, including sucrose, glucose, galactose, succinate, fructose, hexadecanoate, oleanitrile, myo-inositol, and glycerol. Metabolic profiling of the chemical compositions of Dendrobium species revealed that the polysaccharide content of D. huoshanense was higher than that of D. officinale, indicating that the D. huoshanense was of higher quality. Based on the accumulation of Dendrobium metabolites, the optimal harvest time for Dendrobium was in the third year. This initial metabolic profiling platform for Dendrobium provides an important foundation for the further study of secondary metabolites (pharmaceutical active ingredients) and metabolic pathways.

A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

DOE PAGES

Fredriksson, Sten-Ake; Wunschel, David S.; Lindstrom, Susanne Wiklund; ...

2018-03-28

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods andmore » robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.« less

A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fredriksson, Sten-Ake; Wunschel, David S.; Lindstrom, Susanne Wiklund

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods andmore » robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.« less

Potential of near-infrared spectroscopy for quality evaluation of cattle leather.

PubMed

Braz, Carlos Eduardo M; Jacinto, Manuel Antonio C; Pereira-Filho, Edenir R; Souza, Gilberto B; Nogueira, Ana Rita A

2018-05-09

Models using near-infrared spectroscopy (NIRS) were constructed based on physical-mechanical tests to determine the quality of cattle leather. The following official parameters were used, considering the industry requirements: tensile strength (TS), percentage elongation (%E), tear strength (TT), and double hole tear strength (DHS). Classification models were constructed with the use of k-nearest neighbor (kNN), soft independent modeling of class analogy (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The evaluated figures of merit, accuracy, sensitivity, and specificity presented results between 85% and 93%, and the false alarm rates from 9% to 14%. The model with lowest validation percentage (92%) was kNN, and the highest was PLS-DA (100%). For TS, lower values were obtained, from 52% for kNN and 74% for SIMCA. The other parameters %E, TT, and DHS presented hit rates between 87 and 100%. The abilities of the models were similar, showing they can be used to predict the quality of cattle leather. Copyright © 2018 Elsevier B.V. All rights reserved.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Utility of mesohabitat features for determining habitat associations of subadult sharks in Georgia’s estuaries

USGS Publications Warehouse

Belcher, C.N.; Jennings, Cecil A.

2010-01-01

We examined the affects of selected water quality variables on the presence of subadult sharks in six of nine Georgia estuaries. During 231 longline sets, we captured 415 individuals representing nine species. Atlantic sharpnose shark (Rhizoprionodon terranovae), bonnethead (Sphyrna tiburo), blacktip shark (Carcharhinus limbatus) and sandbar shark (C. plumbeus) comprised 96.1% of the catch. Canonical correlation analysis (CCA) was used to assess environmental influences on the assemblage of the four common species. Results of the CCA indicated Bonnethead Shark and Sandbar Shark were correlated with each other and with a subset of environmental variables. When the species occurred singly, depth was the defining environmental variable; whereas, when the two co-occurred, dissolved oxygen and salinity were the defining variables. Discriminant analyses (DA) were used to assess environmental influences on individual species. Results of the discriminant analyses supported the general CCA findings that the presence of bonnethead and sandbar shark were the only two species that correlated with environmental variables. In addition to depth and dissolved oxygen, turbidity influenced the presence of sandbar shark. The presence of bonnethead shark was influenced primarily by salinity and turbidity. Significant relationships existed for both the CCA and DA analyses; however, environmental variables accounted for <16% of the total variation in each. Compared to the environmental variables we measured, macrohabitat features (e.g., substrate type), prey availability, and susceptibility to predation may have stronger influences on the presence and distribution of subadult shark species among sites.

Assessing a traceability technique in fresh oranges (Citrus sinensis L. Osbeck) with an HS-SPME-GC-MS method. Towards a volatile characterisation of organic oranges.

PubMed

Cuevas, Francisco Julián; Moreno-Rojas, José Manuel; Ruiz-Moreno, María José

2017-04-15

A targeted approach using HS-SPME-GC-MS was performed to compare flavour compounds of 'Navelina' and 'Salustiana' orange cultivars from organic and conventional management systems. Both varieties of conventional oranges showed higher content of ester compounds. On the other hand, higher content of some compounds related with the geranyl-diphosphate pathway (neryl and geranyl acetates) and some terpenoids were found in the organic samples. Furthermore, the partial least square discriminant analysis (PLS-DA) achieved an effective classification for oranges based on the farming system using their volatile profiles (90 and 100% correct classification). To our knowledge, it is the first time that a comparative study dealing with farming systems and orange aroma profile has been performed. These new insights, taking into account local databases, cultivars and advanced analytical tools, highlight the potential of volatile composition for organic orange discrimination. Copyright © 2016 Elsevier Ltd. All rights reserved.

Meat mixture detection in Iberian pork sausages.

PubMed

Ortiz-Somovilla, V; España-España, F; De Pedro-Sanz, E J; Gaitán-Jurado, A J

2005-11-01

Five homogenized meat mixture treatments of Iberian (I) and/or Standard (S) pork were set up. Each treatment was analyzed by NIRS as a fresh product (N=75) and as dry-cured sausage (N=75). Spectra acquisition was carried out using DA 7000 equipment (Perten Instruments), obtaining a total of 750 spectra. Several absorption peaks and bands were selected as the most representative for homogenized dry-cured and fresh sausages. Discriminant analysis and mixture prediction equations were carried out based on the spectral data gathered. The best results using discriminant models were for fresh products, with 98.3% (calibration) and 60% (validation) correct classification. For dry-cured sausages 91.7% (calibration) and 80% (validation) of the samples were correctly classified. Models developed using mixture prediction equations showed SECV=4.7, r(2)=0.98 (calibration) and 73.3% of validation set were correctly classified for the fresh product. These values for dry-cured sausages were SECV=5.9, r(2)=0.99 (calibration) and 93.3% correctly classified for validation.

Metabolomic profiles illuminate the efficacy of Chinese herbal Da-Cheng-Qi decoction on acute pancreatitis in rats.

PubMed

Li, Juan; Zhu, Shi-Feng; Zhao, Xian-Lin; Liu, Yi-Xia; Wan, Mei-Hua; Guo, Hui; Liu, Yi-Ling; Gong, Han-Lin; Chen, Guang-Yuan; Tang, Wen-Fu

2015-01-01

Chinese herbal drug Da-Cheng-Qi decoction (DCQD) has been widely used for decades to treat acute pancreatitis (AP). Previous trials are mostly designed to state the potential mechanisms of the therapeutic effects rather than to detect its whole effect on metabolism. This study aimed to investigate the efficacy of DCQD on metabolism in AP. Twenty-two male adult Sprague-Dawley rats were randomized into three groups. AP was induced by retrograde ductal infusion of 3.5% sodium taurocholate solution in DCQD and AP group, while 0.9% saline solution was used in sham operation (SO) group. Blood samples were obtained 12 h after drug administration and a 600 MHz superconducting Nuclear Magnetic Resonance (NMR) spectrometer was used to detected plasma metabolites. Principal Components Analysis (PCA) and Partial Least Squares-Discriminant Analysis after Orthogonal Signal Correction (OSC-PLS-DA) were applied to analyze the Longitudinal Eddy-delay (LED) and Carr-Purcell-Meiboom-Gill (CPMG) spectra. Differences in concentrations of metabolites among the three groups were detected by OSC-PLS-DA of 1HNMR spectra (both LED and CPMG). Compared with SO group, DCQD group had higher levels of plasma glycerol, glutamic acid, low density lipoprotein (LDL), saturated fatty acid (FA) and lower levels of alanine and glutamine, while the metabolic changes were reversed in the AP group. Our results demonstrated that DCQD was capable of altering the changed concentrations of metabolites in rats with AP and 1HNMR-based metabolomic approach provided a new methodological cue for systematically investigating the efficacies and mechanisms of DCQD in treating AP. Copyright © 2015 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Testing of complementarity of PDA and MS detectors using chromatographic fingerprinting of genuine and counterfeit samples containing sildenafil citrate.

PubMed

Custers, Deborah; Krakowska, Barbara; De Beer, Jacques O; Courselle, Patricia; Daszykowski, Michal; Apers, Sandra; Deconinck, Eric

2016-02-01

Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products is a very important issue. In this study, a high-performance liquid chromatography-photodiode array (HPLC-PDA) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) method were developed for the analysis of genuine Viagra®, generic products of Viagra®, and counterfeit samples in order to obtain different types of fingerprints. These data were included in the chemometric data analysis, aiming to test whether PDA and MS are complementary detection techniques. The MS data comprise both MS1 and MS2 fingerprints; the PDA data consist of fingerprints measured at three different wavelengths, i.e., 254, 270, and 290 nm, and all possible combinations of these wavelengths. First, it was verified if both groups of fingerprints can discriminate between genuine, generic, and counterfeit medicines separately; next, it was studied if the obtained results could be ameliorated by combining both fingerprint types. This data analysis showed that MS1 does not provide suitable classification models since several genuines and generics are classified as counterfeits and vice versa. However, when analyzing the MS1_MS2 data in combination with partial least squares-discriminant analysis (PLS-DA), a perfect discrimination was obtained. When only using data measured at 254 nm, good classification models can be obtained by k nearest neighbors (kNN) and soft independent modelling of class analogy (SIMCA), which might be interesting for the characterization of counterfeit drugs in developing countries. However, in general, the combination of PDA and MS data (254 nm_MS1) is preferred due to less classification errors between the genuines/generics and counterfeits compared to PDA and MS data separately.

Temporal dynamics of sensorimotor integration in speech perception and production: independent component analysis of EEG data

PubMed Central

Jenson, David; Bowers, Andrew L.; Harkrider, Ashley W.; Thornton, David; Cuellar, Megan; Saltuklaroglu, Tim

2014-01-01

Activity in anterior sensorimotor regions is found in speech production and some perception tasks. Yet, how sensorimotor integration supports these functions is unclear due to a lack of data examining the timing of activity from these regions. Beta (~20 Hz) and alpha (~10 Hz) spectral power within the EEG μ rhythm are considered indices of motor and somatosensory activity, respectively. In the current study, perception conditions required discrimination (same/different) of syllables pairs (/ba/ and /da/) in quiet and noisy conditions. Production conditions required covert and overt syllable productions and overt word production. Independent component analysis was performed on EEG data obtained during these conditions to (1) identify clusters of μ components common to all conditions and (2) examine real-time event-related spectral perturbations (ERSP) within alpha and beta bands. 17 and 15 out of 20 participants produced left and right μ-components, respectively, localized to precentral gyri. Discrimination conditions were characterized by significant (pFDR < 0.05) early alpha event-related synchronization (ERS) prior to and during stimulus presentation and later alpha event-related desynchronization (ERD) following stimulus offset. Beta ERD began early and gained strength across time. Differences were found between quiet and noisy discrimination conditions. Both overt syllable and word productions yielded similar alpha/beta ERD that began prior to production and was strongest during muscle activity. Findings during covert production were weaker than during overt production. One explanation for these findings is that μ-beta ERD indexes early predictive coding (e.g., internal modeling) and/or overt and covert attentional/motor processes. μ-alpha ERS may index inhibitory input to the premotor cortex from sensory regions prior to and during discrimination, while μ-alpha ERD may index sensory feedback during speech rehearsal and production. PMID:25071633

Metabolomic and Lipidomic Analysis of Serum Samples following Curcuma longa Extract Supplementation in High-Fructose and Saturated Fat Fed Rats.

PubMed

Tranchida, Fabrice; Shintu, Laetitia; Rakotoniaina, Zo; Tchiakpe, Léopold; Deyris, Valérie; Hiol, Abel; Caldarelli, Stefano

2015-01-01

We explored, using nuclear magnetic resonance (NMR) metabolomics and fatty acids profiling, the effects of a common nutritional complement, Curcuma longa, at a nutritionally relevant dose with human use, administered in conjunction with an unbalanced diet. Indeed, traditional food supplements have been long used to counter metabolic impairments induced by unbalanced diets. Here, rats were fed either a standard diet, a high level of fructose and saturated fatty acid (HFS) diet, a diet common to western countries and that certainly contributes to the epidemic of insulin resistance (IR) syndrome, or a HFS diet with a Curcuma longa extract (1% of curcuminoids in the extract) for ten weeks. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on the serum NMR profiles and fatty acid composition (determined by GC/MS) showed a clear discrimination between HFS groups and controls. This discrimination involved metabolites such as glucose, amino acids, pyruvate, creatine, phosphocholine/glycerophosphocholine, ketone bodies and glycoproteins as well as an increase of monounsaturated fatty acids (MUFAs) and a decrease of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Although the administration of Curcuma longa did not prevent the observed increase of glucose, triglycerides, cholesterol and insulin levels, discriminating metabolites were observed between groups fed HFS alone or with addition of a Curcuma longa extract, namely some MUFA and n-3 PUFA, glycoproteins, glutamine, and methanol, suggesting that curcuminoids may act respectively on the fatty acid metabolism, the hexosamine biosynthesis pathway and alcohol oxidation. Curcuma longa extract supplementation appears to be beneficial in these metabolic pathways in rats. This metabolomic approach highlights important serum metabolites that could help in understanding further the metabolic mechanisms leading to IR.

Metabolomic and Lipidomic Analysis of Serum Samples following Curcuma longa Extract Supplementation in High-Fructose and Saturated Fat Fed Rats

PubMed Central

Tranchida, Fabrice; Shintu, Laetitia; Rakotoniaina, Zo; Tchiakpe, Léopold; Deyris, Valérie; Hiol, Abel; Caldarelli, Stefano

2015-01-01

We explored, using nuclear magnetic resonance (NMR) metabolomics and fatty acids profiling, the effects of a common nutritional complement, Curcuma longa, at a nutritionally relevant dose with human use, administered in conjunction with an unbalanced diet. Indeed, traditional food supplements have been long used to counter metabolic impairments induced by unbalanced diets. Here, rats were fed either a standard diet, a high level of fructose and saturated fatty acid (HFS) diet, a diet common to western countries and that certainly contributes to the epidemic of insulin resistance (IR) syndrome, or a HFS diet with a Curcuma longa extract (1% of curcuminoids in the extract) for ten weeks. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on the serum NMR profiles and fatty acid composition (determined by GC/MS) showed a clear discrimination between HFS groups and controls. This discrimination involved metabolites such as glucose, amino acids, pyruvate, creatine, phosphocholine/glycerophosphocholine, ketone bodies and glycoproteins as well as an increase of monounsaturated fatty acids (MUFAs) and a decrease of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Although the administration of Curcuma longa did not prevent the observed increase of glucose, triglycerides, cholesterol and insulin levels, discriminating metabolites were observed between groups fed HFS alone or with addition of a Curcuma longa extract, namely some MUFA and n-3 PUFA, glycoproteins, glutamine, and methanol, suggesting that curcuminoids may act respectively on the fatty acid metabolism, the hexosamine biosynthesis pathway and alcohol oxidation. Curcuma longa extract supplementation appears to be beneficial in these metabolic pathways in rats. This metabolomic approach highlights important serum metabolites that could help in understanding further the metabolic mechanisms leading to IR. PMID:26288372

¹H NMR-based metabolic profiling of human rectal cancer tissue

PubMed Central

2013-01-01

Background Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis. Methods Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the 1H-NMR profiling data to identify the distinguishing metabolites of rectal cancer. Results Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer. Conclusion Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated. PMID:24138801

LIBS data analysis using a predictor-corrector based digital signal processor algorithm

NASA Astrophysics Data System (ADS)

Sanders, Alex; Griffin, Steven T.; Robinson, Aaron

2012-06-01

There are many accepted sensor technologies for generating spectra for material classification. Once the spectra are generated, communication bandwidth limitations favor local material classification with its attendant reduction in data transfer rates and power consumption. Transferring sensor technologies such as Cavity Ring-Down Spectroscopy (CRDS) and Laser Induced Breakdown Spectroscopy (LIBS) require effective material classifiers. A result of recent efforts has been emphasis on Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component Analysis (PCA). Implementation of these via general purpose computers is difficult in small portable sensor configurations. This paper addresses the creation of a low mass, low power, robust hardware spectra classifier for a limited set of predetermined materials in an atmospheric matrix. Crucial to this is the incorporation of PCA or PLS-DA classifiers into a predictor-corrector style implementation. The system configuration guarantees rapid convergence. Software running on multi-core Digital Signal Processor (DSPs) simulates a stream-lined plasma physics model estimator, reducing Analog-to-Digital (ADC) power requirements. This paper presents the results of a predictorcorrector model implemented on a low power multi-core DSP to perform substance classification. This configuration emphasizes the hardware system and software design via a predictor corrector model that simultaneously decreases the sample rate while performing the classification.

Chemical profiling of guarana seeds (Paullinia cupana) from different geographical origins using UPLC-QTOF-MS combined with chemometrics.

PubMed

da Silva, Givaldo Souza; Canuto, Kirley Marques; Ribeiro, Paulo Riceli Vasconcelos; de Brito, Edy Sousa; Nascimento, Madson Moreira; Zocolo, Guilherme Julião; Coutinho, Janclei Pereira; de Jesus, Raildo Mota

2017-12-01

Paullinia cupana, commonly known as guarana, is an Amazonian fruit whose seeds are used to produce the powdered guarana, which is rich in caffeine and consumed for its stimulating activity. The metabolic profile of guarana from the two largest producing regions was investigated using UPLC-MS combined with multivariate statistical analysis. The principal component analysis (PCA) showed significant differences between samples produced in the states of Bahia and Amazonas. The metabolites responsible for the differentiation were identified by orthogonal partial least squares discriminant analysis (OPLS-DA). Fourteen phenolic compounds were characterized in guarana powder samples, and catechin, epicatechin, B-type procyanidin dimer, A-type procyanidin trimer and A-type procyanidin dimer were the main compounds responsible for the geographical variation of the samples. Copyright © 2017. Published by Elsevier Ltd.

Identification of Pulmonary Edema in Forensic Autopsy Cases of Sudden Cardiac Death Using Fourier Transform Infrared Microspectroscopy: A Pilot Study.

PubMed

Lin, Hancheng; Luo, Yiwen; Sun, Qiran; Zhang, Ji; Tuo, Ya; Zhang, Zhong; Wang, Lei; Deng, Kaifei; Chen, Yijiu; Huang, Ping; Wang, Zhenyuan

2018-02-20

Many studies have proven the usefulness of biofluid-based infrared spectroscopy in the clinical domain for diagnosis and monitoring the progression of diseases. Here we present a state-of-the-art study in the forensic field that employed Fourier transform infrared microspectroscopy for postmortem diagnosis of sudden cardiac death (SCD) by in situ biochemical investigation of alveolar edema fluid in lung tissue sections. The results of amide-related spectral absorbance analysis demonstrated that the pulmonary edema fluid of the SCD group was richer in protein components than that of the neurologic catastrophe (NC) and lethal multiple injuries (LMI) groups. The complementary results of unsupervised principle component analysis (PCA) and genetic algorithm-guided partial least-squares discriminant analysis (GA-PLS-DA) further indicated different global spectral band patterns of pulmonary edema fluids between these three groups. Ultimately, a random forest (RF) classification model for postmortem diagnosis of SCD was built and achieved good sensitivity and specificity scores of 97.3% and 95.5%, respectively. Classification predictions of unknown pulmonary edema fluid collected from 16 cases were also performed by the model, resulting in 100% correct discrimination. This pilot study demonstrates that FTIR microspectroscopy in combination with chemometrics has the potential to be an effective aid for postmortem diagnosis of SCD.

Environmental impact of intensive aquaculture: investigation on the accumulation of metals and nutrients in marine sediments of Greece.

PubMed

Farmaki, Eleni G; Thomaidis, Nikolaos S; Pasias, Ioannis N; Baulard, Cecile; Papaharisis, Leonidas; Efstathiou, Constantinos E

2014-07-01

The impact of intensive aquaculture activities on marine sediments along three coastal areas in Greece was studied. The content of nine metals/metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn, As), and three nutrients (P, N and C), that seem to accumulate in marine sediments, was determined under the fish cages (zero distance) and away (50 or 100 m) from them. Elevated concentrations for phosphorus, nitrogen, copper, zinc and cadmium were recorded in the areas where farming establishments are moored. In parallel, the intrinsic differences between the aquaculture facilities and their seasonal variations were investigated. The individual characteristics of each farm (local water currents, facilities' capacity, transferring mechanisms or the geological background) were the determinant factors. On the contrary, significant seasonal differences were not recorded. Statistical techniques, as the non-parametric Mann-Whitney U and Kruskal-Wallis tests and principal components analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were used for the evaluation of the results. These chemometric tools succeeded to discriminate the sampling points according to their distance from the cages or the origin of the sample. Variables' significance, correlations and potential accumulation sources were also investigated. Copyright © 2014 Elsevier B.V. All rights reserved.

Prostate Cancer Patients-Negative Biopsy Controls Discrimination by Untargeted Metabolomics Analysis of Urine by LC-QTOF: Upstream Information on Other Omics

NASA Astrophysics Data System (ADS)

Fernández-Peralbo, M. A.; Gómez-Gómez, E.; Calderón-Santiago, M.; Carrasco-Valiente, J.; Ruiz-García, J.; Requena-Tapia, M. J.; Luque de Castro, M. D.; Priego-Capote, F.

2016-12-01

The existing clinical biomarkers for prostate cancer (PCa) diagnosis are far from ideal (e.g., the prostate specific antigen (PSA) serum level suffers from lack of specificity, providing frequent false positives leading to over-diagnosis). A key step in the search for minimum invasive tests to complement or replace PSA should be supported on the changes experienced by the biochemical pathways in PCa patients as compared to negative biopsy control individuals. In this research a comprehensive global analysis by LC-QTOF was applied to urine from 62 patients with a clinically significant PCa and 42 healthy individuals, both groups confirmed by biopsy. An unpaired t-test (p-value < 0.05) provided 28 significant metabolites tentatively identified in urine, used to develop a partial least squares discriminant analysis (PLS-DA) model characterized by 88.4 and 92.9% of sensitivity and specificity, respectively. Among the 28 significant metabolites 27 were present at lower concentrations in PCa patients than in control individuals, while only one reported higher concentrations in PCa patients. The connection among the biochemical pathways in which they are involved (DNA methylation, epigenetic marks on histones and RNA cap methylation) could explain the concentration changes with PCa and supports, once again, the role of metabolomics in upstream processes.

[NIR Fingerprints of Different Medicinal Parts of Angelicae Sinensis Radix].

PubMed

Zhang, Ya-ya; Gu, Zhi-rong; Ding, Jun-xia; Wang, Yao-peng; Sun, Yu-jing; Wang, Ya-li

2015-07-01

To investigate the spectrum characteristics of near-intrared dittuse retlectance spectroscopy (NIR) fingerprint of different medicinal parts of Angelicae Sinensis Radix. 96 batches of samples were collected from 14 counties of Gansu Province and Yunnan Province. The NIR fingerprints were collected by integrated sphere. Similarity analysis and partial least square discriminant analysis(PLS-DA) were used to analyze the fingerprint. The average spectrum of NIR fingerprint of different medicinal parts of Angelicae Sinensis Radix showed some differences; the absorbance in characteristic absorption was in a decreasing order of body > tail > head > whole. Most NIR fingerprint similarities of different medicinal parts of Angelicae Sinensis Radix exceeded 0. 95. The established model of PLS-DA could be used to accurately classify the medicinal parts of Angelicae Sinensis Radix. The differences of NIR fingerprints of different medicinal parts of Angelicae Sinensis Radix were mainly existing in the wave number ranges of 8,443 - 8,284 cm -1, 7,003 - 6,896 cm-1, 6,102 - 5,864 cm-1, 4,847 - 4,674 cm-1, and 4,386 - 4,208 cm-1. The different medicinal parts of Angelicae Sinensis Radix have some differences in chemical components.

An HR-MAS MR Metabolomics Study on Breast Tissues Obtained with Core Needle Biopsy

PubMed Central

Cho, Nariya; Chang, Jung Min; Koo, Hye Ryoung; Yi, Ann; Kim, Hyeonjin; Park, Sunghyouk; Moon, Woo Kyung

2011-01-01

Background Much research has been devoted to the development of new breast cancer diagnostic measures, including those involving high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopic techniques. Previous HR-MAS MR results have been obtained from post-surgery samples, which limits their direct clinical applicability. Methodology/Principal Findings In the present study, we performed HR-MAS MR spectroscopic studies on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy. We showed that cancer and non-cancer samples can be discriminated very well with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA) multivariate model on the MR spectra. A subsequent blind test showed 69% sensitivity and 94% specificity in the prediction of the cancer status. A spectral analysis showed that in cancer cells, taurine- and choline-containing compounds are elevated. Our approach, additionally, could predict the progesterone receptor statuses of the cancer patients. Conclusions/Significance HR-MAS MR metabolomics on intact breast tissues obtained by core needle biopsy may have a potential to be used as a complement to the current diagnostic and prognostic measures for breast cancers. PMID:22028780

1H NMR-based metabolic profiling reveals the effects of fluoxetine on lipid and amino acid metabolism in astrocytes.

PubMed

Bai, Shunjie; Zhou, Chanjuan; Cheng, Pengfei; Fu, Yuying; Fang, Liang; Huang, Wen; Yu, Jia; Shao, Weihua; Wang, Xinfa; Liu, Meiling; Zhou, Jingjing; Xie, Peng

2015-04-15

Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), is a prescribed and effective antidepressant and generally used for the treatment of depression. Previous studies have revealed that the antidepressant mechanism of fluoxetine was related to astrocytes. However, the therapeutic mechanism underlying its mode of action in astrocytes remains largely unclear. In this study, primary astrocytes were exposed to 10 µM fluoxetine; 24 h post-treatment, a high-resolution proton nuclear magnetic resonance (1H NMR)-based metabolomic approach coupled with multivariate statistical analysis was used to characterize the metabolic variations of intracellular metabolites. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots of the spectra demonstrated that the fluoxetine-treated astrocytes were significantly distinguished from the untreated controls. In total, 17 differential metabolites were identified to discriminate the two groups. These key metabolites were mainly involved in lipids, lipid metabolism-related molecules and amino acids. This is the first study to indicate that fluoxetine may exert antidepressant action by regulating the astrocyte's lipid and amino acid metabolism. These findings should aid our understanding of the biological mechanisms underlying fluoxetine therapy.

1H NMR-Based Metabolic Profiling Reveals the Effects of Fluoxetine on Lipid and Amino Acid Metabolism in Astrocytes

PubMed Central

Bai, Shunjie; Zhou, Chanjuan; Cheng, Pengfei; Fu, Yuying; Fang, Liang; Huang, Wen; Yu, Jia; Shao, Weihua; Wang, Xinfa; Liu, Meiling; Zhou, Jingjing; Xie, Peng

2015-01-01

Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), is a prescribed and effective antidepressant and generally used for the treatment of depression. Previous studies have revealed that the antidepressant mechanism of fluoxetine was related to astrocytes. However, the therapeutic mechanism underlying its mode of action in astrocytes remains largely unclear. In this study, primary astrocytes were exposed to 10 µM fluoxetine; 24 h post-treatment, a high-resolution proton nuclear magnetic resonance (1H NMR)-based metabolomic approach coupled with multivariate statistical analysis was used to characterize the metabolic variations of intracellular metabolites. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots of the spectra demonstrated that the fluoxetine-treated astrocytes were significantly distinguished from the untreated controls. In total, 17 differential metabolites were identified to discriminate the two groups. These key metabolites were mainly involved in lipids, lipid metabolism-related molecules and amino acids. This is the first study to indicate that fluoxetine may exert antidepressant action by regulating the astrocyte’s lipid and amino acid metabolism. These findings should aid our understanding of the biological mechanisms underlying fluoxetine therapy. PMID:25884334

Developmental changes in leaf phenolics composition from three artichoke cvs. (Cynara scolymus) as determined via UHPLC-MS and chemometrics.

PubMed

El Senousy, Amira S; Farag, Mohamed A; Al-Mahdy, Dalia A; Wessjohann, Ludger A

2014-12-01

The metabolomic differences in phenolics from leaves derived from 3 artichoke cultivars (Cynara scolymus): American Green Globe, French Hyrious and Egyptian Baladi, collected at different developmental stages, were assessed using UHPLC-MS coupled to chemometrics. Ontogenic changes were considered as leaves were collected at four different time intervals and positions (top and basal) during artichoke development. Unsupervised principal component analysis (PCA) and supervised orthogonal projection to latent structures-discriminant analysis (O2PLS-DA) were used for comparing and classification of samples harvested from different cultivars at different time points and positions. A clear separation among the three investigated cultivars was revealed, with the American Green Globe samples found most enriched in caffeic acid conjugates and flavonoids vs. other cultivars. Furthermore, these metabolites also showed a marked effect on the discrimination between leaf samples from cultivars harvested at different positions, regardless of the plant age. Metabolite absolute quantifications further confirmed that discrimination was mostly influenced by phenolic compounds, namely caffeoylquinic acids and flavonoids. This study demonstrates an effect of artichoke leaf position, regardless of plant age, on its secondary metabolites composition. To the best of our knowledge, this is the first report for compositional differences among artichoke leaves, based on their positions, via a metabolomic approach and suggesting that top positioned artichoke leaves present a better source of caffeoylquinic acids, compared to basal ones. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification of jet fuel properties by near-infrared spectroscopy using fuzzy rule-building expert systems and support vector machines.

PubMed

Xu, Zhanfeng; Bunker, Christopher E; Harrington, Peter de B

2010-11-01

Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.

A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report.

PubMed

Hafen, G M; Hurst, C; Yearwood, J; Smith, J; Dzalilov, Z; Robinson, P J

2008-10-05

Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. (1) Feature selection: CAP has a more effective "modelling" focus than DA.(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.

Metabolic Profiling of Liver Tissue in Diabetic Mice Treated with Artemisia Capillaris and Alisma Rhizome Using LC-MS and CE-MS.

PubMed

Kim, Yumi; Lee, In-Seung; Kim, Kang-Hoon; Park, Jiyoung; Lee, Ji-Hyun; Bang, Eunjung; Jang, Hyeung-Jin; Na, Yun-Cheol

2016-01-01

Artemisia Capillaris (AC) and Alisma Rhizome (AR) are natural products for the treatment of liver disorders in oriental medicine clinics. Here, we report metabolomic changes in the evaluation of the treatment effects of AC and AR on fatty livers in diabetic mice, along with a proposition of the underlying metabolic pathway. Hydrophobic and hydrophilic metabolites extracted from mouse livers were analyzed using HPLC-QTOF and CE-QTOF, respectively, to generate metabolic profiles. Statistical analysis of the metabolites by PLS-DA and OPLA-DA fairly discriminated between the diabetic, and the AC- and AR-treated mice groups. Various PEs mostly contributed to the discrimination of the diabetic mice from the normal mice, and besides, DG (18:1/16:0), TG (16:1/16:1/20:1), PE (21:0/20:5), and PA (18:0/21:0) were also associated with discrimination by s-plot. Nevertheless, the effects of AC and AR treatment were indistinct with respect to lipid metabolites. Of the 97 polar metabolites extracted from the CE-MS data, 40 compounds related to amino acid, central carbon, lipid, purine, and pyrimidine metabolism, with [Formula: see text] values less than 0.05, were shown to contribute to liver dysregulation. Following treatment with AC and AR, the metabolites belonging to purine metabolism preferentially recovered to the metabolic state of the normal mice. The AMP/ATP ratio of cellular energy homeostasis in AR-treated mice was more apparently increased ([Formula: see text]) than that of AC-treated mice. On the other hand, amino acids, which showed the main alterations in diabetic mice, did not return to the normal levels upon treatment with AR or AC. In terms of metabolomics, AR was a more effective natural product in the treatment of liver dysfunction than AC. These results may provide putative biomarkers for the prognosis of fatty liver disorder following treatment with AC and AR extracts.

In vivo study of dermal collagen of striae distensae by confocal Raman spectroscopy.

PubMed

Lung, Pam Wen; Tippavajhala, Vamshi Krishna; de Oliveira Mendes, Thiago; Téllez-Soto, Claudio A; Schuck, Desirée Cigaran; Brohem, Carla Abdo; Lorencini, Marcio; Martin, Airton Abrahão

2018-04-01

This research work mainly deals with studying qualitatively the changes in the dermal collagen of two forms of striae distensae (SD) namely striae rubrae (SR) and striae albae (SA) when compared to normal skin (NS) using confocal Raman spectroscopy. The methodology includes an in vivo human skin study for the comparison of confocal Raman spectra of dermis region of SR, SA, and NS by supervised multivariate analysis using partial least squares discriminant analysis (PLS-DA) to determine qualitatively the changes in dermal collagen. These groups are further analyzed for the extent of hydration of dermal collagen by studying the changes in the water content bound to it. PLS-DA score plot showed good separation of the confocal Raman spectra of dermis region into SR, SA, and NS data groups. Further analysis using loading plot and S-plot indicated the participation of various components of dermal collagen in the separation of these groups. Bound water content analysis showed that the extent of hydration of collagen is more in SD when compared to NS. Based on the results obtained, this study confirms the active involvement of dermal collagen in the formation of SD. It also emphasizes the need to study quantitatively the role of these various biochemical changes in the dermal collagen responsible for the variance between SR, SA, and NS.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

PubMed

Guo, Hui; Zhang, Zhen; Yao, Yuan; Liu, Jialin; Chang, Ruirui; Liu, Zhao; Hao, Hongyuan; Huang, Taohong; Wen, Jun; Zhou, Tingting

2018-08-30

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods.

PubMed

Yao, Sen; Li, Tao; Liu, HongGao; Li, JieQing; Wang, YuanZhong

2018-04-01

Boletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. Compared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. The results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Diagnosing dry eye with dynamic-area high-speed videokeratoscopy

NASA Astrophysics Data System (ADS)

Alonso-Caneiro, David; Turuwhenua, Jason; Iskander, D. Robert; Collins, Michael J.

2011-07-01

Dry eye syndrome is one of the most commonly reported eye health conditions. Dynamic-area high-speed videokeratoscopy (DA-HSV) represents a promising alternative to the most invasive clinical methods for the assessment of the tear film surface quality (TFSQ), particularly as Placido-disk videokeratoscopy is both relatively inexpensive and widely used for corneal topography assessment. Hence, improving this technique to diagnose dry eye is of clinical significance and the aim of this work. First, a novel ray-tracing model is proposed that simulates the formation of a Placido image. This model shows the relationship between tear film topography changes and the obtained Placido image and serves as a benchmark for the assessment of indicators of the ring's regularity. Further, a novel block-feature TFSQ indicator is proposed for detecting dry eye from a series of DA-HSV measurements. The results of the new indicator evaluated on data from a retrospective clinical study, which contains 22 normal and 12 dry eyes, have shown a substantial improvement of the proposed technique to discriminate dry eye from normal tear film subjects. The best discrimination was obtained under suppressed blinking conditions. In conclusion, this work highlights the potential of the DA-HSV as a clinical tool to diagnose dry eye syndrome.

Phenolic Profiling for Traceability of Vanilla ×tahitensis

PubMed Central

Busconi, Matteo; Lucini, Luigi; Soffritti, Giovanna; Bernardi, Jamila; Bernardo, Letizia; Brunschwig, Christel; Lepers-Andrzejewski, Sandra; Raharivelomanana, Phila; Fernandez, Jose A.

2017-01-01

Vanilla is a flavoring recovered from the cured beans of the orchid genus Vanilla. Vanilla ×tahitensis is traditionally cultivated on the islands of French Polynesia, where vanilla vines were first introduced during the nineteenth century and, since the 1960s, have been introduced to other Pacific countries such as Papua New Guinea (PNG), cultivated and sold as “Tahitian vanilla,” although both sensory properties and aspect are different. From an economic point of view, it is important to ensure V. ×tahitensis traceability and to guarantee that the marketed product is part of the future protected designation of the origin “Tahitian vanilla” (PDO), currently in progress in French Polynesia. The application of metabolomics, allowing the detection and simultaneous analysis of hundreds or thousands of metabolites from different matrices, has recently gained high interest in food traceability. Here, metabolomics analysis of phenolic compounds profiles was successfully applied for the first time to V. ×tahitensis to deepen our knowledge of vanilla metabolome, focusing on phenolics compounds, for traceability purposes. Phenolics were screened through a quadrupole-time-of-flight mass spectrometer coupled to a UHPLC liquid chromatography system, and 260 different compounds were clearly evidenced and subjected to different statistical analysis in order to enable the discrimination of the samples based on their origin. Eighty-eight and twenty three compounds, with a prevalence of flavonoids, resulted to be highly discriminant through ANOVA and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) respectively. Volcano plot analysis and pairwise comparisons were carried out to determine those compounds, mainly responsible for the differences among samples as a consequence of either origin or cultivar. The samples from PNG were clearly different from the Tahitian samples that were further divided in two different groups based on the different phenolic patterns. Among the 260 compounds, metabolomics analysis enabled the detection of previously unreported phenolics in vanilla (such as flavonoids, lignans, stilbenes and other polyphenols). PMID:29075276

Phenolic Profiling for Traceability of Vanilla ×tahitensis.

PubMed

Busconi, Matteo; Lucini, Luigi; Soffritti, Giovanna; Bernardi, Jamila; Bernardo, Letizia; Brunschwig, Christel; Lepers-Andrzejewski, Sandra; Raharivelomanana, Phila; Fernandez, Jose A

2017-01-01

Vanilla is a flavoring recovered from the cured beans of the orchid genus Vanilla . Vanilla × tahitensis is traditionally cultivated on the islands of French Polynesia, where vanilla vines were first introduced during the nineteenth century and, since the 1960s, have been introduced to other Pacific countries such as Papua New Guinea (PNG), cultivated and sold as "Tahitian vanilla," although both sensory properties and aspect are different. From an economic point of view, it is important to ensure V . × tahitensis traceability and to guarantee that the marketed product is part of the future protected designation of the origin "Tahitian vanilla" (PDO), currently in progress in French Polynesia. The application of metabolomics, allowing the detection and simultaneous analysis of hundreds or thousands of metabolites from different matrices, has recently gained high interest in food traceability. Here, metabolomics analysis of phenolic compounds profiles was successfully applied for the first time to V . × tahitensis to deepen our knowledge of vanilla metabolome, focusing on phenolics compounds, for traceability purposes. Phenolics were screened through a quadrupole-time-of-flight mass spectrometer coupled to a UHPLC liquid chromatography system, and 260 different compounds were clearly evidenced and subjected to different statistical analysis in order to enable the discrimination of the samples based on their origin. Eighty-eight and twenty three compounds, with a prevalence of flavonoids, resulted to be highly discriminant through ANOVA and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) respectively. Volcano plot analysis and pairwise comparisons were carried out to determine those compounds, mainly responsible for the differences among samples as a consequence of either origin or cultivar. The samples from PNG were clearly different from the Tahitian samples that were further divided in two different groups based on the different phenolic patterns. Among the 260 compounds, metabolomics analysis enabled the detection of previously unreported phenolics in vanilla (such as flavonoids, lignans, stilbenes and other polyphenols).

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data.

PubMed

Tomazzoli, Maíra M; Pai Neto, Remi D; Moresco, Rodolfo; Westphal, Larissa; Zeggio, Amelia R S; Specht, Leandro; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-12-01

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( λ= 280-400 ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data.

PubMed

Tomazzoli, Maíra Maciel; Pai Neto, Remi Dal; Moresco, Rodolfo; Westphal, Larissa; Zeggio, Amélia Regina Somensi; Specht, Leandro; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-10-21

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis' chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds (λ = 280-400ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Human dopamine receptor nanovesicles for gate-potential modulators in high-performance field-effect transistor biosensors

NASA Astrophysics Data System (ADS)

Park, Seon Joo; Song, Hyun Seok; Kwon, Oh Seok; Chung, Ji Hyun; Lee, Seung Hwan; An, Ji Hyun; Ahn, Sae Ryun; Lee, Ji Eun; Yoon, Hyeonseok; Park, Tai Hyun; Jang, Jyongsik

2014-03-01

The development of molecular detection that allows rapid responses with high sensitivity and selectivity remains challenging. Herein, we demonstrate the strategy of novel bio-nanotechnology to successfully fabricate high-performance dopamine (DA) biosensor using DA Receptor-containing uniform-particle-shaped Nanovesicles-immobilized Carboxylated poly(3,4-ethylenedioxythiophene) (CPEDOT) NTs (DRNCNs). DA molecules are commonly associated with serious diseases, such as Parkinson's and Alzheimer's diseases. For the first time, nanovesicles containing a human DA receptor D1 (hDRD1) were successfully constructed from HEK-293 cells, stably expressing hDRD1. The nanovesicles containing hDRD1 as gate-potential modulator on the conducting polymer (CP) nanomaterial transistors provided high-performance responses to DA molecule owing to their uniform, monodispersive morphologies and outstanding discrimination ability. Specifically, the DRNCNs were integrated into a liquid-ion gated field-effect transistor (FET) system via immobilization and attachment processes, leading to high sensitivity and excellent selectivity toward DA in liquid state. Unprecedentedly, the minimum detectable level (MDL) from the field-induced DA responses was as low as 10 pM in real- time, which is 10 times more sensitive than that of previously reported CP based-DA biosensors. Moreover, the FET-type DRNCN biosensor had a rapid response time (<1 s) and showed excellent selectivity in human serum.

Rapid detection of Listeria monocytogenes in milk using confocal micro-Raman spectroscopy and chemometric analysis.

PubMed

Wang, Junping; Xie, Xinfang; Feng, Jinsong; Chen, Jessica C; Du, Xin-jun; Luo, Jiangzhao; Lu, Xiaonan; Wang, Shuo

2015-07-02

Listeria monocytogenes is a facultatively anaerobic, Gram-positive, rod-shape foodborne bacterium causing invasive infection, listeriosis, in susceptible populations. Rapid and high-throughput detection of this pathogen in dairy products is critical as milk and other dairy products have been implicated as food vehicles in several outbreaks. Here we evaluated confocal micro-Raman spectroscopy (785 nm laser) coupled with chemometric analysis to distinguish six closely related Listeria species, including L. monocytogenes, in both liquid media and milk. Raman spectra of different Listeria species and other bacteria (i.e., Staphylococcus aureus, Salmonella enterica and Escherichia coli) were collected to create two independent databases for detection in media and milk, respectively. Unsupervised chemometric models including principal component analysis and hierarchical cluster analysis were applied to differentiate L. monocytogenes from Listeria and other bacteria. To further evaluate the performance and reliability of unsupervised chemometric analyses, supervised chemometrics were performed, including two discriminant analyses (DA) and soft independent modeling of class analogies (SIMCA). By analyzing Raman spectra via two DA-based chemometric models, average identification accuracies of 97.78% and 98.33% for L. monocytogenes in media, and 95.28% and 96.11% in milk were obtained, respectively. SIMCA analysis also resulted in satisfied average classification accuracies (over 93% in both media and milk). This Raman spectroscopic-based detection of L. monocytogenes in media and milk can be finished within a few hours and requires no extensive sample preparation. Copyright © 2015 Elsevier B.V. All rights reserved.

Analysis of spreadable cheese by Raman spectroscopy and chemometric tools.

PubMed

Oliveira, Kamila de Sá; Callegaro, Layce de Souza; Stephani, Rodrigo; Almeida, Mariana Ramos; de Oliveira, Luiz Fernando Cappa

2016-03-01

In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

PubMed

Lussu, Milena; Camboni, Tania; Piras, Cristina; Serra, Corrado; Del Carratore, Francesco; Griffin, Julian; Atzori, Luigi; Manzin, Aldo

2017-09-21

Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1 H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R 2 Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.

Toward a definition of blueprint of virgin olive oil by comprehensive two-dimensional gas chromatography.

PubMed

Purcaro, Giorgia; Cordero, Chiara; Liberto, Erica; Bicchi, Carlo; Conte, Lanfranco S

2014-03-21

This study investigates the applicability of an iterative approach aimed at defining a chemical blueprint of virgin olive oil volatiles to be correlated to the product sensory quality. The investigation strategy proposed allows to fully exploit the informative content of a comprehensive multidimensional gas chromatography (GC×GC) coupled to a mass spectrometry (MS) data set. Olive oil samples (19), including 5 reference standards, obtained from the International Olive Oil Council, and commercial samples, were submitted to a sensory evaluation by a Panel test, before being analyzed in two laboratories using different instrumentation, column set, and software elaboration packages in view of a cross-validation of the entire methodology. A first classification of samples based on untargeted peak features information, was obtained on raw data from two different column combinations (apolar×polar and polar×apolar) by applying unsupervised multivariate analysis (i.e., principal component analysis-PCA). However, to improve effectiveness and specificity of this classification, peak features were reliably identified (261 compounds), on the basis of the MS spectrum and linear retention index matching, and subjected to successive pair-wise comparisons based on 2D patterns, which revealed peculiar distribution of chemicals correlated with samples sensory classification. The most informative compounds were thus identified and collected in a "blueprint" of specific defects (or combination of defects) successively adopted to discriminate Extra Virgin from defected oils (i.e., lampante oil) with the aid of a supervised approach, i.e., partial least squares-discriminant analysis (PLS-DA). In this last step, the principles of sensomics, which assigns higher information potential to analytes with lower odor threshold proved to be successful, and a much more powerful discrimination of samples was obtained in view of a sensory quality assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterization of human breast cancer tissues by infrared imaging.

PubMed

Verdonck, M; Denayer, A; Delvaux, B; Garaud, S; De Wind, R; Desmedt, C; Sotiriou, C; Willard-Gallo, K; Goormaghtigh, E

2016-01-21

Fourier Transform InfraRed (FTIR) spectroscopy coupled to microscopy (IR imaging) has shown unique advantages in detecting morphological and molecular pathologic alterations in biological tissues. The aim of this study was to evaluate the potential of IR imaging as a diagnostic tool to identify characteristics of breast epithelial cells and the stroma. In this study a total of 19 breast tissue samples were obtained from 13 patients. For 6 of the patients, we also obtained Non-Adjacent Non-Tumor tissue samples. Infrared images were recorded on the main cell/tissue types identified in all breast tissue samples. Unsupervised Principal Component Analyses and supervised Partial Least Square Discriminant Analyses (PLS-DA) were used to discriminate spectra. Leave-one-out cross-validation was used to evaluate the performance of PLS-DA models. Our results show that IR imaging coupled with PLS-DA can efficiently identify the main cell types present in FFPE breast tissue sections, i.e. epithelial cells, lymphocytes, connective tissue, vascular tissue and erythrocytes. A second PLS-DA model could distinguish normal and tumor breast epithelial cells in the breast tissue sections. A patient-specific model reached particularly high sensitivity, specificity and MCC rates. Finally, we showed that the stroma located close or at distance from the tumor exhibits distinct spectral characteristics. In conclusion FTIR imaging combined with computational algorithms could be an accurate, rapid and objective tool to identify/quantify breast epithelial cells and differentiate tumor from normal breast tissue as well as normal from tumor-associated stroma, paving the way to the establishment of a potential complementary tool to ensure safe tumor margins.

The multiple hop test: a discriminative or evaluative instrument for chronic ankle instability?

PubMed

Eechaute, Christophe; Bautmans, Ivan; De Hertogh, Willem; Vaes, Peter

2012-05-01

To determine whether the multiple hop test should be used as an evaluative or a discriminative instrument for chronic ankle instability (CAI). Blinded case-control study. : University research laboratory. Twenty-nine healthy subjects (21 men, 8 women, mean age 21.8 years) and 29 patients with CAI (17 men, 12 women, mean age 24.9 years) were selected. Subjects performed a multiple hop test and hopped on 10 different tape markers while trying to avoid any postural correction. Minimal detectable changes (MDC) of the number of balance errors, the time value, and the visual analog scale (VAS) score (perceived difficulty) were calculated as evaluative measures. For the discriminative properties, a receiver operating characteristic curve was determined and the area under curve (AUC), the sensitivity, specificity, diagnostic accuracy (DA), and likelihood ratios (LR) were calculated whether 1, 2, or 3 outcomes were positive. Based on their MDC, outcomes should, respectively, change by more than 7 errors (41%), 6 seconds (15%), and 27 mm (55%, VAS score) before considering it as a real change. Area under curves were, respectively, 79% (errors), 77% (time value), and 65% (VAS score). The most optimal cutoff point was, respectively, 13.5 errors, 35 seconds, and 32.5 mm. When 2 of 3 outcomes were positive, the sensitivity was 86%, the specificity was 79%, the DA was 83%, the positive LR was 4.2, and the negative LR was 0.17. The multiple hop test seems to be more a discriminative instrument for CAI, and its responsiveness needs to be demonstrated.

Metabolomics Tools for Describing Complex Pesticide Exposure in Pregnant Women in Brittany (France)

PubMed Central

Bonvallot, Nathalie; Tremblay-Franco, Marie; Chevrier, Cécile; Canlet, Cécile; Warembourg, Charline; Cravedi, Jean-Pierre; Cordier, Sylvaine

2013-01-01

Background The use of pesticides and the related environmental contaminations can lead to human exposure to various molecules. In early-life, such exposures could be responsible for adverse developmental effects. However, human health risks associated with exposure to complex mixtures are currently under-explored. Objective This project aims at answering the following questions: What is the influence of exposures to multiple pesticides on the metabolome? What mechanistic pathways could be involved in the metabolic changes observed? Methods Based on the PELAGIE cohort (Brittany, France), 83 pregnant women who provided a urine sample in early pregnancy, were classified in 3 groups according to the surface of land dedicated to agricultural cereal activities in their town of residence. Nuclear magnetic resonance-based metabolomics analyses were performed on urine samples. Partial Least Squares Regression-Discriminant Analysis (PLS-DA) and polytomous regressions were used to separate the urinary metabolic profiles from the 3 exposure groups after adjusting for potential confounders. Results The 3 groups of exposure were correctly separated with a PLS-DA model after implementing an orthogonal signal correction with pareto standardizations (R2 = 90.7% and Q2 = 0.53). After adjusting for maternal age, parity, body mass index and smoking habits, the most statistically significant changes were observed for glycine, threonine, lactate and glycerophosphocholine (upward trend), and for citrate (downward trend). Conclusion This work suggests that an exposure to complex pesticide mixtures induces modifications of metabolic fingerprints. It can be hypothesized from identified discriminating metabolites that the pesticide mixtures could increase oxidative stress and disturb energy metabolism. PMID:23704985

High speed measurement of corn seed viability using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan

2016-03-01

Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.

Menthol smokers: metabolomic profiling and smoking behavior

PubMed Central

Hsu, Ping-Ching; Lan, Renny S.; Brasky, Theodore M.; Marian, Catalin; Cheema, Amrita K.; Ressom, Habtom W.; Loffredo, Christopher A.; Pickworth, Wallace B.; Shields, Peter G.

2016-01-01

Background The use of menthol in cigarettes and marketing is under consideration for regulation by the FDA. However, the effects of menthol on smoking behavior and carcinogen exposure have been inconclusive. We previously reported metabolomic profiling for cigarette smokers, and novelly identified a menthol-glucuronide (MG) as the most significant metabolite directly related to smoking. Here, MG is studied in relation to smoking behavior and metabolomic profiles. Methods A cross-sectional study of 105 smokers who smoked two cigarettes in the laboratory one hour apart. Blood nicotine, MG and exhaled carbon monoxide (CO) boosts were determined (the difference before and after smoking). Spearman's correlation, Chi-Square and ANCOVA adjusted for gender, race and cotinine levels for menthol smokers assessed the relationship of MG boost, smoking behavior, and metabolic profiles. Multivariate metabolite characterization using supervised Partial Least Squares-Discriminant Analysis (PLS-DA) was carried out for the classification of metabolomics profiles. Results MG boost was positively correlated with CO boost, nicotine boost, average puff volume, puff duration, and total smoke exposure. Classification using PLS-DA, MG was the top metabolite discriminating metabolome of menthol vs. non-menthol smokers. Among menthol smokers, forty-two metabolites were significantly correlated with MG boost, which linked to cellular functions such as of cell death, survival, and movement. Conclusion Plasma MG boost is a new smoking behavior biomarker that may provides novel information over self-reported use of menthol cigarettes by integrating different smoking measures for understanding smoking behavior and harm of menthol cigarettes. Impacts These results provide insight into the biological effect of menthol smoking. PMID:27628308

Molecular and Phenotypic Characterization of Staphylococcus epidermidis Isolates from Healthy Conjunctiva and a Comparative Analysis with Isolates from Ocular Infection

PubMed Central

Flores-Páez, Luis A.; Zenteno, Juan C.; Alcántar-Curiel, María D.; Vargas-Mendoza, Carlos F.; Rodríguez-Martínez, Sandra; Cancino-Diaz, Mario E.; Jan-Roblero, Janet; Cancino-Diaz, Juan C.

2015-01-01

Staphylococcus epidermidis is a common commensal of healthy conjunctiva and it can cause endophthalmitis, however its presence in conjunctivitis, keratitis and blepharitis is unknown. Molecular genotyping of S. epidermidis from healthy conjunctiva could provide information about the origin of the strains that infect the eye. In this paper two collections of S. epidermidis were used: one from ocular infection (n = 62), and another from healthy conjunctiva (n = 45). All isolates were genotyped by pulsed field gel electrophoresis (PFGE), multilocus sequence typing (MLST), staphylococcal cassette chromosome mec (SCCmec), detection of the genes icaA, icaD, IS256 and polymorphism type of agr locus. The phenotypic data included biofilm production and antibiotic resistance. The results displayed 61 PFGE types from 107 isolates and they were highly discriminatory. MLST analysis generated a total of 25 STs, of which 11 STs were distributed among the ocular infection isolates and lineage ST2 was the most frequent (48.4%), while 14 STs were present in the healthy conjunctiva isolates and lineage ST5 was the most abundant (24.4%). By means of a principal coordinates analysis (PCoA) and a discriminant analysis (DA) it was found that ocular infection isolates had as discriminant markers agr III or agr II, SCCmec V or SCCmec I, mecA gene, resistance to tobramycin, positive biofilm, and IS256+. In contrast to the healthy conjunctiva isolates, the discriminating markers were agr I, and resistance to chloramphenicol, ciprofloxacin, gatifloxacin and oxacillin. The discriminant biomarkers of ocular infection were examined in healthy conjunctiva isolates, and it was found that 3 healthy conjunctiva isolates [two with ST2 and another with ST9] (3/45, 6.66%) had similar genotypic and phenotypic characteristics to ocular infection isolates, therefore a small population from healthy conjunctiva could cause an ocular infection. These data suggest that the healthy conjunctiva isolates do not, in almost all cases, infect the eye due to their large genotypic and phenotypic difference with the ocular infection isolates. PMID:26275056

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

PubMed

Hicks, Lucy C; Huang, Juzheng; Kumar, Sacheen; Powles, Sam T; Orchard, Timothy R; Hanna, George B; Williams, Horace R T

2015-09-01

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Attenuated total reflectance-FT-IR spectroscopy for gunshot residue analysis: potential for ammunition determination.

PubMed

Bueno, Justin; Sikirzhytski, Vitali; Lednev, Igor K

2013-08-06

The ability to link a suspect to a particular shooting incident is a principal task for many forensic investigators. Here, we attempt to achieve this goal by analysis of gunshot residue (GSR) through the use of attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FT-IR) combined with statistical analysis. The firearm discharge process is analogous to a complex chemical process. Therefore, the products of this process (GSR) will vary based upon numerous factors, including the specific combination of the firearm and ammunition which was discharged. Differentiation of FT-IR data, collected from GSR particles originating from three different firearm-ammunition combinations (0.38 in., 0.40 in., and 9 mm calibers), was achieved using projection to latent structures discriminant analysis (PLS-DA). The technique was cross (leave-one-out), both internally and externally, validated. External validation was achieved via assignment (caliber identification) of unknown FT-IR spectra from unknown GSR particles. The results demonstrate great potential for ATR-FT-IR spectroscopic analysis of GSR for forensic purposes.

Optimization of the quenching method for metabolomics analysis of Lactobacillus bulgaricus.

PubMed

Chen, Ming-ming; Li, Ai-li; Sun, Mao-cheng; Feng, Zhen; Meng, Xiang-chen; Wang, Ying

2014-04-01

This study proposed a quenching protocol for metabolite analysis of Lactobacillus delbrueckii subsp. bulgaricus. Microbial cells were quenched with 60% methanol/water, 80% methanol/glycerol, or 80% methanol/water. The effect of the quenching process was assessed by the optical density (OD)-based method, flow cytometry, and gas chromatography-mass spectrometry (GC-MS). The principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were employed for metabolite identification. The results indicated that quenching with 80% methanol/water solution led to less damage to the L. bulgaricus cells, characterized by the lower relative fraction of prodium iodide (PI)-labeled cells and the higher OD recovery ratio. Through GC-MS analysis, higher levels of intracellular metabolites (including focal glutamic acid, aspartic acid, alanine, and AMP) and a lower leakage rate were detected in the sample quenched with 80% methanol/water compared with the others. In conclusion, we suggested a higher concentration of cold methanol quenching for L. bulgaricus metabolomics due to its decreasing metabolite leakage.

Optimization of the quenching method for metabolomics analysis of Lactobacillus bulgaricus *

PubMed Central

Chen, Ming-ming; Li, Ai-li; Sun, Mao-cheng; Feng, Zhen; Meng, Xiang-chen; Wang, Ying

2014-01-01

This study proposed a quenching protocol for metabolite analysis of Lactobacillus delbrueckii subsp. bulgaricus. Microbial cells were quenched with 60% methanol/water, 80% methanol/glycerol, or 80% methanol/water. The effect of the quenching process was assessed by the optical density (OD)-based method, flow cytometry, and gas chromatography-mass spectrometry (GC-MS). The principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were employed for metabolite identification. The results indicated that quenching with 80% methanol/water solution led to less damage to the L. bulgaricus cells, characterized by the lower relative fraction of prodium iodide (PI)-labeled cells and the higher OD recovery ratio. Through GC-MS analysis, higher levels of intracellular metabolites (including focal glutamic acid, aspartic acid, alanine, and AMP) and a lower leakage rate were detected in the sample quenched with 80% methanol/water compared with the others. In conclusion, we suggested a higher concentration of cold methanol quenching for L. bulgaricus metabolomics due to its decreasing metabolite leakage. PMID:24711354

Essential-Oil Variability in Natural Populations of Pinus mugo Turra from the Julian Alps.

PubMed

Bojović, Srdjan; Jurc, Maja; Ristić, Mihailo; Popović, Zorica; Matić, Rada; Vidaković, Vera; Stefanović, Milena; Jurc, Dušan

2016-02-01

The composition and variability of the terpenes and their derivatives isolated from the needles of a representative pool of 114 adult trees originating from four natural populations of dwarf mountain pine (Pinus mugo Turra) from the Julian Alps were investigated by GC-FID and GC/MS analyses. In total, 54 of the 57 detected essential-oil components were identified. Among the different compound classes present in the essential oils, the chief constituents belonged to the monoterpenes, comprising an average content of 79.67% of the total oil composition (74.80% of monoterpene hydrocarbons and 4.87% of oxygenated monoterpenes). Sesquiterpenes were present in smaller amounts (average content of 19.02%), out of which 16.39% were sesquiterpene hydrocarbons and 2.62% oxygenated sesquiterpenes. The most abundant components in the needle essential oils were the monoterpenes δ-car-3-ene, β-phellandrene, α-pinene, β-myrcene, and β-pinene and the sesquiterpene β-caryophyllene. From the total data set of 57 detected compounds, 40 were selected for principal-component analysis (PCA), discriminant analysis (DA), and cluster analysis (CA). The overlap tendency of the four populations suggested by PCA, was as well observed by DA. CA also demonstrated similarity among the populations, which was the highest between Populations I and II. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

Predicting Bobsled Pushing Ability from Various Combine Testing Events.

PubMed

Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

2018-03-12

The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

A ricin forensic profiling approach based on a complex set of biomarkers.

PubMed

Fredriksson, Sten-Åke; Wunschel, David S; Lindström, Susanne Wiklund; Nilsson, Calle; Wahl, Karen; Åstot, Crister

2018-08-15

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1-PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimizing Decision Support for Tailored Health Behavior Change Applications.

PubMed

Kukafka, Rita; Jeong, In cheol; Finkelstein, Joseph

2015-01-01

The Tailored Lifestyle Change Decision Aid (TLC DA) system was designed to provide support for a person to make an informed choice about which behavior change to work on when multiple unhealthy behaviors are present. TLC DA can be delivered via web, smartphones and tablets. The system collects a significant amount of information that is used to generate tailored messages to consumers to persuade them in certain healthy lifestyles. One limitation is the necessity to collect vast amounts of information from users who manually enter. By identifying an optimal set of self-reported parameters we will be able to minimize the data entry burden of the app users. The study was to identify primary determinants of health behavior choices made by patients after using the system. Using discriminant analysis an optimal set of predictors was identified. The resulting set included smoking status, smoking cessation success estimate, self-efficacy, body mass index and diet status. Predicting smoking cessation choice was the most accurate, followed by weight management. Physical activity and diet choices were better identified in a combined cluster.

What are the cultural effects on consumers' perceptions? A case study covering coalho cheese in the Brazilian northeast and southeast area using word association.

PubMed

Soares, Eveline K B; Esmerino, Erick A; Ferreira, Marcus Vinícius S; da Silva, Maria Aparecida A P; Freitas, Mônica Q; Cruz, Adriano G

2017-12-01

The aim of this study was to investigate the effects of regional diversity aspects related to consumers' perceptions of coalho cheese, with Brazilian Northeast and Southeast consumers (n=400, divided equally in each area) using Word Association (WA) task. Different perceptions were detected for both Northeast and Southeast consumers, and among 17 categories elicited for describing coalho cheese, only 7 categories (positive feeling, social aspects, sensory characteristic, dairy product technology, negative feeling, and lack of quality standard) presented significant differences in the frequency of mention according to chi-square per cell approach. The application of the discriminant technique Partial Least Square Discriminant Analysis (PLS-DA) indicated that the categories "Social", "Accompaniment", "Manufacturing method" were the main responsible for differentiating consumers' perceptions of both areas. Overall, the main dimensions involved in the consumers' perceptions of coalho cheese from different Brazilian regions were identified, thus obtaining comprehensive insights that can be used as a guideline for coalho cheese producers to develop marketing strategies considering the intra-cultural differences. Copyright © 2017. Published by Elsevier Ltd.

GC-MS Profiling of Volatile Components in Different Fermentation Products of Cordyceps Sinensis Mycelia.

PubMed

Zhang, Hongyang; Li, Yahui; Mi, Jianing; Zhang, Min; Wang, Yuerong; Jiang, Zhihong; Hu, Ping

2017-10-24

The fermentation products of Cordyceps sinensis ( C. sinensis ) mycelia are sustainable substitutes for natural C. sinensis . However, the volatile compositions of the commercial products are still unclear. In this paper, we have developed a simultaneous distillation-extraction (SDE) and gas chromatography-mass spectrometry (GC-MS) method for the profiling of volatile components in five fermentation products. A total of 64, 39, 56, 52, and 44 components were identified in the essential oils of Jinshuibao capsule (JSBC), Bailing capsule (BLC), Zhiling capsule (ZLC), Ningxinbao capsule (NXBC), and Xinganbao capsule (XGBC), respectively. 5,6-Dihydro-6-pentyl-2H-pyran-2-one (massoia lactone) was first discovered as the dominant component in JSBC volatiles. Fatty acids including palmitic acid (C16:0) and linoleic acid (C18:2) were also found to be major volatile compositions of the fermentation products. The multivariate partial least squares-discriminant analysis (PLS-DA) showed a clear discrimination among the different commercial products as well as the counterfeits. This study may provide further chemical evidences for the quality evaluation of the fermentation products of C. sinensis mycelia.

Seasonal discrimination of C3 and C4 grasses functional types: An evaluation of the prospects of varying spectral configurations of new generation sensors

NASA Astrophysics Data System (ADS)

Shoko, Cletah; Mutanga, Onisimo

2017-10-01

The present study assessed the potential of varying spectral configuration of Landsat 8 Operational Land Imager (OLI), Sentinel 2 MultiSpectal Instrument (MSI) and Worldview 2 sensors in the seasonal discrimination of Festuca costata (C3) and Themeda Triandra (C4) grass species in the Drakensberg, South Africa. This was achieved by resampling hyperspectral measurements to the spectral windows corresponding to the three sensors at two distinct seasonal periods (summer peak and end of winter), using the Discriminant Analysis (DA) classification ensemble. In summer, standard bands of the Worldview 2 produced the highest overall classification accuracy (98.61%), followed by Sentinel 2 (97.52%), whereas the Landsat 8 spectral configuration was the least performer, using vegetation indices (95.83%). In winter, Sentinel 2 spectral bands produced the highest accuracy (96.18%) for the two species, followed by Worldview 2 (94.44%) and Landsat 8 yielded the least (91.67%) accuracy. Results also showed that maximum separability between C3 and C4 grasses was in summer, while at the end of winter considerable overlaps were noted, especially when using the spectral settings of the Landsat 8 OLI and Sentinel 2 shortwave infrared bands. Test of significance in species reflectance further confirmed that in summer, there were significant differences (P < 0.05), whereas in winter, most of the spectral windows of all sensors yielded insignificant differences (P > 0.05) between the two species. In this regard, the peak summer period presents a promising opportunity for the spectral discrimination of C3 and C4 grass species functional types, than the end of winter, when using multispectral sensors. Results from this study highlight the influence of seasonality on discrimination and therefore provide the basis for the successful discrimination and mapping of C3 and C4 grass species.

Analysis of the polymeric fractions of scrap from mobile phones using laser-induced breakdown spectroscopy: chemometric applications for better data interpretation.

PubMed

Aquino, Francisco W B; Pereira-Filho, Edenir R

2015-03-01

Because of their short life span and high production and consumption rates, mobile phones are one of the contributors to WEEE (waste electrical and electronic equipment) growth in many countries. If incorrectly managed, the hazardous materials used in the assembly of these devices can pollute the environment and pose dangers for workers involved in the recycling of these materials. In this study, 144 polymer fragments originating from 50 broken or obsolete mobile phones were analyzed via laser-induced breakdown spectroscopy (LIBS) without previous treatment. The coated polymers were mainly characterized by the presence of Ag, whereas the uncoated polymers were related to the presence of Al, K, Na, Si and Ti. Classification models were proposed using black and white polymers separately in order to identify the manufacturer and origin using KNN (K-nearest neighbor), SIMCA (Soft Independent Modeling of Class Analogy) and PLS-DA (Partial Least Squares for Discriminant Analysis). For the black polymers the percentage of correct predictions was, in average, 58% taking into consideration the models for manufacturer and origin identification. In the case of white polymers, the percentage of correct predictions ranged from 72.8% (PLS-DA) to 100% (KNN). Copyright © 2014 Elsevier B.V. All rights reserved.

Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis.

PubMed

Xia, Jianguo; Wishart, David S

2016-09-07

MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Peng, Jiyu; Song, Kunlin; Zhu, Hongyan; Kong, Wenwen; Liu, Fei; Shen, Tingting; He, Yong

2017-03-01

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm.

PubMed

Luck, Margaux; Schmitt, Caroline; Talbi, Neila; Gouya, Laurent; Caradeuc, Cédric; Puy, Hervé; Bertho, Gildas; Pallet, Nicolas

2018-01-01

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease. In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses. Urine 1 H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained. Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients. These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

Age determination of bottled Chinese rice wine by VIS-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Yu, Haiyan; Lin, Tao; Ying, Yibin; Pan, Xingxiang

2006-10-01

The feasibility of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining wine age (1, 2, 3, 4, and 5 years) of Chinese rice wine was investigated. Samples of Chinese rice wine were analyzed in 600 mL square brown glass bottles with side length of approximately 64 mm at room temperature. VIS-NIR spectra of 100 bottled Chinese rice wine samples were collected in transmission mode in the wavelength range of 350-1200 nm by a fiber spectrometer system. Discriminant models were developed based on discriminant analysis (DA) together with raw, first and second derivative spectra. The concentration of alcoholic degree, total acid, and °Brix was determined to validate the NIR results. The calibration result for raw spectra was better than that for first and second derivative spectra. The percentage of samples correctly classified for raw spectra was 98%. For 1-, 2-, and 3-year-old sample groups, the sample were all correctly classified, and for 4- and 5-year-old sample groups, the percentage of samples correctly classified was 92.9%, respectively. In validation analysis, the percentage of samples correctly classified was 100%. The results demonstrated that VIS-NIR spectroscopic technique could be used as a non-invasive, rapid and reliable method for predicting wine age of bottled Chinese rice wine.

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

PubMed Central

Peng, Jiyu; Song, Kunlin; Zhu, Hongyan; Kong, Wenwen; Liu, Fei; Shen, Tingting; He, Yong

2017-01-01

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves. PMID:28300144

Secondary metabolite profiling of Curcuma species grown at different locations using GC/TOF and UPLC/Q-TOF MS.

PubMed

Lee, Jueun; Jung, Youngae; Shin, Jeoung-Hwa; Kim, Ho Kyoung; Moon, Byeong Cheol; Ryu, Do Hyun; Hwang, Geum-Sook

2014-07-04

Curcuma, a genus of rhizomatous herbaceous species, has been used as a spice, traditional medicine, and natural dye. In this study, the metabolite profile of Curcuma extracts was determined using gas chromatography-time of flight mass spectrometry (GC/TOF MS) and ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) to characterize differences between Curcuma aromatica and Curcuma longa grown on the Jeju-do or Jin-do islands, South Korea. Previous studies have performed primary metabolite profiling of Curcuma species grown in different regions using NMR-based metabolomics. This study focused on profiling of secondary metabolites from the hexane extract of Curcuma species. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) plots showed significant differences between the C. aromatica and C. longa metabolite profiles, whereas geographical location had little effect. A t-test was performed to identify statistically significant metabolites, such as terpenoids. Additionally, targeted profiling using UPLC/Q-TOF MS showed that the concentration of curcuminoids differed depending on the plant origin. Based on these results, a combination of GC- and LC-MS allowed us to analyze curcuminoids and terpenoids, the typical bioactive compounds of Curcuma, which can be used to discriminate Curcuma samples according to species or geographical origin.

UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption.

PubMed

Liu, Haiyan; Garrett, Timothy J; Su, Zhihua; Khoo, Christina; Gu, Liwei

2017-07-01

Plasma metabolome in young women following cranberry juice consumption were investigated using a global UHPLC-Q-Orbitrap-HRMS approach. Seventeen female college students, between 21 and 29 years old, were given either cranberry juice or apple juice for three days using a cross-over design. Plasma samples were collected before and after juice consumption. Plasma metabolomes were analyzed using UHPLC-Q-Orbitrap-HRMS followed by orthogonal partial least squares-discriminant analyses (OPLS-DA). S-plot was used to identify discriminant metabolites. Validated OPLS-DA analyses showed that the plasma metabolome in young women, including both exogenous and endogenous metabolites, were altered following cranberry juice consumption. Cranberry juice caused increases of exogenous metabolites including quinic acid, vanilloloside, catechol sulfate, 3,4-dihydroxyphenyl ethanol sulfate, coumaric acid sulfate, ferulic acid sulfate, 5-(trihydroxphenyl)-gamma-valerolactone, 3-(hydroxyphenyl)proponic acid, hydroxyphenylacetic acid and trihydroxybenzoic acid. In addition, the plasma levels of endogenous metabolites including citramalic acid, aconitic acid, hydroxyoctadecanoic acid, hippuric acid, 2-hydroxyhippuric acid, vanilloylglycine, 4-acetamido-2-aminobutanoic acid, dihydroxyquinoline, and glycerol 3-phosphate were increased in women following cranberry juice consumption. The metabolic differences and discriminant metabolites observed in this study may serve as biomarkers of cranberry juice consumption and explain its health promoting properties in human. Copyright © 2017 Elsevier Inc. All rights reserved.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Investigating sub-2 μm particle stationary phase supercritical fluid chromatography coupled to mass spectrometry for chemical profiling of chamomile extracts.

PubMed

Jones, Michael D; Avula, Bharathi; Wang, Yan-Hong; Lu, Lu; Zhao, Jianping; Avonto, Cristina; Isaac, Giorgis; Meeker, Larry; Yu, Kate; Legido-Quigley, Cristina; Smith, Norman; Khan, Ikhlas A

2014-10-17

Roman and German chamomile are widely used throughout the world. Chamomiles contain a wide variety of active constituents including sesquiterpene lactones. Various extraction techniques were performed on these two types of chamomile. A packed-column supercritical fluid chromatography-mass spectrometry method was designed for the identification of sesquiterpenes and other constituents from chamomile extracts with no derivatization step prior to analysis. Mass spectrometry detection was achieved by using electrospray ionization. All of the compounds of interest were separated within 15 min. The chamomile extracts were analyzed and compared for similarities and distinct differences. Multivariate statistical analysis including principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to differentiate between the chamomile samples. German chamomile samples confirmed the presence of cis- and trans-tonghaosu, chrysosplenols, apigenin diglucoside whereas Roman chamomile samples confirmed the presence of apigenin, nobilin, 1,10-epioxynobilin, and hydroxyisonobilin. Copyright © 2014 Elsevier B.V. All rights reserved.

Metabolic fingerprinting of Leontopodium species (Asteraceae) by means of 1H NMR and HPLC–ESI-MS

PubMed Central

Safer, Stefan; Cicek, Serhat S.; Pieri, Valerio; Schwaiger, Stefan; Schneider, Peter; Wissemann, Volker; Stuppner, Hermann

2011-01-01

The genus Leontopodium, mainly distributed in Central and Eastern Asia, consists of ca. 34–58 different species. The European Leontopodium alpinum, commonly known as Edelweiss, has a long tradition in folk medicine. Recent research has resulted in the identification of prior unknown secondary metabolites, some of them with interesting biological activities. Despite this, nearly nothing is known about the Asian species of the genus. In this study, we applied proton nuclear magnetic resonance (1H NMR) spectroscopy and liquid chromatography–mass spectrometry (LC–MS) metabolic fingerprinting to reveal insights into the metabolic patterns of 11 different Leontopodium species, and to conclude on their taxonomic relationship. Principal component analysis (PCA) of 1H NMR fingerprints revealed two species groups. Discriminators for these groups were identified as fatty acids and sucrose for group A, and ent-kaurenoic acid and derivatives thereof for group B. Five diterpenes together with one sesquiterpene were isolated from Leontopodium franchetii roots; the compounds were described for the first time for L. franchetii: ent-kaur-16-en-19-oic acid, methyl-15α-angeloyloxy-ent-kaur-16-en-19-oate, methyl-ent-kaur-16-en-19-oate, 8-acetoxymodhephene, 19-acetoxy-ent-kaur-16-ene, methyl-15β–angeloyloxy-16,17-epoxy-ent-kauran-19-oate. In addition, differences in the metabolic profile between collected and cultivated species could be observed using a partial least squares-discriminant analysis (PLS-DA). PCA of the LC–MS fingerprints revealed three groups. Discriminating signals were compared to literature data and identified as two bisabolane derivatives responsible for discrimination of group A and C, and one ent-kaurenoic acid derivative, discriminating group B. A taxonomic relationship between a previously unidentified species and L. franchetii and Leontopodium sinense could be determined by comparing NMR fingerprints. This finding supports recent molecular data. Furthermore, Leontopodium dedekensii and L. sinense, two closely related species in terms of morphology and DNA-fingerprints, could be distinguished clearly using 1H NMR and LC–MS metabolic fingerprinting. PMID:21550615

Discriminative stimulus effects of cocaine and amphetamine in rats following developmental exposure to polychlorinated biphenyls (PCBs)

PubMed Central

Sable, Helen J. K.; Monaikul, Supida; Poon, Emily; Eubig, Paul A.; Schantz, Susan L.

2010-01-01

Polychlorinated biphenyls (PCBs) are environmental neurotoxicants known to affect the brain dopaminergic (DA) system. This project investigated whether developmental exposure to PCBs would alter the discriminative stimulus effects of psychostimulant drugs known to act on the DA system. Female Long-Evans rats were orally exposed to 0, 3, or 6 mg/kg/day of an environmentally relevant PCB mixture from four weeks prior to breeding through weaning of their litters on PND 21. When they reached adulthood one male and female/litter were trained to discriminate cocaine (10.0 mg/kg, IP) from saline by repeatedly pairing cocaine injections with reinforcement on one operant response lever, and saline injections with reinforcement on the other lever. After response training, generalization tests to four lower doses of cocaine (7.5, 5.0, 2.5, and 1.25 mg/kg, IP) and to amphetamine (1.0, 0.5, 0.25, and 0.125 mg/kg, IP) were given two days/week, with additional training dose days in-between. Percent responding of the PCB-exposed rats on the cocaine-paired lever was significantly higher than that of controls for the highest generalization dose of cocaine, and lower than that of controls for the highest dose of amphetamine. Response rate and percent responding on the cocaine lever did not differ among the exposure groups on the days when the training dose of cocaine was given, suggesting the generalization test results were not due to pre-existing differences in discrimination ability or rate of responding. These findings suggest developmental PCB exposure can alter the interoceptive cues of psychostimulants. PMID:20933596

Serum Proteome Analysis for Profiling Predictive Protein Markers Associated with the Severity of Skin Lesions Induced by Ionizing Radiation.

PubMed

Chaze, Thibault; Hornez, Louis; Chambon, Christophe; Haddad, Iman; Vinh, Joelle; Peyrat, Jean-Philippe; Benderitter, Marc; Guipaud, Olivier

2013-07-10

The finding of new diagnostic and prognostic markers of local radiation injury, and particularly of the cutaneous radiation syndrome, is crucial for its medical management, in the case of both accidental exposure and radiotherapy side effects. Especially, a fast high-throughput method is still needed for triage of people accidentally exposed to ionizing radiation. In this study, we investigated the impact of localized irradiation of the skin on the early alteration of the serum proteome of mice in an effort to discover markers associated with the exposure and severity of impending damage. Using two different large-scale quantitative proteomic approaches, 2D-DIGE-MS and SELDI-TOF-MS, we performed global analyses of serum proteins collected in the clinical latency phase (days 3 and 7) from non-irradiated and locally irradiated mice exposed to high doses of 20, 40 and 80 Gy which will develop respectively erythema, moist desquamation and necrosis. Unsupervised and supervised multivariate statistical analyses (principal component analysis, partial-least square discriminant analysis and Random Forest analysis) using 2D-DIGE quantitative protein data allowed us to discriminate early between non-irradiated and irradiated animals, and between uninjured/slightly injured animals and animals that will develop severe lesions. On the other hand, despite a high number of animal replicates, PLS-DA and Random Forest analyses of SELDI-TOF-MS data failed to reveal sets of MS peaks able to discriminate between the different groups of animals. Our results show that, unlike SELDI-TOF-MS, the 2D-DIGE approach remains a powerful and promising method for the discovery of sets of proteins that could be used for the development of clinical tests for triage and the prognosis of the severity of radiation-induced skin lesions. We propose a list of 15 proteins which constitutes a set of candidate proteins for triage and prognosis of skin lesion outcomes.

Serum Proteome Analysis for Profiling Predictive Protein Markers Associated with the Severity of Skin Lesions Induced by Ionizing Radiation

PubMed Central

Chaze, Thibault; Hornez, Louis; Chambon, Christophe; Haddad, Iman; Vinh, Joelle; Peyrat, Jean-Philippe; Benderitter, Marc; Guipaud, Olivier

2013-01-01

The finding of new diagnostic and prognostic markers of local radiation injury, and particularly of the cutaneous radiation syndrome, is crucial for its medical management, in the case of both accidental exposure and radiotherapy side effects. Especially, a fast high-throughput method is still needed for triage of people accidentally exposed to ionizing radiation. In this study, we investigated the impact of localized irradiation of the skin on the early alteration of the serum proteome of mice in an effort to discover markers associated with the exposure and severity of impending damage. Using two different large-scale quantitative proteomic approaches, 2D-DIGE-MS and SELDI-TOF-MS, we performed global analyses of serum proteins collected in the clinical latency phase (days 3 and 7) from non-irradiated and locally irradiated mice exposed to high doses of 20, 40 and 80 Gy which will develop respectively erythema, moist desquamation and necrosis. Unsupervised and supervised multivariate statistical analyses (principal component analysis, partial-least square discriminant analysis and Random Forest analysis) using 2D-DIGE quantitative protein data allowed us to discriminate early between non-irradiated and irradiated animals, and between uninjured/slightly injured animals and animals that will develop severe lesions. On the other hand, despite a high number of animal replicates, PLS-DA and Random Forest analyses of SELDI-TOF-MS data failed to reveal sets of MS peaks able to discriminate between the different groups of animals. Our results show that, unlike SELDI-TOF-MS, the 2D-DIGE approach remains a powerful and promising method for the discovery of sets of proteins that could be used for the development of clinical tests for triage and the prognosis of the severity of radiation-induced skin lesions. We propose a list of 15 proteins which constitutes a set of candidate proteins for triage and prognosis of skin lesion outcomes. PMID:28250398

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

Discrimination Between Peptide O-Sulfo- and O-Phosphotyrosine Residues by Negative Ion Mode Electrospray Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Edelson-Averbukh, Marina; Shevchenko, Andrej; Pipkorn, Rüdiger; Lehmann, Wolf D.

2011-12-01

Unambiguous differentiation between isobaric sulfated and phosphorylated tyrosine residues (sTyr and pTyr) of proteins by mass spectrometry is challenging, even using high resolution mass spectrometers. Here we show that upon negative ion mode collision-induced dissociation (CID), pTyr- and sTyr-containing peptides exhibit entirely different modification-specific fragmentation patterns leading to a rapid discrimination between the isobaric covalent modifications using the tandem mass spectral data. This study reveals that the ratio between the relative abundances of [M-H-80]- and [M-H-98]- fragment ions in ion-trap CID and higher energy collision dissociation (HCD) spectra of singly deprotonated +80 Da Tyr-peptides can be used as a reliable indication of the Tyr modification group nature. For multiply deprotonated +80 Da Tyr-peptides, CID spectra of sTyr- and pTyr-containing sequences can be readily distinguished based on the presence/absence of the [M-nH-79](n-1)- and [M-nH-79-NL]( n-1)- ( n = 2, 3) fragment ions (NL = neutral loss).

Metabolomic analysis of primary metabolites in citrus leaf during defense responses.

PubMed

Asai, Tomonori; Matsukawa, Tetsuya; Kajiyama, Shin'ichiro

2017-03-01

Mechanical damage is one of the unavoidable environmental stresses to plant growth and development. Plants induce a variety of reactions which defend against natural enemies and/or heal the wounded sites. Jasmonic acid (JA) and salicylic acid (SA), defense-related plant hormones, are well known to be involved in induction of defense reactions and play important roles as signal molecules. However, defense related metabolites are so numerous and diverse that roles of individual compounds are still to be elucidated. In this report, we carried out a comprehensive analysis of metabolic changes during wound response in citrus plants which are one of the most commercially important fruit tree families. Changes in amino acid, sugar, and organic acid profiles in leaves were surveyed after wounding, JA and SA treatments using gas chromatography-mass spectrometry (GC/MS) in seven citrus species, Citrus sinensis, Citrus limon, Citrus paradisi, Citrus unshiu, Citrus kinokuni, Citrus grandis, and Citrus hassaku. GC/MS data were applied to multivariate analyses including hierarchical cluster analysis (HCA), primary component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) to extract stress-related compounds. HCA showed the amino acid cluster including phenylalanine and tryptophan, suggesting that amino acids in this cluster are concertedly regulated during responses against treatments. OPLS-DA exhibited that tryptophan was accumulated after wounding and JA treatments in all species tested, while serine was down regulated. Our results suggest that tryptophan and serine are common biomarker candidates in citrus plants for wound stress. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Measuring the quality of life in hypertension according to Item Response Theory.

PubMed

Borges, José Wicto Pereira; Moreira, Thereza Maria Magalhães; Schmitt, Jeovani; Andrade, Dalton Francisco de; Barbetta, Pedro Alberto; Souza, Ana Célia Caetano de; Lima, Daniele Braz da Silva; Carvalho, Irialda Saboia

2017-05-04

To analyze the Miniquestionário de Qualidade de Vida em Hipertensão Arterial (MINICHAL - Mini-questionnaire of Quality of Life in Hypertension) using the Item Response Theory. This is an analytical study conducted with 712 persons with hypertension treated in thirteen primary health care units of Fortaleza, State of Ceará, Brazil, in 2015. The steps of the analysis by the Item Response Theory were: evaluation of dimensionality, estimation of parameters of items, and construction of scale. The study of dimensionality was carried out on the polychoric correlation matrix and confirmatory factor analysis. To estimate the item parameters, we used the Gradual Response Model of Samejima. The analyses were conducted using the free software R with the aid of psych and mirt. The analysis has allowed the visualization of item parameters and their individual contributions in the measurement of the latent trait, generating more information and allowing the construction of a scale with an interpretative model that demonstrates the evolution of the worsening of the quality of life in five levels. Regarding the item parameters, the items related to the somatic state have had a good performance, as they have presented better power to discriminate individuals with worse quality of life. The items related to mental state have been those which contributed with less psychometric data in the MINICHAL. We conclude that the instrument is suitable for the identification of the worsening of the quality of life in hypertension. The analysis of the MINICHAL using the Item Response Theory has allowed us to identify new sides of this instrument that have not yet been addressed in previous studies. Analisar o Miniquestionário de Qualidade de Vida em Hipertensão Arterial (MINICHAL) por meio da Teoria da Resposta ao Item. Estudo analítico realizado com 712 pessoas com hipertensão arterial atendidas em 13 unidades de atenção primária em saúde de Fortaleza, CE, em 2015. As etapas da análise pela Teoria da Resposta ao Item foram: avaliação da dimensionalidade, estimação dos parâmetros dos itens e construção da escala. O estudo da dimensionalidade foi realizado sobre a matriz de correlação policórica e análise fatorial confirmatória. Para a estimação dos parâmetros dos itens, foi utilizado o Modelo de Resposta Gradual de Samejima. As análises foram conduzidas no software livre R com o auxílio dos pacotes psych e mirt. A análise permitiu a visualização dos parâmetros dos itens e suas contribuições individuais na mensuração do traço latente, gerando mais informação, permitindo a construção de uma escala com um modelo interpretativo que demonstra a evolução da piora da qualidade de vida em cinco níveis. Quanto aos parâmetros dos itens, houve bom desempenho daqueles referentes ao estado somático, pois apresentaram melhor poder de discriminar os indivíduos com pior qualidade de vida. Os itens relacionados ao estado mental foram os que contribuíram com menor quantidade de informação psicométrica no MINICHAL. Conclui-se que o instrumento é indicado para a identificação da deterioração da qualidade de vida em hipertensão arterial. A análise do MINICHAL pela Teoria da Resposta ao Item permitiu identificar novas facetas desse instrumento ainda não abordadas em estudos anteriores.

Biological variation of Vanilla planifolia leaf metabolome.

PubMed

Palama, Tony Lionel; Fock, Isabelle; Choi, Young Hae; Verpoorte, Robert; Kodja, Hippolyte

2010-04-01

The metabolomic analysis of Vanilla planifolia leaves collected at different developmental stages was carried out using (1)H-nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis in order to evaluate their variation. Ontogenic changes of the metabolome were considered since leaves of different ages were collected at two different times of the day and in two different seasons. Principal component analysis (PCA) and partial least square modeling discriminate analysis (PLS-DA) of (1)H NMR data provided a clear separation according to leaf age, time of the day and season of collection. Young leaves were found to have higher levels of glucose, bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A) and bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-(2-butyl)-tartrate (glucoside B), whereas older leaves had more sucrose, acetic acid, homocitric acid and malic acid. Results obtained from PLS-DA analysis showed that leaves collected in March 2008 had higher levels of glucosides A and B as compared to those collected in August 2007. However, the relative standard deviation (RSD) exhibited by the individual values of glucosides A and B showed that those compounds vary more according to their developmental stage (50%) than to the time of day or the season in which they were collected (19%). Although morphological variations of the V. planifolia accessions were observed, no clear separation of the accessions was determined from the analysis of the NMR spectra. The results obtained in this study, show that this method based on the use of (1)H NMR spectroscopy in combination with multivariate analysis has a great potential for further applications in the study of vanilla leaf metabolome. Copyright 2009 Elsevier Ltd. All rights reserved.

Protein biomarkers in vernix with potential to predict the development of atopic eczema in early childhood

PubMed Central

Holm, T; Rutishauser, D; Kai-Larsen, Y; Lyutvinskiy, Y; Stenius, F; Zubarev, R A; Agerberth, B; Alm, J; Scheynius, A

2014-01-01

Background Atopic eczema (AE) is a chronic inflammatory skin disease, which has increased in prevalence. Evidence points toward lifestyle as a major risk factor. AE is often the first symptom early in life later followed by food allergy, asthma, and allergic rhinitis. Thus, there is a great need to find early, preferentially noninvasive, biomarkers to identify individuals that are predisposed to AE with the goal to prevent disease development. Objective To investigate whether the protein abundances in vernix can predict later development of AE. Methods Vernix collected at birth from 34 newborns within the Assessment of Lifestyle and Allergic Disease During INfancy (ALADDIN) birth cohort was included in the study. At 2 years of age, 18 children had developed AE. Vernix proteins were identified and quantified with liquid chromatography coupled to tandem mass spectrometry. Results We identified and quantified 203 proteins in all vernix samples. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was found with R2 = 0.85, Q2 = 0.39, and discrimination power between the AE and healthy group of 73.5%. Polyubiquitin-C and calmodulin-like protein 5 showed strong negative correlation to the AE group, with a correlation coefficient of 0.73 and 0.68, respectively, and a P-value of 8.2 E-7 and 1.8 E-5, respectively. For these two proteins, the OPLS-DA model showed a prediction accuracy of 91.2%. Conclusion The protein abundances in vernix, and particularly that of polyubiquitin-C and calmodulin-like protein 5, are promising candidates as biomarkers for the identification of newborns predisposed to develop AE. PMID:24205894

Characteristic glycopeptides associated with extreme human longevity identified through plasma glycoproteomics.

PubMed

Miura, Yuri; Hashii, Noritaka; Ohta, Yuki; Itakura, Yoko; Tsumoto, Hiroki; Suzuki, Junya; Takakura, Daisuke; Abe, Yukiko; Arai, Yasumichi; Toyoda, Masashi; Kawasaki, Nana; Hirose, Nobuyoshi; Endo, Tamao

2018-06-01

Glycosylation is highly susceptible to changes of the physiological conditions, and accordingly, is a potential biomarker associated with several diseases and/or longevity. Semi-supercentenarians (SSCs; older than 105 years) are thought to be a model of human longevity. Thus, we performed glycoproteomics using plasma samples of SSCs, and identified proteins and conjugated N-glycans that are characteristic of extreme human longevity. Plasma proteins from Japanese semi-supercentenarians (SSCs, 106-109 years), aged controls (70-88 years), and young controls (20-38 years) were analysed by using lectin microarrays and liquid chromatography/mass spectrometry (LC/MS). Peak area ratios of glycopeptides to corresponding normalising peptides were subjected to orthogonal projections to latent structures discriminant analysis (OPLS-DA). Furthermore, plasma levels of clinical biomarkers were measured. We found two lectins such as Phaseolus vulgaris, and Erythrina cristagalli (ECA), of which protein binding were characteristically increased in SSCs. Peak area ratios of ECA-enriched glycopeptides were successfully discriminated between SSCs and controls using OPLS-DA, and indicated that tri-antennary and sialylated N-glycans of haptoglobin at Asn207 and Asn211 sites were characterized in SSCs. Sialylated glycans of haptoglobin are a potential biomarker of several diseases, such as hepatocellular carcinoma, liver cirrhosis, and IgA-nephritis. However, the SSCs analysed here did not suffer from these diseases. Tri-antennary and sialylated N-glycans on haptoglobin at the Asn207 and Asn211 sites were abundant in SSCs and characteristic of extreme human longevity. We found abundant glycans in SSCs, which may be associated with human longevity. Copyright © 2018 Elsevier B.V. All rights reserved.

Integrated fingerprint and high wavenumber confocal Raman spectroscopy for in vivo diagnosis of cervical precancer

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J. H.; Ilancheran, A.; Huang, Zhiwei

2013-03-01

Raman spectroscopy is a vibrational spectroscopic technique capable of optically probing the compositional, conformational, and structural changes in the tissue associated with disease progression. The main goal of this work is to develop an integrated fingerprint (FP) and high wavenumber (HW) in vivo confocal Raman spectroscopy for simultaneous FP/HW tissue Raman spectral measurements. This work further explores the potential of integrated FP/HW Raman spectroscopy developed as a diagnostic tool for in vivo detection of cervical precancer. A total of 473 in vivo integrated FP/HW Raman spectra (340 normal and 133 precancer) were acquired from 35 patients within 1 s during clinical colposcopy. The major tissue Raman peaks are noticed around 854, 937, 1001, 1095, 1253, 1313, 1445, 1654, 2946 and 3400 cm-1, related to the molecular changes (e.g., proteins, lipids, glycogen, nucleic acids, water, etc.) that accompany the dysplastic transformation of tissue. The FP (800 - 1800 cm-1), HW (2800 - 3800 cm-1) and the integrated FP/HW Raman spectra were analyzed using partial least squares-discriminant analysis (PLS-DA) together with the leave-one patient-out, cross-validation. The developed PLS-DA classification models and receiver operating characteristics (ROC) curves for the FP, HW and integrated FP/HW spectroscopy further discloses that the performance of integrated FP/HW Raman spectroscopy is superior to that of all others in discriminating the dysplastic cervix. The results of this work indicate that the co-contributions of underlying rich biochemical information revealed by the complementary spectral modalities (FP and HW Raman) can improve the in vivo early diagnosis of cervical precancer at clinical colposcopy

Development and validation of an APCI-MS/GC–MS approach for the classification and prediction of Cheddar cheese maturity

PubMed Central

Gan, Heng Hui; Yan, Bingnan; Linforth, Robert S.T.; Fisk, Ian D.

2016-01-01

Headspace techniques have been extensively employed in food analysis to measure volatile compounds, which play a central role in the perceived quality of food. In this study atmospheric pressure chemical ionisation-mass spectrometry (APCI-MS), coupled with gas chromatography–mass spectrometry (GC–MS), was used to investigate the complex mix of volatile compounds present in Cheddar cheeses of different maturity, processing and recipes to enable characterisation of the cheeses based on their ripening stages. Partial least squares-linear discriminant analysis (PLS-DA) provided a 70% success rate in correct prediction of the age of the cheeses based on their key headspace volatile profiles. In addition to predicting maturity, the analytical results coupled with chemometrics offered a rapid and detailed profiling of the volatile component of Cheddar cheeses, which could offer a new tool for quality assessment and accelerate product development. PMID:26212994

Microorganisms detection on substrates using QCL spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Castro-Suarez, John R.; Ríos-Velázquez, Carlos; Vázquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2013-05-01

Recent investigations have focused on the improvement of rapid and accurate methods to develop spectroscopic markers of compounds constituting microorganisms that are considered biological threats. Quantum cascade lasers (QCL) systems have revolutionized many areas of research and development in defense and security applications, including his area of research. Infrared spectroscopy detection based on QCL was employed to acquire mid infrared (MIR) spectral signatures of Bacillus thuringiensis (Bt), Escherichia coli (Ec) and Staphylococcus epidermidis (Se), which were used as biological agent simulants of biothreats. The experiments were carried out in reflection mode on various substrates such as cardboard, glass, travel baggage, wood and stainless steel. Chemometrics statistical routines such as principal component analysis (PCA) regression and partial least squares-discriminant analysis (PLS-DA) were applied to the recorded MIR spectra. The results show that the infrared vibrational techniques investigated are useful for classification/detection of the target microorganisms on the types of substrates studied.

A comparative study of volatile components in Dianhong teas from fresh leaves of four tea cultivars by using chromatography-mass spectrometry, multivariate data analysis, and descriptive sensory analysis.

PubMed

Wang, Chao; Zhang, Chenxia; Kong, Yawen; Peng, Xiaopei; Li, Changwen; Liu, Shunhang; Du, Liping; Xiao, Dongguang; Xu, Yongquan

2017-10-01

Dianhong teas produced from fresh leaves of different tea cultivars (YK is Yunkang No. 10, XY is Xueya 100, CY is Changyebaihao, SS is Shishengmiao), were compared in terms of volatile compounds and descriptive sensory analysis. A total of 73 volatile compounds in 16 tea samples were tentatively identified. YK, XY, CY, and SS contained 55, 53, 49, and 51 volatile compounds, respectively. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were used to classify the samples, and 40 key components were selected based on variable importance in the projection. Moreover, 11 flavor attributes, namely, floral, fruity, grass/green, woody, sweet, roasty, caramel, mellow and thick, bitter, astringent, and sweet aftertaste were identified through descriptive sensory analysis (DSA). In generally, innate differences among the tea varieties significantly affected the intensities of most of the key sensory attributes of Dianhong teas possibly because of the different amounts of aroma-active and taste components in Dianhong teas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying States along the Hematopoietic Stem Cell Differentiation Hierarchy with Single Cell Specificity via Raman Spectroscopy.

PubMed

Ilin, Yelena; Choi, Ji Sun; Harley, Brendan A C; Kraft, Mary L

2015-11-17

A major challenge for expanding specific types of hematopoietic cells ex vivo for the treatment of blood cell pathologies is identifying the combinations of cellular and matrix cues that direct hematopoietic stem cells (HSC) to self-renew or differentiate into cell populations ex vivo. Microscale screening platforms enable minimizing the number of rare HSCs required to screen the effects of numerous cues on HSC fate decisions. These platforms create a strong demand for label-free methods that accurately identify the fate decisions of individual hematopoietic cells at specific locations on the platform. We demonstrate the capacity to identify discrete cells along the HSC differentiation hierarchy via multivariate analysis of Raman spectra. Notably, cell state identification is accurate for individual cells and independent of the biophysical properties of the functionalized polyacrylamide gels upon which these cells are cultured. We report partial least-squares discriminant analysis (PLS-DA) models of single cell Raman spectra enable identifying four dissimilar hematopoietic cell populations across the HSC lineage specification. Successful discrimination was obtained for a population enriched for long-term repopulating HSCs (LT-HSCs) versus their more differentiated progeny, including closely related short-term repopulating HSCs (ST-HSCs) and fully differentiated lymphoid (B cells) and myeloid (granulocytes) cells. The lineage-specific differentiation states of cells from these four subpopulations were accurately identified independent of the stiffness of the underlying biomaterial substrate, indicating subtle spectral variations that discriminated these populations were not masked by features from the culture substrate. This approach enables identifying the lineage-specific differentiation stages of hematopoietic cells on biomaterial substrates of differing composition and may facilitate correlating hematopoietic cell fate decisions with the extrinsic cues that elicited them.

Exploring the potential of needle trap microextraction combined with chromatographic and statistical data to discriminate different types of cancer based on urinary volatomic biosignature.

PubMed

Porto-Figueira, Priscilla; Pereira, Jorge A M; Câmara, José S

2018-09-06

The worldwide high cancer incidence and mortality demands for more effective and specific diagnostic strategies. In this study, we evaluated the efficiency of an innovative methodology, Needle Trap Microextraction (NTME), combined with gas chromatography-mass spectrometry (GC-MS), for the establishment of the urinary volatomic biosignature from breast (BC), and colon (CC) cancer patients as well as healthy individuals (CTL). To achieve this, 40 mL of the headspace of acidified urine (4 mL, 20% NaCl, pH = 2), equilibrated at 50 °C during 40 min, were loaded through the DVB/Car1000/CarX sorbent inside the NTD, and subjected to a GC-MS analysis. This allowed the identification of 130 VOMs from different chemical families that were further processed using discriminant analysis through the partial least squares method (PLS-DA). Several pathways are over activated in cancer patients, being phenylalanine pathway in BC and limonene and pinene degradation pathway in CC the most relevant. Butanoate metabolism is also highly activated in both cancers, as well as tyrosine metabolism in a lesser extension. In BC the xenobiotics metabolism by cytochrome P450 and fatty acid biosynthesis are also differentially activated. Different clusters corresponding to the groups recruited allowed to define sets of volatile organic metabolites (VOMs fingerprints) that exhibit high classification rates, sensitivity and specificity in the discrimination of the selected cancers. As far as we are aware, this is the first time that NTME is used for isolation urinary volatile metabolites, being the obtained results very promising. Copyright © 2018 Elsevier B.V. All rights reserved.

Sphingolipids as New Biomarkers for Assessment of Delayed-Type Hypersensitivity and Response to Triptolide

PubMed Central

Qu, Feng; Wu, Cai-Sheng; Hou, Jin-Feng; Jin, Ying; Zhang, Jin-Lan

2012-01-01

Background Hypersensitivity diseases are associated with many severe human illnesses, including leprosy and tuberculosis. Emerging evidence suggests that the pathogenesis and pathological mechanisms of treating these diseases may be attributable to sphingolipid metabolism. Methods High performance liquid chromatography-tandem mass spectrometry was employed to target and measure 43 core sphingolipids in the plasma, kidneys, livers and spleens of BALB/c mice from four experimental groups: control, delayed-type hypersensitivity (DTH) model, DTH+triptolide, and control+triptolide. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify potential biomarkers associated with variance between groups. Relationships between the identified biomarkers and disease markers were evaluated by Spearman correlation. Results As a treatment to hypersensitivity disease, triptolide significantly inhibit the ear swelling and recover the reduction of splenic index caused by DTH. The sphingolipidomic result revealed marked alterations in sphingolipid levels between groups that were associated with the effects of the disease and triptolide treatment. Based on this data, 23 potential biomarkers were identified by OPLS-DA, and seven of these biomarkers correlated markedly with the disease markers (p<0.05) by Spearman correlation. Conclusions These data indicate that differences in sphingolipid levels in plasma and tissues are related to DTH and treatment with triptolide. Restoration of proper sphingolipid levels may attribute to the therapeutic effect of triptolide treatment. Furthermore, these findings demonstrate that targeted sphingolipidomic analysis followed by multivariate analysis presents a novel strategy for the identification of biomarkers in biological samples. PMID:23300675

Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry.

PubMed

Pasikanti, Kishore Kumar; Esuvaranathan, Kesavan; Hong, Yanjun; Ho, Paul C; Mahendran, Ratha; Raman Nee Mani, Lata; Chiong, Edmund; Chan, Eric Chun Yong

2013-09-06

Cystoscopy is the gold standard clinical diagnosis of human bladder cancer (BC). As cystoscopy is expensive and invasive, it compromises patients' compliance toward surveillance screening and challenges the detection of recurrent BC. Therefore, the development of a noninvasive method for the diagnosis and surveillance of BC and the elucidation of BC progression become pertinent. In this study, urine samples from 38 BC patients and 61 non-BC controls were subjected to urinary metabotyping using two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Subsequent to data preprocessing and chemometric analysis, the orthogonal partial least-squares discriminant analysis (OPLS-DA, R2X=0.278, R2Y=0.904 and Q2Y (cumulative)=0.398) model was validated using permutation tests and receiver operating characteristic (ROC) analysis. Marker metabolites were further screened from the OPLS-DA model using statistical tests. GC×GC-TOFMS urinary metabotyping demonstrated 100% specificity and 71% sensitivity in detecting BC, while 100% specificity and 46% sensitivity were observed via cytology. In addition, the model revealed 46 metabolites that characterize human BC. Among the perturbed metabolic pathways, our clinical finding on the alteration of the tryptophan-quinolinic metabolic axis in BC suggested the potential roles of kynurenine in the malignancy and therapy of BC. In conclusion, global urinary metabotyping holds potential for the noninvasive diagnosis and surveillance of BC in clinics. In addition, perturbed metabolic pathways gleaned from urinary metabotyping shed new and established insights on the biology of human BC.

[Urine metabonomic study on hypertension patients of ascendant hyperactivity of gan yang syndrome by high performance liquid chromatography coupled with time of flight mass spectrometry].

PubMed

Jiang, Hai-Qiang; Li, Yun-Lun; Xie, Jun

2012-03-01

To study the changes of urine metabolites in hypertension patients of ascendant hyperactivity of Gan yang syndrome (AHGYS), and to explore its essence in hypertension patients. Ten typical hypertension patients of AHGYS were recruited as the patient group, and the other twelve healthy volunteers were recruited as the normal group. The metabolite profiling in the urine were collected using by high performance liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOFMS). The principal component analysis (PCA) and partial least-square discriminant analysis (PLS-DA) were analyzed using SIMCA-P Software. The differential metabolites in the urine were found out and identified. The possible relevant metabolic pathways were explained. The data from the analysis by PCA in the urine samples of the patient group and the normal group showed, two sets of data could be obviously classified in the score plot. Compared with the normal group, significant changes happened to the body metabolism in the patient group. The metabolites relevant to hypertension patients of AHGYS were determined using the PLS-DA. Fifteen compounds of the structure and metabolic pathways had been confirmed through inquiring KEGG Database, mainly including amino acids, free fatty acids, sphingosine, and so on. The hypertension patients of AHGYS were studied using HPLC-TOFMS combined with pattern recognition, thus finding out small molecular metabolic markers from the microscopic field, which was advantageous in probing the biological nature of Chinese medicine syndromes.

Near infrared spectroscopy of human muscles

NASA Astrophysics Data System (ADS)

Gasbarrone, R.; Currà, A.; Cardillo, A.; Bonifazi, G.; Serranti, S.

2018-02-01

Optical spectroscopy is a powerful tool in research and industrial applications. Its properties of being rapid, non-invasive and not destructive make it a promising technique for qualitative as well as quantitative analysis in medicine. Recent advances in materials and fabrication techniques provided portable, performant, sensing spectrometers readily operated by user-friendly cabled or wireless systems. We used such a system to test whether infrared spectroscopy techniques, currently utilized in many areas as primary/secondary raw materials sector, cultural heritage, agricultural/food industry, environmental remote and proximal sensing, pharmaceutical industry, etc., could be applied in living humans to categorize muscles. We acquired muscles infrared spectra in the Vis-SWIR regions (350-2500 nm), utilizing an ASD FieldSpec 4 Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350-1000 nm and 1.1 nm at 1001-2500 nm. After a preliminary spectra pre-processing (i.e. signal scattering reduction), Principal Component Analysis (PCA) was applied to identify similar spectral features presence and to realize their further grouping. Partial Least-Squares Discriminant Analysis (PLS-DA) was utilized to implement discrimination/prediction models. We studied 22 healthy subjects (age 25-89 years, 11 females), by acquiring Vis-SWIR spectra from the upper limb muscles (i.e. biceps, a forearm flexor, and triceps, a forearm extensor). Spectroscopy was performed in fixed limb postures (elbow angle approximately 90‡). We found that optical spectroscopy can be applied to study human tissues in vivo. Vis-SWIR spectra acquired from the arm detect muscles, distinguish flexors from extensors.

[Application of Fourier transform infrared spectroscopy in identification of wine spoilage].

PubMed

Zhao, Xian-De; Dong, Da-Ming; Zheng, Wen-Gang; Jiao, Lei-Zi; Lang, Yun

2014-10-01

In the present work, fresh and spoiled wine samples from three wines produced by different companies were studied u- sing Fourier transform infrared (FTIR) spectroscopy. We analyzed the physicochemical property change in the process of spoil- age, and then, gave out the attribution of some main FTIR absorption peaks. A novel determination method was explored based on the comparisons of some absorbance ratios at different wavebands although the absorbance ratios in this method were relative. Through the compare of the wine spectra before and after spoiled, the authors found that they were informative at the bands of 3,020~2,790, 1,760~1,620 and 1,550~800 cm(-1). In order to find the relation between these informative spectral bands and the wine deterioration and achieve the discriminant analysis, chemometrics methods were introduced. Principal compounds analysis (PCA) and soft independent modeling of class analogy (SIMCA) were used for classifying different-quality wines. And partial least squares discriminant analysis (PLS-DA) was applied to identify spoiled wines and good wines. Results showed that FTIR technique combined with chemometrics methods could effectively distinguish spoiled wines from fresh samples. The effect of classification at the wave band of 1 550-800 cm(-1) was the best. The recognition rate of SIMCA and PLSDA were respectively 94% and 100%. This study demonstrates that Fourier transform infrared spectroscopy is an effective tool for monitoring red wine's spoilage and provides theoretical support for developing early-warning equipments.

UPLC-QTOF analysis reveals metabolomic changes in the flag leaf of wheat (Triticum aestivum L.) under low-nitrogen stress.

PubMed

Zhang, Yang; Ma, Xin-Ming; Wang, Xiao-Chun; Liu, Ji-Hong; Huang, Bing-Yan; Guo, Xiao-Yang; Xiong, Shu-Ping; La, Gui-Xiao

2017-02-01

Wheat is one of the most important grain crop plants worldwide. Nitrogen (N) is an essential macronutrient for the growth and development of wheat and exerts a marked influence on its metabolites. To investigate the influence of low nitrogen stress on various metabolites of the flag leaf of wheat (Triticum aestivum L.), a metabolomic analysis of two wheat cultivars under different induced nitrogen levels was conducted during two important growth periods based on large-scale untargeted metabolomic analysis using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF). Multivariate analyses-such as principle components analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA)-were used for data analysis. PCA yielded distinctive clustering information among the samples, classifying the wheat flag samples into two categories: those under normal N treatment and low N treatment. By processing OPLS-DA, eleven secondary metabolites were shown to be responsible for classifying the two groups. The secondary metabolites may be considered potential biomarkers of low nitrogen stress. Chemical analyses showed that most of the identified secondary metabolites were flavonoids and their related derivatives, such as iso-vitexin, iso-orientin and methylisoorientin-2″-O-rhamnoside, etc. This study confirmed the effect of low nitrogen stress on the metabolism of wheat, and revealed that the accumulation of secondary metabolites is a response to abiotic stresses. Meanwhile, we aimed to identify markers which could be used to monitor the nitrogen status of wheat crops, presumably to guide appropriate fertilization regimens. Furthermore, the UPLC-QTOF metabolic platform technology can be used to study metabolomic variations of wheat under abiotic stresses. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Comparative Metabolomic Analysis of the Green Microalga Chlorella sorokiniana Cultivated in the Single Culture and a Consortium with Bacteria for Wastewater Remediation.

PubMed

Chen, Taojing; Zhao, Quanyu; Wang, Liang; Xu, Yunfeng; Wei, Wei

2017-11-01

Co-culture of microalgae with many types of bacteria usually comes out with significant different treatment efficiencies for COD, nitrogen, and phosphorus in wastewater remediation, compared with the single culture. In order to understand the mechanism behind, a comparative experiment was designed in this study, using the green microalgae species Chlorella sorokiniana in the single culture and a consortium with a bacterium, Pseudomonas H4, for nutrient removal. Comparative metabolome profile analysis was conducted to reveal the Chlorella cell responses to the synergistic growth with the bacteria, and possible relations between the metabolic regulation of microalgae and the nutrient degradation were discussed. The detectable differential metabolites of Chlorella belonged to several classes, including carbohydrates, fatty acids, amino acids, phosphates, polyols, etc. The orthogonal partial least squares discriminant analysis (OPLS-DA) model of the identified metabolites suggests the metabolism in this alga was significantly affected by the bacteria, corresponding to different treatment behaviors.

Chemical data as markers of the geographical origins of sugarcane spirits.

PubMed

Serafim, F A T; Pereira-Filho, Edenir R; Franco, D W

2016-04-01

In an attempt to classify sugarcane spirits according to their geographic region of origin, chemical data for 24 analytes were evaluated in 50 cachaças produced using a similar procedure in selected regions of Brazil: São Paulo - SP (15), Minas Gerais - MG (11), Rio de Janeiro - RJ (11), Paraiba -PB (9), and Ceará - CE (4). Multivariate analysis was applied to the analytical results, and the predictive abilities of different classification methods were evaluated. Principal component analysis identified five groups, and chemical similarities were observed between MG and SP samples and between RJ and PB samples. CE samples presented a distinct chemical profile. Among the samples, partial linear square discriminant analysis (PLS-DA) classified 50.2% of the samples correctly, K-nearest neighbor (KNN) 86%, and soft independent modeling of class analogy (SIMCA) 56.2%. Therefore, in this proof of concept demonstration, the proposed approach based on chemical data satisfactorily predicted the cachaças' geographic origins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classification of illicit heroin by UPLC-Q-TOF analysis of acidic and neutral manufacturing impurities.

PubMed

Liu, Cuimei; Hua, Zhendong; Bai, Yanping

2015-12-01

The illicit manufacture of heroin results in the formation of trace levels of acidic and neutral manufacturing impurities that provide valuable information about the manufacturing process used. In this work, a new ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF) method; that features high resolution, mass accuracy and sensitivity for profiling neutral and acidic heroin manufacturing impurities was developed. After the UPLC-Q-TOF analysis, the retention times and m/z data pairs of acidic and neutral manufacturing impurities were detected, and 19 peaks were found to be evidently different between heroin samples from "Golden Triangle" and "Golden Crescent". Based on the data set of these 19 impurities in 150 authentic heroin samples, classification of heroin geographic origins was successfully achieved utilizing partial least squares discriminant analysis (PLS-DA). By analyzing another data set of 267 authentic heroin samples, the developed discrimiant model was validated and proved to be accurate and reliable. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A P3 study.

PubMed

Koerner, Tess K; Zhang, Yang; Nelson, Peggy B; Wang, Boxiang; Zou, Hui

2017-07-01

This study examined how speech babble noise differentially affected the auditory P3 responses and the associated neural oscillatory activities for consonant and vowel discrimination in relation to segmental- and sentence-level speech perception in noise. The data were collected from 16 normal-hearing participants in a double-oddball paradigm that contained a consonant (/ba/ to /da/) and vowel (/ba/ to /bu/) change in quiet and noise (speech-babble background at a -3 dB signal-to-noise ratio) conditions. Time-frequency analysis was applied to obtain inter-trial phase coherence (ITPC) and event-related spectral perturbation (ERSP) measures in delta, theta, and alpha frequency bands for the P3 response. Behavioral measures included percent correct phoneme detection and reaction time as well as percent correct IEEE sentence recognition in quiet and in noise. Linear mixed-effects models were applied to determine possible brain-behavior correlates. A significant noise-induced reduction in P3 amplitude was found, accompanied by significantly longer P3 latency and decreases in ITPC across all frequency bands of interest. There was a differential effect of noise on consonant discrimination and vowel discrimination in both ERP and behavioral measures, such that noise impacted the detection of the consonant change more than the vowel change. The P3 amplitude and some of the ITPC and ERSP measures were significant predictors of speech perception at segmental- and sentence-levels across listening conditions and stimuli. These data demonstrate that the P3 response with its associated cortical oscillations represents a potential neurophysiological marker for speech perception in noise. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolic Profiling in Patients with Pneumonia on Intensive Care.

PubMed

Antcliffe, David; Jiménez, Beatriz; Veselkov, Kirill; Holmes, Elaine; Gordon, Anthony C

2017-04-01

Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care. In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D 1 H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R 2 Y=0.91, Q 2 Y=0.28, p=0.02) and those with VAP from those without (R 2 Y=0.94, Q 2 Y=0.27, p=0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins. Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics.

PubMed

Zhang, Chu; Shen, Tingting; Liu, Fei; He, Yong

2017-12-31

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied.

Use of metabolomics and lipidomics to evaluate the hypocholestreolemic effect of Proanthocyanidins from grape seed in a pig model.

PubMed

Quifer-Rada, Paola; Choy, Ying Yng; Calvert, Christopher C; Waterhouse, Andrew L; Lamuela-Raventos, Rosa M

2016-10-01

This work aims to evaluate changes in the fecal metabolomic profile due to grape seed extract (GSE) intake by untargeted and targeted analysis using high resolution mass spectrometry in conjunction with multivariate statistics. An intervention study with six crossbred female pigs was performed. The pigs followed a standard diet for 3 days, then they were fed with a supplemented diet containing 1% (w/w) of MegaNatural® Gold grape seed extract for 6 days. Fresh pig fecal samples were collected daily. A combination of untargeted high resolution mass spectrometry, multivariate analysis (PLS-DA), data-dependent MS/MS scan, and accurate mass database matching was used to measure the effect of the treatment on fecal composition. The resultant PLS-DA models showed a good discrimination among classes with great robustness and predictability. A total of 14 metabolites related to the GSE consumption were identified including biliary acid, dicarboxylic fatty acid, cholesterol metabolites, purine metabolites, and eicosanoid metabolites among others. Moreover, targeted metabolomics using GC-MS showed that cholesterol and its metabolites fecal excretion was increased due to the proanthocyanidins from grape seed extract. The results show that oligomeric procyanidins from GSE modifies bile acid and steroid excretion, which could exert a hypocholesterolemic effect. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

PubMed Central

Zhang, Chu; Shen, Tingting

2017-01-01

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied. PMID:29301228

Metabonomics identifies serum metabolite markers of colorectal cancer.

PubMed

Tan, Binbin; Qiu, Yunping; Zou, Xia; Chen, Tianlu; Xie, Guoxiang; Cheng, Yu; Dong, Taotao; Zhao, Linjing; Feng, Bo; Hu, Xiaofang; Xu, Lisa X; Zhao, Aihua; Zhang, Menghui; Cai, Guoxiang; Cai, Sanjun; Zhou, Zhanxiang; Zheng, Minhua; Zhang, Yan; Jia, Wei

2013-06-07

Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.

Influence of metal substrates on the detection of explosive residues with laser-induced breakdown spectroscopy.

PubMed

Gottfried, Jennifer L

2013-02-01

Laser-induced breakdown spectroscopy is a promising approach for explosive residue detection, but several limitations to its widespread use remain. One issue is that the emission spectra of the residues are dependent on the substrate composition because some of the substrate is usually entrained in the laser-induced plasma and the laser-material interaction can be significantly affected by the substrate type. Here, we have demonstrated that despite the strong spectral variation in cyclotrimethylenetrinitramine (RDX) residues applied to various metal substrates, classification of the RDX residue independent of substrate type is feasible. Several approaches to improving the chemometric models based on partial least squares discriminant analysis (PLS-DA) have been described: classifying the RDX residue spectra together in one class independent of substrate, using selected emission intensities and ratios to increase the true positive rate (TPR) and decrease the false positive rate (FPR), and fusing the results from two PLS-DA models generated using the full broadband spectra and selected intensities and ratios. The combination of these approaches resulted in a TPR of 97.5% and a FPR of 1.0% for RDX classification on metal substrates.

Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes.

PubMed

Diaz, Sílvia O; Barros, António S; Goodfellow, Brian J; Duarte, Iola F; Galhano, Eulália; Pita, Cristina; Almeida, Maria do Céu; Carreira, Isabel M; Gil, Ana M

2013-06-07

Given the recognized lack of prenatal clinical methods for the early diagnosis of preterm delivery, intrauterine growth restriction, preeclampsia and gestational diabetes mellitus, and the continuing need for optimized diagnosis methods for specific chromosomal disorders (e.g., trisomy 21) and fetal malformations, this work sought specific metabolic signatures of these conditions in second trimester maternal urine, using (1)H Nuclear Magnetic Resonance ((1)H NMR) metabolomics. Several variable importance to the projection (VIP)- and b-coefficient-based variable selection methods were tested, both individually and through their intersection, and the resulting data sets were analyzed by partial least-squares discriminant analysis (PLS-DA) and submitted to Monte Carlo cross validation (MCCV) and permutation tests to evaluate model predictive power. The NMR data subsets produced significantly improved PLS-DA models for all conditions except for pre-premature rupture of membranes. Specific urinary metabolic signatures were unveiled for central nervous system malformations, trisomy 21, preterm delivery, gestational diabetes, intrauterine growth restriction and preeclampsia, and biochemical interpretations were proposed. This work demonstrated, for the first time, the value of maternal urine profiling as a complementary means of prenatal diagnostics and early prediction of several poor pregnancy outcomes.

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning.

PubMed

Vamsikrishna, K M; Dogra, Debi Prosad; Desarkar, Maunendra Sankar

2016-05-01

Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

Nuclear Forensic Inferences Using Iterative Multidimensional Statistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robel, M; Kristo, M J; Heller, M A

2009-06-09

Nuclear forensics involves the analysis of interdicted nuclear material for specific material characteristics (referred to as 'signatures') that imply specific geographical locations, production processes, culprit intentions, etc. Predictive signatures rely on expert knowledge of physics, chemistry, and engineering to develop inferences from these material characteristics. Comparative signatures, on the other hand, rely on comparison of the material characteristics of the interdicted sample (the 'questioned sample' in FBI parlance) with those of a set of known samples. In the ideal case, the set of known samples would be a comprehensive nuclear forensics database, a database which does not currently exist. Inmore » fact, our ability to analyze interdicted samples and produce an extensive list of precise materials characteristics far exceeds our ability to interpret the results. Therefore, as we seek to develop the extensive databases necessary for nuclear forensics, we must also develop the methods necessary to produce the necessary inferences from comparison of our analytical results with these large, multidimensional sets of data. In the work reported here, we used a large, multidimensional dataset of results from quality control analyses of uranium ore concentrate (UOC, sometimes called 'yellowcake'). We have found that traditional multidimensional techniques, such as principal components analysis (PCA), are especially useful for understanding such datasets and drawing relevant conclusions. In particular, we have developed an iterative partial least squares-discriminant analysis (PLS-DA) procedure that has proven especially adept at identifying the production location of unknown UOC samples. By removing classes which fell far outside the initial decision boundary, and then rebuilding the PLS-DA model, we have consistently produced better and more definitive attributions than with a single pass classification approach. Performance of the iterative PLS-DA method compared favorably to that of classification and regression tree (CART) and k nearest neighbor (KNN) algorithms, with the best combination of accuracy and robustness, as tested by classifying samples measured independently in our laboratories against the vendor QC based reference set.« less

Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Yang, Yue; Wu, Yongjiang; Li, Weili; Liu, Xuesong; Zheng, Jiyu; Zhang, Wentao; Chen, Yong

2018-02-01

Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii. The recognition rates of the optimum SVM model were up to 100% for the calibration set and 94.44% for the prediction set, respectively. In addition, the feasibility of NIR spectroscopy with the CARS-PLSR calibration model in prediction of icariin content of Herba Epimedii was also investigated. The determination coefficient (RP2) and root-mean-square error (RMSEP) for prediction set were 0.9269 and 0.0480, respectively. It can be concluded that the NIR spectroscopy technique in combination with chemometrics has great potential in determination of geographical origin and icariin content of Herba Epimedii. This study can provide a valuable reference for rapid quality control of food products.

Multivariate statistical analysis of the hydrogeochemical and isotopic composition of the groundwater resources in northeastern Peloponnesus (Greece).

PubMed

Matiatos, Ioannis; Alexopoulos, Apostolos; Godelitsas, Athanasios

2014-04-01

The present study involves an integration of the hydrogeological, hydrochemical and isotopic (both stable and radiogenic) data of the groundwater samples taken from aquifers occurring in the region of northeastern Peloponnesus. Special emphasis has been given to health-related ions and isotopes in relation to the WHO and USEPA guidelines, to highlight the concentrations of compounds (e.g., As and Ba) exceeding the drinking water thresholds. Multivariate statistical analyses, i.e. two principal component analyses (PCA) and one discriminant analysis (DA), combined with conventional hydrochemical methodologies, were applied, with the aim to interpret the spatial variations in the groundwater quality and to identify the main hydrogeochemical factors and human activities responsible for the high ion concentrations and isotopic content in the groundwater analysed. The first PCA resulted in a three component model, which explained approximately 82% of the total variance of the data sets and enabled the identification of the hydrogeological processes responsible for the isotopic content i.e., δ(18)Ο, tritium and (222)Rn. The second PCA, involving the trace element presence in the water samples, revealed a four component model, which explained approximately 89% of the total variance of the data sets, giving more insight into the geochemical and anthropogenic controls on the groundwater composition (e.g., water-rock interaction, hydrothermal activity and agricultural activities). Using discriminant analysis, a four parameter (δ(18)O, (Ca+Mg)/(HCO3+SO4), EC and Cl) discriminant function concerning the (222)Rn content was derived, which favoured a classification of the samples according to the concentration of (222)Rn as (222)Rn-safe (<11 Bq·L(-1)) and (222)Rn-contaminated (>11 Bq·L(-1)). The selection of radon builds on the fact that this radiogenic isotope has been generally related to increased health risk when consumed. Copyright © 2014 Elsevier B.V. All rights reserved.

MALDI-TOF-MS with PLS Modeling Enables Strain Typing of the Bacterial Plant Pathogen Xanthomonas axonopodis

NASA Astrophysics Data System (ADS)

Sindt, Nathan M.; Robison, Faith; Brick, Mark A.; Schwartz, Howard F.; Heuberger, Adam L.; Prenni, Jessica E.

2018-02-01

Matrix-assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) is a fast and effective tool for microbial species identification. However, current approaches are limited to species-level identification even when genetic differences are known. Here, we present a novel workflow that applies the statistical method of partial least squares discriminant analysis (PLS-DA) to MALDI-TOF-MS protein fingerprint data of Xanthomonas axonopodis, an important bacterial plant pathogen of fruit and vegetable crops. Mass spectra of 32 X. axonopodis strains were used to create a mass spectral library and PLS-DA was employed to model the closely related strains. A robust workflow was designed to optimize the PLS-DA model by assessing the model performance over a range of signal-to-noise ratios (s/n) and mass filter (MF) thresholds. The optimized parameters were observed to be s/n = 3 and MF = 0.7. The model correctly classified 83% of spectra withheld from the model as a test set. A new decision rule was developed, termed the rolled-up Maximum Decision Rule (ruMDR), and this method improved identification rates to 92%. These results demonstrate that MALDI-TOF-MS protein fingerprints of bacterial isolates can be utilized to enable identification at the strain level. Furthermore, the open-source framework of this workflow allows for broad implementation across various instrument platforms as well as integration with alternative modeling and classification algorithms.

A GC-MS Based Metabonomics Study of Rheumatoid Arthritis and the Interventional Effects of the Simiaowan in Rats.

PubMed

Wang, Yuming; Guo, Xuejun; Xie, Jiabin; Hou, Zhiguo; Li, Yubo

2015-12-01

Simiaowan (SMW) is a famous Chinese prescription widely used in clinical treatment of rheumatoid arthritis (RA). The aim of the present study is to determine novel biomarkers to increase the current understanding of RA mechanisms, as well as the underlying therapeutic mechanism of SMW, in RA-model rats. Plasma extracts from control, RA model, and SMW-treated rats were analyzed by gas chromatography coupled with mass spectrometry (GC-MS). An orthogonal partial least-square discriminant analysis (OPLS-DA) model was created to detect metabolites that were expressed in significantly different amounts between the RA model and the control rats and investigate the therapeutic effect of SMW. Metabonomics may prove to be a valuable tool for determining the efficacy of complex traditional prescriptions.

Discerning some Tylenol brands using attenuated total reflection Fourier transform infrared data and multivariate analysis techniques.

PubMed

Msimanga, Huggins Z; Ollis, Robert J

2010-06-01

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify acetaminophen-containing medicines using their attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 Hour Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coefficients ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (determined by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examined. Spectra were baseline corrected and normalized to unity before multivariate analysis. Analysis of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a constant force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a constant force gauge setting of 50 units. After removing outliers, PCA successfully classified the five samples in the first and second components, accounting for 45.0% and 24.5% of the variances, respectively. The four-component PLS-DA model (R(2)=0.925 and Q(2)=0.906) gave good test spectra predictions with an overall average of 0.961 +/- 7.1% RSD versus the expected 1.0 prediction for the 20 test spectra used.

Unbiased plasma metabolomics reveal the correlation of metabolic pathways and Prakritis of humans.

PubMed

Shirolkar, Amey; Chakraborty, Sutapa; Mandal, Tusharkanti; Dabur, Rajesh

2017-11-25

Ayurveda, an ancient Indian medicinal system, has categorized human body constitutions in three broad constitutional types (prakritis) i.e. Vata, Pitta and Kapha. Analysis of plasma metabolites and related pathways to classify Prakriti specific dominant marker metabolites and metabolic pathways. 38 healthy male individuals were assessed for dominant Prakritis and their fasting blood samples were collected. The processed plasma samples were subjected to rapid resolution liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry (RRLC-ESI-QTOFMS). Mass profiles were aligned and subjected to multivariate analysis. Partial least square discriminant analysis (PLS-DA) model showed 97.87% recognition capability. List of PLS-DA metabolites was subjected to permutative Benjamini-Hochberg false discovery rate (FDR) correction and final list of 76 metabolites with p < 0.05 and fold-change > 2.0 was identified. Pathway analysis using metascape and JEPETTO plugins in Cytoscape revealed that steroidal hormone biosynthesis, amino acid, and arachidonic acid metabolism are major pathways varying with different constitution. Biological Go processes analysis showed that aromatic amino acids, sphingolipids, and pyrimidine nucleotides metabolic processes were dominant in kapha type of body constitution. Fat soluble vitamins, cellular amino acid, and androgen biosynthesis process along with branched chain amino acid and glycerolipid catabolic processes were dominant in pitta type individuals. Vata Prakriti was found to have dominant catecholamine, arachidonic acid and hydrogen peroxide metabolomics processes. The neurotransmission and oxidative stress in vata, BCAA catabolic, androgen, xenobiotics metabolic processes in pitta, and aromatic amino acids, sphingolipid, and pyrimidine metabolic process in kaphaPrakriti were the dominant marker pathways. Copyright © 2017 Transdisciplinary University, Bangalore and World Ayurveda Foundation. Published by Elsevier B.V. All rights reserved.

Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury.

PubMed

Gao, Songyan; Chen, Wei; Peng, Zhongjiang; Li, Na; Su, Li; Lv, Diya; Li, Ling; Lin, Qishan; Dong, Xin; Guo, Zhiyong; Lou, Ziyang

2015-05-26

Orthosiphon stamineus (OS), a traditional Chinese herb, is often used for promoting urination and treating nephrolithiasis. Urolithiasis is a major worldwide public health burden due to its high incidence of recurrence and damage to renal function. However, the etiology for urolithiasis is not well understood. Metabonomics, the systematic study of small molecule metabolites present in biological samples, has become a valid and powerful tool for understanding disease phenotypes. In this study, a urinary metabolic profiling analysis was performed in a mouse model of renal calcium oxalate crystal deposition to identify potential biomarkers for crystal-induced renal damage and the anti-crystal mechanism of OS. Thirty six mice were randomly divided into six groups including Saline, Crystal, Cystone and OS at dosages of 0.5g/kg, 1g/kg, and 2g/kg. A metabonomics approach using ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) was developed to perform the urinary metabolic profiling analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to identify differences between the metabolic profiles of mice in the saline control group and crystal group. Using partial least squares-discriminant analysis, 30 metabolites were identified as potential biomarkers of crystal-induced renal damage. Most of them were primarily involved in amino acid metabolism, taurine and hypotaurine metabolism, purine metabolism, and the citrate cycle (TCA). After the treatment with OS, the levels of 20 biomarkers had returned to the levels of the control samples. Our results suggest that OS has a protective effect for mice with crystal-induced kidney injury via the regulation of multiple metabolic pathways primarily involving amino acid, energy and choline metabolism. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Heuristics to Facilitate Understanding of Discriminant Analysis.

ERIC Educational Resources Information Center

Van Epps, Pamela D.

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Differential effects of 3,4-methylenedioxypyrovalerone (MDPV) and 4-methylmethcathinone (mephedrone) in rats trained to discriminate MDMA or a d-amphetamine + MDMA mixture.

PubMed

Harvey, Eric L; Baker, Lisa E

2016-02-01

Recent reports on the abuse of novel synthetic cathinone derivatives call attention to serious public health risks of these substances. In response to this concern, a growing body of preclinical research has characterized the psychopharmacology of these substances, particularly mephedrone (MEPH) or methylenedioxypyrovalerone (MDPV), noting their similarities to 3,4-methylenedioxymethamphetamine (MDMA) and cocaine. Few studies have utilized drug discrimination methodology to characterize the psychopharmacological properties of these substances. The present study employed a rodent drug discrimination assay to further characterize the stimulus effects of MEPH and MDPV in comparison to MDMA and to a drug mixture comprised of d-amphetamine and MDMA. Eight male Sprague-Dawley rats were trained to discriminate 1.5 mg/kg MDMA, and eight rats were trained to discriminate a mixture of 1.5 mg/kg MDMA and 0.5 mg/kg d-amphetamine (MDMA + AMPH) from vehicle. Substitution tests were conducted with MDMA, d-amphetamine, MDPV, MEPH, and cocaine. Dose-response curves generated with MDMA and MEPH were comparable between training groups. In contrast, AMPH, MDPV, and cocaine produced only partial substitution in animals trained to discriminate MDMA but produced full substitution in animals trained to discriminate the MDMA + AMPH mixture. These findings indicate that MDPV's effects may be more similar to those of traditional psychostimulants, whereas MEPH exerts stimulus effects more similar to those of MDMA. Additional experiments with selective DA and 5-hydroxytryptamine (5-HT) receptor antagonists are required to further elucidate specific receptor mechanisms mediating the discriminative stimulus effects of MDPV and mephedrone.

A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia

ERIC Educational Resources Information Center

Lukes, Robin; Bangs, Joann

2014-01-01

This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…

Development of LLNA:DAE: a new local lymph node assay that includes the elicitation phase, discriminates borderline-positive chemicals, and is useful for cross-sensitization testing.

PubMed

Yamashita, Kunihiko; Shinoda, Shinsuke; Hagiwara, Saori; Itagaki, Hiroshi

2014-02-01

We developed a new local lymph node assay (LLNA) that includes the elicitation phase termed LLNA:DAE for discrimination of borderline-positive chemicals as classified by the LLNA modified by Daicel based on ATP content (LLNA:DA) and for cross-sensitization testing. Although the LLNA:DA method could help identify skin sensitizers, some skin irritants classified as non-sensitizers by the LLNA were classified as borderline positive. In addition, the evaluation for the cross-sensitization potential between chemicals was impossible. In the LLNA:DAE procedure, test group of mice received four applications of chemicals on the dorsum of the right ear for induction and one application on the dorsum of the left ear for elicitation. Control group of mice received one chemical application on the dorsum of the left ear. We evaluated the sensitizing potential by comparing the weights of the lymph nodes from the left ears between the two groups. The results of using the LLNA:DAE method to examine 24 chemicals, which contained borderline-positive chemicals, were consistent with those from the LLNA method, except for nickel chloride (NiCl2). Two chemical pairs, 2,4-dinitrochlorobenzene (DNCB) with 2,4-dinitrofluorobenzene (DNFB) and hydroquinone (HQ) with p-benzoquinone (p-BQ), showed clear cross-sensitization with each other, while another chemical pair, DNFB with hexylcinnamic aldehyde (HCA) did not. Taken together, our results suggest that the LLNA:DAE method is useful for discriminating borderline-positive chemicals and for determining chemical cross-sensitization.

Metabolically Generated Stable Isotope-Labeled Deoxynucleoside Code for Tracing DNA N6-Methyladenine in Human Cells.

PubMed

Liu, Baodong; Liu, Xiaoling; Lai, Weiyi; Wang, Hailin

2017-06-06

DNA N 6 -methyl-2'-deoxyadenosine (6mdA) is an epigenetic modification in both eukaryotes and bacteria. Here we exploited stable isotope-labeled deoxynucleoside [ 15 N 5 ]-2'-deoxyadenosine ([ 15 N 5 ]-dA) as an initiation tracer and for the first time developed a metabolically differential tracing code for monitoring DNA 6mdA in human cells. We demonstrate that the initiation tracer [ 15 N 5 ]-dA undergoes a specific and efficient adenine deamination reaction leading to the loss the exocyclic amine 15 N, and further utilizes the purine salvage pathway to generate mainly both [ 15 N 4 ]-dA and [ 15 N 4 ]-2'-deoxyguanosine ([ 15 N 4 ]-dG) in mammalian genomes. However, [ 15 N 5 ]-dA is largely retained in the genomes of mycoplasmas, which are often found in cultured cells and experimental animals. Consequently, the methylation of dA generates 6mdA with a consistent coding pattern, with a predominance of [ 15 N 4 ]-6mdA. Therefore, mammalian DNA 6mdA can be potentially discriminated from that generated by infecting mycoplasmas. Collectively, we show a promising approach for identification of authentic DNA 6mdA in human cells and determine if the human cells are contaminated with mycoplasmas.

The urine metabolome differs between lean and overweight Labrador Retriever dogs during a feed-challenge.

PubMed

Söder, Josefin; Hagman, Ragnvi; Dicksved, Johan; Lindåse, Sanna; Malmlöf, Kjell; Agback, Peter; Moazzami, Ali; Höglund, Katja; Wernersson, Sara

2017-01-01

Obesity in dogs is an increasing problem and better knowledge of the metabolism of overweight dogs is needed. Identification of molecular changes related to overweight may lead to new methods to improve obesity prevention and treatment. The aim of the study was firstly to investigate whether Nuclear Magnetic Resonance (NMR) based metabolomics could be used to differentiate postprandial from fasting urine in dogs, and secondly to investigate whether metabolite profiles differ between lean and overweight dogs in fasting and postprandial urine, respectively. Twenty-eight healthy intact male Labrador Retrievers were included, 12 of which were classified as lean (body condition score (BCS) 4-5 on a 9-point scale) and 16 as overweight (BCS 6-8). After overnight fasting, a voided morning urine sample was collected. Dogs were then fed a high-fat mixed meal and postprandial urine was collected after 3 hours. Metabolic profiles were generated using NMR and 45 metabolites identified from the spectral data were evaluated using multivariate data analysis. The results revealed that fasting and postprandial urine differed in relative metabolite concentration (partial least-squares discriminant analysis (PLS-DA) 1 comp: R2Y = 0.4, Q2Y = 0.32; cross-validated ANOVA: P = 0.00006). Univariate analyses of discriminant metabolites showed that taurine and citrate concentrations were elevated in postprandial urine, while allantoin concentration had decreased. Interestingly, lean and overweight dogs differed in terms of relative metabolite concentrations in postprandial urine (PLS-DA 1 comp: R2Y = 0.5, Q2Y = 0.36, cross-validated ANOVA: P = 0.005) but not in fasting urine. Overweight dogs had lower postprandial taurine and a trend of higher allantoin concentrations compared with lean dogs. These findings demonstrate that metabolomics can differentiate 3-hour postprandial urine from fasting urine in dogs, and that postprandial urine metabolites may be more useful than fasting metabolites for identification of metabolic alterations linked to overweight. The lowered urinary taurine concentration in overweight dogs could indicate alterations in lipid metabolism and merits further investigation.

The urine metabolome differs between lean and overweight Labrador Retriever dogs during a feed-challenge

PubMed Central

Söder, Josefin; Hagman, Ragnvi; Dicksved, Johan; Lindåse, Sanna; Malmlöf, Kjell; Agback, Peter; Moazzami, Ali; Höglund, Katja; Wernersson, Sara

2017-01-01

Obesity in dogs is an increasing problem and better knowledge of the metabolism of overweight dogs is needed. Identification of molecular changes related to overweight may lead to new methods to improve obesity prevention and treatment. The aim of the study was firstly to investigate whether Nuclear Magnetic Resonance (NMR) based metabolomics could be used to differentiate postprandial from fasting urine in dogs, and secondly to investigate whether metabolite profiles differ between lean and overweight dogs in fasting and postprandial urine, respectively. Twenty-eight healthy intact male Labrador Retrievers were included, 12 of which were classified as lean (body condition score (BCS) 4–5 on a 9-point scale) and 16 as overweight (BCS 6–8). After overnight fasting, a voided morning urine sample was collected. Dogs were then fed a high-fat mixed meal and postprandial urine was collected after 3 hours. Metabolic profiles were generated using NMR and 45 metabolites identified from the spectral data were evaluated using multivariate data analysis. The results revealed that fasting and postprandial urine differed in relative metabolite concentration (partial least-squares discriminant analysis (PLS-DA) 1 comp: R2Y = 0.4, Q2Y = 0.32; cross-validated ANOVA: P = 0.00006). Univariate analyses of discriminant metabolites showed that taurine and citrate concentrations were elevated in postprandial urine, while allantoin concentration had decreased. Interestingly, lean and overweight dogs differed in terms of relative metabolite concentrations in postprandial urine (PLS-DA 1 comp: R2Y = 0.5, Q2Y = 0.36, cross-validated ANOVA: P = 0.005) but not in fasting urine. Overweight dogs had lower postprandial taurine and a trend of higher allantoin concentrations compared with lean dogs. These findings demonstrate that metabolomics can differentiate 3-hour postprandial urine from fasting urine in dogs, and that postprandial urine metabolites may be more useful than fasting metabolites for identification of metabolic alterations linked to overweight. The lowered urinary taurine concentration in overweight dogs could indicate alterations in lipid metabolism and merits further investigation. PMID:28662207

Metabolic changes in different developmental stages of Vanilla planifolia pods.

PubMed

Palama, Tony Lionel; Khatib, Alfi; Choi, Young Hae; Payet, Bertrand; Fock, Isabelle; Verpoorte, Robert; Kodja, Hippolyte

2009-09-09

The metabolomic analysis of developing Vanilla planifolia green pods (between 3 and 8 months after pollination) was carried out by nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Multivariate data analysis of the (1)H NMR spectra, such as principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA), showed a trend of separation of those samples based on the metabolites present in the methanol/water (1:1) extract. Older pods had a higher content of glucovanillin, vanillin, p-hydroxybenzaldehyde glucoside, p-hydroxybenzaldehyde, and sucrose, while younger pods had more bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A), bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-(2-butyl)tartrate (glucoside B), glucose, malic acid, and homocitric acid. A liquid chromatography-mass spectrometry (LC-MS) analysis targeted at phenolic compound content was also performed on the developing pods and confirmed the NMR results. Ratios of aglycones/glucosides were estimated and thus allowed for detection of more minor metabolites in the green vanilla pods. Quantification of compounds based on both LC-MS and NMR analyses showed that free vanillin can reach 24% of the total vanillin content after 8 months of development in the vanilla green pods.

Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics.

PubMed

Gouveia, Liana Ribeiro; Santos, Joelma Carvalho; Silva, Ronaldo Dionísio; Batista, Andrea Dória; Domingues, Ana Lúcia Coutinho; Lopes, Edmundo Pessoa de Almeida; Silva, Ricardo Oliveira

2017-01-01

Diagnosis of liver involvement due to schistosomiasis in asymptomatic patients from endemic areas previously diagnosed with chronic hepatitis B (HBV) or C (HCV) and periportal fibrosis is challenging. H-1 Nuclear Magnetic Resonance (NMR)-based metabonomics strategy is a powerful tool for providing a profile of endogenous metabolites of low molecular weight in biofluids in a non-invasive way. The aim of this study was to diagnose periportal fibrosis due to schistosomiasis mansoni in patients with chronic HBV or HCV infection through NMR-based metabonomics models. The study included 40 patients divided into two groups: (i) 18 coinfected patients with schistosomiasis mansoni and HBV or HCV; and (ii) 22 HBV or HCV monoinfected patients. The serum samples were analyzed through H-1 NMR spectroscopy and the models were based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). Ultrasonography examination was used to ascertain the diagnosis of periportal fibrosis. Exploratory analysis showed a clear separation between coinfected and monoinfected samples. The supervised model built from PLS-DA showed accuracy, R2 and Q2 values equal to 100%, 98.1% and 97.5%, respectively. According to the variable importance in the projection plot, lactate serum levels were higher in the coinfected group, while the signals attributed to HDL serum cholesterol were more intense in the monoinfected group. The metabonomics models constructed in this study are promising as an alternative tool for diagnosis of periportal fibrosis by schistosomiasis in patients with chronic HBV or HCV infection from endemic areas for Schistosoma mansoni.

Tumour xenograft detection through quantitative analysis of the metabolic profile of urine in mice

NASA Astrophysics Data System (ADS)

Moroz, Jennifer; Turner, Joan; Slupsky, Carolyn; Fallone, Gino; Syme, Alasdair

2011-02-01

The metabolic content of urine from NIH III nude mice (n = 22) was analysed before and after inoculation with human glioblastoma multiforme (GBM) cancer cells. An age- and gender-matched control population (n = 14) was also studied to identify non-tumour-related changes. Urine samples were collected daily for 6 weeks, beginning 1 week before cell injection. Metabolite concentrations were obtained via targeted profiling with Chenomx Suite 5.1, based on nuclear magnetic resonance (NMR) spectra acquired on an Oxford 800 MHz cold probe NMR spectrometer. The Wilcoxon rank sum test was used to evaluate the significance of the change in metabolite concentration between the two time points. Both the metabolite concentrations and the ratios of pairs of metabolites were studied. The complicated inter-relationships between metabolites were assessed through partial least-squares discriminant analysis (PLS-DA). Receiver operating characteristic (ROC) curves were generated for all variables and the area under the curve (AUC) calculated. The data indicate that the number of statistically significant changes in metabolite concentrations was more pronounced in the tumour-bearing population than in the control animals. This was also true of the ratios of pairs of metabolites. ROC analysis suggests that the ratios were better able to differentiate between the pre- and post-injection samples compared to the metabolite concentrations. PLS-DA models produced good separation between the populations and had the best AUC results (all models exceeded 0.937). These results demonstrate that metabolomics may be used as a screening tool for GBM cells grown in xenograft models in mice.

Quantitative determination of phenolic compounds by UHPLC-UV-MS and use of partial least-square discriminant analysis to differentiate chemo-types of Chamomile/Chrysanthemum flower heads.

PubMed

Avula, Bharathi; Wang, Yan-Hong; Wang, Mei; Avonto, Cristina; Zhao, Jianping; Smillie, Troy J; Rua, Diego; Khan, Ikhlas A

2014-01-01

A new rapid UHPLC-UV-QTOF/MS method has been developed for the simultaneous analysis of nine phenolic compounds [(Z)-2-β-d-glucopyranosyloxy-4-methoxycinnamic acid (cis-GMCA), chlorogenic acid, (E)-2-β-d-glucopyranosyloxy-4-methoxycinnamic acid (trans-GMCA), quercetagetin-7-O-β-d-glucopyranoside, luteolin-7-O-β-d-glucoside, apigenin-7-O-β-d-glucoside, chamaemeloside, apigenin 7-O-(6″-O-acetyl-β-d-glucopyranoside), apigenin] and one polyacetylene (tonghaosu) from the flower heads of Chamomile/Chrysanthemum samples. The chromatographic separation was achieved using a reversed phase C18 column with a mobile phase of water and acetonitrile, both containing 0.05% formic acid. The ten compounds were completely separated within 15min at a flow rate of 0.25mL/min with a 2μL injection volume. The different chemo-types of Chamomiles/Chrysanthemum displayed variations in the presence of chemical constituents. German Chamomile samples confirmed the presence of cis-GMCA, trans-GMCA, apigenin-7-O-β-d-glucoside and tonghaosu as major constituents whereas Roman chamomile samples confirmed the presence of chamamaeloside and apigenin as major compounds. The Chrysanthemum morifolium samples showed the presence of luteolin-7-O-β-d-glucose as the major compound. The method was applied for the analysis of various commercial products including capsules, tea bags, body and hair care products. LC-mass spectrometry with electrospray ionization (ESI) interface method is described for the evaluation of ten compounds in plant samples and commercial products. This method involved the detection of [M+Na](+) and [M+H](+) ions in the positive mode. Partial least squares discriminant analysis (PLS-DA) was used to visualize commercial samples quality and may be of value for discriminating between chamomile types and Chrysanthemum with regards to the relative content of individual constituents. The results indicated that the method is suitable as a quality control test for various Chamomile/Chrysanthemum samples and market products. Copyright © 2013 Elsevier B.V. All rights reserved.

Metabolic reprogramming of the urea cycle pathway in experimental pulmonary arterial hypertension rats induced by monocrotaline.

PubMed

Zheng, Hai-Kuo; Zhao, Jun-Han; Yan, Yi; Lian, Tian-Yu; Ye, Jue; Wang, Xiao-Jian; Wang, Zhe; Jing, Zhi-Cheng; He, Yang-Yang; Yang, Ping

2018-05-11

Pulmonary arterial hypertension (PAH) is a rare systemic disorder associated with considerable metabolic dysfunction. Although enormous metabolomic studies on PAH have been emerging, research remains lacking on metabolic reprogramming in experimental PAH models. We aim to evaluate the metabolic changes in PAH and provide new insight into endogenous metabolic disorders of PAH. A single subcutaneous injection of monocrotaline (MCT) (60 mg kg - 1 ) was used for rats to establish PAH model. Hemodynamics and right ventricular hypertrophy were adopted to evaluate the successful establishment of PAH model. Plasma samples were assessed through targeted metabolomic profiling platform to quantify 126 endogenous metabolites. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to discriminate between MCT-treated model and control groups. Metabolite Set Enrichment Analysis was adapted to exploit the most disturbed metabolic pathways. Endogenous metabolites of MCT treated PAH model and control group were well profiled using this platform. A total of 13 plasma metabolites were significantly altered between the two groups. Metabolite Set Enrichment Analysis highlighted that a disruption in the urea cycle pathway may contribute to PAH onset. Moreover, five novel potential biomarkers in the urea cycle, adenosine monophosphate, urea, 4-hydroxy-proline, ornithine, N-acetylornithine, and two candidate biomarkers, namely, O-acetylcarnitine and betaine, were found to be highly correlated with PAH. The present study suggests a new role of urea cycle disruption in the pathogenesis of PAH. We also found five urea cycle related biomarkers and another two candidate biomarkers to facilitate early diagnosis of PAH in metabolomic profile.

The influence of a sports drink on the postexercise metabolism of elite athletes as investigated by NMR-based metabolomics.

PubMed

Miccheli, Alfredo; Marini, Federico; Capuani, Giorgio; Miccheli, Alberta Tomassini; Delfini, Maurizio; Di Cocco, Maria Enrica; Puccetti, Caterina; Paci, Maurizio; Rizzo, Marta; Spataro, Antonio

2009-10-01

The aim of this study is to evaluate the systemic effects of an isotonic sports drink on the metabolic status of athletes of the Italian Olympic rowing team during recovery after strenuous and prolonged physical exercise by means of nuclear magnetic resonance (NMR)-based metabolomics analysis on plasma and urine. Forty-four male athletes of the Italian Olympic rowing team were enrolled in a double-blind crossover study. All subjects underwent 2 evaluations at 1-week intervals. The evaluation was performed on a rowing ergometer after strenuous physical exercise to produce a state of dehydration. Afterward, the athletes were rehydrated either with a green tea-based carbohydrate-hydroelectrolyte drink or with oligomineral water. Three blood samples were drawn for each subject: at rest, after the exercise, and following rehydratation, while 2 urine samples were collected: at rest and after the rehydratation period. Biofluid samples were analyzed by high-resolution (1)H NMR metabolic profiling combined with multilevel simultaneous data-analysis (MSCA) and partial-least squares-discriminant analysis (PLS-DA). The between-subject variations, as evaluated by MSCA, reflected the variations of lactate levels induced by the physical exercise. Analysis of the within-individual variance using multilevel PLS-DA models of plasma and urine metabolic profiles showed an effect of the green tea-based sports drink on glucose, citrate, and lactate levels in plasma and on acetone, 3-OH-butyrate, and lactate levels in urine. The increase of caffeine and hippuric acid levels in urine indicated the absorption of green tea extract components. NMR-based metabolomics allowed the complex effects of a green tea extract-based carbohydrate/hydroelectrolyte beverage on the energy metabolism of athletes during recovery by postexercise rehydration to be evaluated.

Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus.

PubMed

Monteyne, Tinne; Coopman, Renaat; Kishabongo, Antoine S; Himpe, Jonas; Lapauw, Bruno; Shadid, Samyah; Van Aken, Elisabeth H; Berenson, Darja; Speeckaert, Marijn M; De Beer, Thomas; Delanghe, Joris R

2018-05-11

Glycated keratin allows the monitoring of average tissue glucose exposure over previous weeks. In the present study, we wanted to explore if near-infrared (NIR) spectroscopy could be used as a non-invasive diagnostic tool for assessing glycation in diabetes mellitus. A total of 52 patients with diabetes mellitus and 107 healthy subjects were enrolled in this study. A limited number (n=21) of nails of healthy subjects were glycated in vitro with 0.278 mol/L, 0.556 mol/L and 0.833 mol/L glucose solution to study the effect of glucose on the nail spectrum. Consequently, the nail clippings of the patients were analyzed using a Thermo Fisher Antaris II Near-IR Analyzer Spectrometer and near infrared (NIR) chemical imaging. Spectral classification (patients with diabetes mellitus vs. healthy subjects) was performed using partial least square discriminant analysis (PLS-DA). In vitro glycation resulted in peak sharpening between 4300 and 4400 cm-1 and spectral variations at 5270 cm-1 and between 6600 and 7500 cm-1. Similar regions encountered spectral deviations during analysis of the patients' nails. Optimization of the spectral collection parameters was necessary in order to distinguish a large dataset. Spectra had to be collected at 16 cm-1, 128 scans, region 4000-7500 cm-1. Using standard normal variate, Savitsky-Golay smoothing (7 points) and first derivative preprocessing allowed for the prediction of the test set with 100% correct assignments utilizing a PLS-DA model. Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.

Peer Deviance, Parental Divorce, and Genetic Risk in the Prediction of Drug Abuse in a Nationwide Swedish Sample

PubMed Central

Kendler, Kenneth S.; Ohlsson, Henrik; Sundquist, Kristina; Sundquist, Jan

2014-01-01

IMPORTANCE Peer deviance (PD) strongly predicts externalizing psychopathologic conditions but has not been previously assessable in population cohorts. We sought to develop such an index of PD and to clarify its effects on risk of drug abuse (DA). OBJECTIVES To examine how strongly PD increases the risk of DA and whether this community-level liability indicator interacts with key DA risk factors at the individual and family levels. DESIGN, SETTING, AND PARTICIPANTS Studies of future DA registration in 1 401 698 Swedish probands born from January 1, 1970, through December 31, 1985, and their adolescent peers in approximately 9200 small community areas. Peer deviance was defined as the proportion of individuals born within 5 years of the proband living in the same small community when the proband was 15 years old who eventually were registered for DA. MAIN OUTCOMES AND MEASURES Drug abuse recorded in medical, legal, or pharmacy registry records. RESULTS Peer deviance was associated with future DA in the proband, with rates of DA in older and male peers more strongly predictive than in younger or female peers. The predictive power of PD was only slightly attenuated by adding measures of community deprivation, collective efficacy, or family socioeconomic status. Probands whose parents were divorced were more sensitive to the pathogenic effects of high PD environments. A robust positive interaction was also seen between genetic risk of DA (indexed by rates of DA in first-, second-, and third-degree relatives) and PD exposure. CONCLUSIONS AND RELEVANCE With sufficient data, PD can be measured in populations and strongly predicts DA. In a nationwide sample, risk factors at the level of the individual (genetic vulnerability), family (parental loss), and community (PD) contribute substantially to risk of DA. Individuals at elevated DA risk because of parental divorce or high genetic liability are more sensitive to the pathogenic effects of PD. Although the effect of our PD measure on DA liability cannot be explained by standard measures of community or family risk, we cannot, with available data, discriminate definitively between the effect of true peer effects and other unmeasured risk factors. PMID:24576925

Peer deviance, parental divorce, and genetic risk in the prediction of drug abuse in a nationwide Swedish sample: evidence of environment-environment and gene-environment interaction.

PubMed

Kendler, Kenneth S; Ohlsson, Henrik; Sundquist, Kristina; Sundquist, Jan

2014-04-01

Peer deviance (PD) strongly predicts externalizing psychopathologic conditions but has not been previously assessable in population cohorts. We sought to develop such an index of PD and to clarify its effects on risk of drug abuse (DA). To examine how strongly PD increases the risk of DA and whether this community-level liability indicator interacts with key DA risk factors at the individual and family levels. Studies of future DA registration in 1,401,698 Swedish probands born from January 1, 1970, through December 31, 1985, and their adolescent peers in approximately 9200 small community areas. Peer deviance was defined as the proportion of individuals born within 5 years of the proband living in the same small community when the proband was 15 years old who eventually were registered for DA. Drug abuse recorded in medical, legal, or pharmacy registry records. Peer deviance was associated with future DA in the proband, with rates of DA in older and male peers more strongly predictive than in younger or female peers. The predictive power of PD was only slightly attenuated by adding measures of community deprivation, collective efficacy, or family socioeconomic status. Probands whose parents were divorced were more sensitive to the pathogenic effects of high PD environments. A robust positive interaction was also seen between genetic risk of DA (indexed by rates of DA in first-, second-, and third-degree relatives) and PD exposure. With sufficient data, PD can be measured in populations and strongly predicts DA. In a nationwide sample, risk factors at the level of the individual (genetic vulnerability), family (parental loss), and community (PD) contribute substantially to risk of DA. Individuals at elevated DA risk because of parental divorce or high genetic liability are more sensitive to the pathogenic effects of PD. Although the effect of our PD measure on DA liability cannot be explained by standard measures of community or family risk, we cannot, with available data, discriminate definitively between the effect of true peer effects and other unmeasured risk factors.

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems.

PubMed

Jácome, Gabriel; Valarezo, Carla; Yoo, Changkyoo

2018-03-30

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.

Developmental cigarette smoke exposure II: Hepatic proteome profiles in 6 month old adult offspring.

PubMed

Neal, Rachel E; Chen, Jing; Webb, Cindy; Stocke, Kendall; Gambrell, Caitlin; Greene, Robert M; Pisano, M Michele

2016-10-01

Utilizing a mouse model of 'active' developmental cigarette smoke exposure (CSE) [gestational day (GD) 1 through postnatal day (PD) 21] characterized by offspring low birth weight, the impact of developmental CSE on liver proteome profiles of adult offspring at 6 months of age was determined. Liver tissue was collected from Sham- and CSE-offspring for 2D-SDS-PAGE based proteome analysis with Partial Least Squares-Discriminant Analysis (PLS-DA). A similar study conducted at the cessation of exposure to cigarette smoke documented decreased gluconeogenesis coupled to oxidative stress in weanling offspring. In the current study, exposure throughout development to cigarette smoke resulted in impaired hepatic carbohydrate metabolism, decreased serum glucose levels, and increased gluconeogenic regulatory enzyme abundances during the fed-state coupled to decreased expression of SIRT1 as well as increased PEPCK and PGC1α expression. Together these findings indicate inappropriately timed gluconeogenesis that may reflect impaired insulin signaling in mature offspring exposed to 'active' developmental CSE. Copyright © 2016 Elsevier Inc. All rights reserved.

Identification and Characterization of Diploid and Tetraploid in Platycodon grandiflorum.

PubMed

Shin, Jeoung-Hwa; Ahn, Yun Gyong; Jung, Ju-Hee; Woo, Sun-Hee; Kim, Hag-Hyun; Gorinstein, Shela; Boo, Hee-Ock

2017-03-01

Platycodon grandiflorum (PG), a species of herbaceous flowering perennial plant of the family Campanulaceae, has been used as a traditional oriental medicine for bronchitis, asthma, pulmonary tuberculosis, diabetes, hepatic fibrosis, bone disorders and many others similar diseases and as a food supplement. For the primary profiling of PG gas chromatography coupled with high resolution - time of flight mass spectrometry (GC/HR-TOF MS) was used as an analytical tool. A comparison of optimal extraction of metabolites was carried out with a number of solvents [hexane, methylene chloride, methanol, ethanol, methanol: ethanol (70:30, v:v)]. In extracts with methanol: ethanol (70:30 v:v) were detected higher amounts of metabolites than with other solvents. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) plots showed significant differences between the diploid and tetraploid metabolite profiles. Extracts of tetraploid showed higher amounts of amino acids, while extracts of diploid contained more organic acids and sugars. Graphical Abstract ᅟ.

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study.

PubMed

Ritota, Mena; Casciani, Lorena; Valentini, Massimiliano

2013-05-01

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research. Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin. Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin. © 2012 Society of Chemical Industry.

UHPLC/Q-TOF MS-based plasma metabolic profiling analysis of the bleeding mechanism in a rat model of yeast and ethanol-induced blood heat and hemorrhage syndrome.

PubMed

Shang, Jing; Liu, Jia; He, Mu; Shang, Erxin; Zhang, Li; Shan, Mingqiu; Yao, Weifeng; Yu, Bing; Yao, Yingzhi; Ding, Anwei

2014-04-01

Blood heat and hemorrhage (BHH) syndrome is the most common bleeding disease in clinic. In this study, a rat model with BHH syndrome was built for the first time. Biochemical study showed the intrinsic coagulation pathways and the platelet aggregation rate in the rat model were inhibited, while extrinsic pathway of coagulation cascade was activated. An UHPLC/Q-TOF MS combined with orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to construct plasma metabolic profiling of the rat model with BHH syndrome. Twenty-four unique metabolites were identified, which were involved in glycerophospholipid metabolism, arachidonic acid metabolism, fatty acid metabolism, amino acid metabolism and cholic acid metabolism. In the end, we concluded that bleeding mechanism of the rat with BHH syndrome may be associated with augmenting blood viscosity, inhibiting platelet aggregation and intrinsic coagulation pathways. Copyright © 2013 Elsevier B.V. All rights reserved.

123I-FP-CIT SPECT imaging in early diagnosis of dementia in patients with and without a vascular component

PubMed Central

Garriga, Marina; Milà, Marta; Mir, Manzoor; Al-Baradie, Raid; Huertas, Sonia; Castejon, Cesar; Casas, Laura; Badenes, Dolors; Giménez, Nuria; Font, M. Angels; Gonzalez, Jose M.; Ysamat, Maria; Aguilar, Miguel; Slevin, Mark; Krupinski, Jerzy

2015-01-01

Alzheimer’s disease (AD) and vascular dementia (VaD) are the most common cause of dementia. Cerebral ischemia is a major risk factor for development of dementia. 123I-FP-CIT SPECT (DaTScan) is a complementary tool in the differential diagnoses of patients with incomplete or uncertain Parkinsonism. Additional application of DaTScan enables the categorization of Parkinsonian disease with dementia (PDD), and its differentiation from pure AD, and may further contribute to change the therapeutic decision. The aim of this study was to analyze the vascular contribution towards dementia and mild cognitive impairment (MCI). We evaluated the utility of DaTScan for the early diagnosis of dementia in patients with and without a clinical vascular component, and the association between neuropsychological function, vascular component and dopaminergic function on DaTScan. One-hundred and five patients with MCI or the initial phases of dementia were studied prospectively. We developed an initial assessment using neurologic examination, blood tests, cognitive function tests, structural neuroimaging and DaTScan. The vascular component was later quantified in two ways: clinically, according to the Framingham Risk Score (FRS) and by structural neuroimaging using Wahlund Scale Total Score (WSTS). Early diagnosis of dementia was associated with an abnormal DaTScan. A significant association was found between a high WSTS and an abnormal DaTScan (p < 0.01). Mixed AD was the group with the highest vascular component, followed by the VaD group, while MCI and pure AD showed similar WSTS. No significant associations were found between neuropsychological impairment and DaTScan independently of associated vascular component. DaTScan seems to be a good tool to discriminate, in a first clinical assessment, patients with MCI from those with established dementia. There was bigger general vascular affectation observable in MRI or CT in patients with abnormal dopaminergic uptake seen on DaTScan. PMID:26190980

Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases.

PubMed

Dawiskiba, Tomasz; Deja, Stanisław; Mulak, Agata; Ząbek, Adam; Jawień, Ewa; Pawełka, Dorota; Banasik, Mirosław; Mastalerz-Migas, Agnieszka; Balcerzak, Waldemar; Kaliszewski, Krzysztof; Skóra, Jan; Barć, Piotr; Korta, Krzysztof; Pormańczuk, Kornel; Szyber, Przemyslaw; Litarski, Adam; Młynarz, Piotr

2014-01-07

To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases (IBD). Serum and urine samples were collected from 24 patients with ulcerative colitis (UC), 19 patients with the Crohn's disease (CD) and 17 healthy controls. The activity of UC was assessed with the Simple Clinical Colitis Activity Index, while the activity of CD was determined using the Harvey-Bradshaw Index. The analysis of serum and urine samples was performed using proton nuclear magnetic resonance (NMR) spectroscopy. All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine. Prior to the chemometric analysis, both data sets were unit variance scaled. The differences in metabolite fingerprints were assessed using partial least-squares-discriminant analysis (PLS-DA). Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models. Metabolites responsible for separation in models were tested using STATISTICA 10 with the Mann-Whitney-Wilcoxon test and the Student's t test (α = 0.05). The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models (P value 0.002 for serum and 0.003 for urine). The metabolites that allowed to distinguish these groups were: N-acetylated compounds and phenylalanine (up-regulated in serum), low-density lipoproteins and very low-density lipoproteins (decreased in serum) as well as glycine (increased in urine) and acetoacetate (decreased in urine). The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation (P value < 0.001 for serum and 0.003 for urine). The metabolites that were found to be the strongest biomarkers included in this case: leucine, isoleucine, 3-hydroxybutyric acid, N-acetylated compounds, acetoacetate, glycine, phenylalanine and lactate (increased in serum), creatine, dimethyl sulfone, histidine, choline and its derivatives (decreased in serum), as well as citrate, hippurate, trigonelline, taurine, succinate and 2-hydroxyisobutyrate (decreased in urine). No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples, although one metabolite (formate) in univariate statistical analysis was significantly lower in serum of patients with active CD, and two metabolites (alanine and N-acetylated compounds) were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases. Contrary to the results obtained from the serum samples, the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects. The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate, hippurate, taurine, succinate, glycine, alanine and formate. NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission.

Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases

PubMed Central

Dawiskiba, Tomasz; Deja, Stanisław; Mulak, Agata; Ząbek, Adam; Jawień, Ewa; Pawełka, Dorota; Banasik, Mirosław; Mastalerz-Migas, Agnieszka; Balcerzak, Waldemar; Kaliszewski, Krzysztof; Skóra, Jan; Barć, Piotr; Korta, Krzysztof; Pormańczuk, Kornel; Szyber, Przemyslaw; Litarski, Adam; Młynarz, Piotr

2014-01-01

AIM: To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases (IBD). METHODS: Serum and urine samples were collected from 24 patients with ulcerative colitis (UC), 19 patients with the Crohn’s disease (CD) and 17 healthy controls. The activity of UC was assessed with the Simple Clinical Colitis Activity Index, while the activity of CD was determined using the Harvey-Bradshaw Index. The analysis of serum and urine samples was performed using proton nuclear magnetic resonance (NMR) spectroscopy. All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine. Prior to the chemometric analysis, both data sets were unit variance scaled. The differences in metabolite fingerprints were assessed using partial least-squares-discriminant analysis (PLS-DA). Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models. Metabolites responsible for separation in models were tested using STATISTICA 10 with the Mann-Whitney-Wilcoxon test and the Student’s t test (α = 0.05). RESULTS: The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models (P value 0.002 for serum and 0.003 for urine). The metabolites that allowed to distinguish these groups were: N-acetylated compounds and phenylalanine (up-regulated in serum), low-density lipoproteins and very low-density lipoproteins (decreased in serum) as well as glycine (increased in urine) and acetoacetate (decreased in urine). The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation (P value < 0.001 for serum and 0.003 for urine). The metabolites that were found to be the strongest biomarkers included in this case: leucine, isoleucine, 3-hydroxybutyric acid, N-acetylated compounds, acetoacetate, glycine, phenylalanine and lactate (increased in serum), creatine, dimethyl sulfone, histidine, choline and its derivatives (decreased in serum), as well as citrate, hippurate, trigonelline, taurine, succinate and 2-hydroxyisobutyrate (decreased in urine). No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples, although one metabolite (formate) in univariate statistical analysis was significantly lower in serum of patients with active CD, and two metabolites (alanine and N-acetylated compounds) were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases. Contrary to the results obtained from the serum samples, the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects. The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate, hippurate, taurine, succinate, glycine, alanine and formate. CONCLUSION: NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission. PMID:24415869

A litmus-type colorimetric and fluorometric volatile organic compound sensor based on inkjet-printed polydiacetylenes on paper substrates.

PubMed

Yoon, Bora; Park, In Sung; Shin, Hyora; Park, Hye Jin; Lee, Chan Woo; Kim, Jong-Man

2013-05-14

Inkjet-printed paper-based volatile organic compound (VOC) sensor strips imaged with polydiacetylenes (PDAs) are developed. A microemulsion ink containing bisurethane-substituted diacetylene (DA) monomers, 4BCMU, was inkjet printed onto paper using a conventional inkjet office printer. UV irradiation of the printed image allowed fabrication of blue-colored poly-4BCMU on the paper and the polymer was found to display colorimetric responses to VOCs. Interestingly, a blue-to-yellow color change was observed when the strip was exposed to chloroform vapor, which was accompanied by the generation of green fluorescence. The principal component analysis plot of the color and fluorescence images of the VOC-exposed polymers allowed a more precise discrimination of VOC vapors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prospective pilot study to detect dogs with non food-induced canine atopic dermatitis using Fourier transform infrared spectroscopy.

PubMed

Bruet, Vincent; Dumon, Henri; Bourdeau, Patrick; Desfontis, Jean-Claude; Martin, Lucile

2016-10-01

The diagnosis of canine atopic dermatitis (CAD) remains challenging due to the lack of a simple biomarker or metabolic profile. In human medicine, Fourier transform infrared spectroscopy (FTIR) is an analytical technique used for several diseases. It requires a small amount of sample and allows the identification of structural moieties of biomolecules on the basis of their infrared absorption, with limited sample pretreatment. The aim of the study was to evaluate the diagnostic value of FTIR. Three groups were tested: 21 dogs with non food-induced CAD (NFICAD), 16 dogs with inflammatory conditions of various origins but without allergic dermatoses (OD) and 10 healthy dogs (H). Peripheral blood was collected and spectra were acquired with a FTIR spectrophotometer. A principal component analysis (PCA) was performed on the full wavenumber spectra (4000-600/cm), followed by a Fisher discriminant analysis (DA) to assess the differences between the three groups. The PCA followed by the DA of whole spectra showed significant differences between the three groups. These results suggest that by using the FTIR method, dogs with NFICAD can be differentiated from healthy dogs and dogs with nonallergic inflammation. There was no overlap between the spectral data of the three groups indicating that NFICAD dogs were correctly segregated from the H and OD groups. A study on a larger cohort including common pruritic skin diseases is necessary to confirm these initial results and the relevance of this diagnostic technique. © 2016 ESVD and ACVD.

Effects of soil type and organic fertilizers on fatty acids and vitamin E in Korean ginseng (Panax ginseng Meyer).

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Yang, Jin-Hee; Lee, Ji-Hee; Park, Sung-Kyu; Son, Na-Young; Kim, Seung-Hyun

2017-12-01

This study examined the effects of soil type and fertilizer regimes on variations in fatty acids (FAs) and vitamin E (Vit-E) in 6-year-old ginseng roots. We observed significant variation in both FA and Vit-E contents owing to the type and quantity of organic fertilizer used in each soil type during cultivation. Unsaturated FAs were approximately 2.7-fold higher in ginseng than in saturated FAs. Linoleic, palmitic, and oleic acids were the most abundant FAs detected in ginseng roots. Additionally, α-tocopherol was the major Vit-E detected. In particular, the increased application of rice straw compost or food waste fertilizer elevated the quantity of nutritionally desirable FAs and bioactive Vit-E in ginseng root. Partial least square-discriminant analysis (PLS-DA) score plots showed that soil type might be the main cause of differences in FA and Vit-E levels in ginseng. Specifically, the PLS-DA model indicated that palmitic acid is a suitable FA marker in determining whether ginseng plants were grown in a paddy-converted field or an upland field. Moreover, linoleic acid levels were highly correlated with α-linolenic acid (r=0.8374; p<0.0001) according to Pearson's correlations and hierarchical clustering analysis. Hence, these preliminary results should prove useful for the reliable production of ginseng containing high phytonutrient quantities according to cultivation conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic

PubMed Central

de Groot, Maartje H.; van Campen, Jos P.; Beijnen, Jos H.; Hortobágyi, Tibor; Vuillerme, Nicolas; Lamoth, Claudine C. J.

2017-01-01

Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares–Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified ‘pace’, ‘variability’, and ‘coordination’ as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients’ fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics. PMID:28575126

Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic.

PubMed

Kikkert, Lisette H J; de Groot, Maartje H; van Campen, Jos P; Beijnen, Jos H; Hortobágyi, Tibor; Vuillerme, Nicolas; Lamoth, Claudine C J

2017-01-01

Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares-Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified 'pace', 'variability', and 'coordination' as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients' fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics.

Metabolomic changes in follicular fluid induced by soy isoflavones administered to rats from weaning until sexual maturity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Wenxiang; Zhang, Wenchang, E-mail: wenchang2002@sina.com; Liu, Jin

Female Wistar rats at 21 days of age were treated with one of three concentrations of soy isoflavones (SIF) (50, 100 or 200 mg/kg body weight, orally, once per day) from weaning until sexual maturity (3 months) in order to evaluate the influence of SIF on ovarian follicle development. After treatment, the serum sex hormone levels and enumeration of ovarian follicles of the ovary were measured. The metabolic profile of follicular fluid was determined using HPLC-MS. Principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was used to identify differences in metabolites and reveal useful toxic biomarkers. The results indicatedmore » that modest doses of SIF affect ovarian follicle development, as demonstrated by decreased serum estradiol levels and increases in both ovarian follicle atresia and corpora lutea number in the ovary. SIF treatment-related metabolic alterations in follicular fluid were also found in the PCA and PLS-DA models. The 24 most significantly altered metabolites were identified, including primary sex hormones, amino acids, fatty acids and metabolites involved in energy metabolism. These findings may indicate that soy isoflavones affect ovarian follicle development by inducing metabolomic variations in the follicular fluid. - Highlights: ► Modest doses of soy isoflavones (SIF) do affect ovarian follicle development. ► SIF treatment-related metabolic alterations in follicular fluid were found. ► The 24 most significantly altered metabolites were identified.« less

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer.

PubMed

Lin, Yan; Ma, Changchun; Liu, Chengkang; Wang, Zhening; Yang, Jurong; Liu, Xinmu; Shen, Zhiwei; Wu, Renhua

2016-05-17

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

PubMed Central

Lin, Yan; Ma, Changchun; Liu, Chengkang; Wang, Zhening; Yang, Jurong; Liu, Xinmu; Shen, Zhiwei; Wu, Renhua

2016-01-01

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients. PMID:27107423

Chemical and genetic discrimination of Cistanches Herba based on UPLC-QTOF/MS and DNA barcoding.

PubMed

Zheng, Sihao; Jiang, Xue; Wu, Labin; Wang, Zenghui; Huang, Linfang

2014-01-01

Cistanches Herba (Rou Cong Rong), known as "Ginseng of the desert", has a striking curative effect on strength and nourishment, especially in kidney reinforcement to strengthen yang. However, the two plant origins of Cistanches Herba, Cistanche deserticola and Cistanche tubulosa, vary in terms of pharmacological action and chemical components. To discriminate the plant origin of Cistanches Herba, a combined method system of chemical and genetic--UPLC-QTOF/MS technology and DNA barcoding--were firstly employed in this study. The results indicated that three potential marker compounds (isomer of campneoside II, cistanoside C, and cistanoside A) were obtained to discriminate the two origins by PCA and OPLS-DA analyses. DNA barcoding enabled to differentiate two origins accurately. NJ tree showed that two origins clustered into two clades. Our findings demonstrate that the two origins of Cistanches Herba possess different chemical compositions and genetic variation. This is the first reported evaluation of two origins of Cistanches Herba, and the finding will facilitate quality control and its clinical application.

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy.

PubMed

Kuligowski, Julia; Carrión, David; Quintás, Guillermo; Garrigues, Salvador; de la Guardia, Miguel

2011-01-01

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy.

PubMed

da Silva, Neirivaldo C; Pimentel, Maria Fernanda; Honorato, Ricardo S; Talhavini, Marcio; Maldaner, Adriano O; Honorato, Fernanda A

2015-08-01

The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Unique plasma metabolomic signatures of individuals with inherited disorders of long-chain fatty acid oxidation

PubMed Central

McCoin, Colin S.; Piccolo, Brian D.; Knotts, Trina A.; Matern, Dietrich; Vockley, Jerry; Gillingham, Melanie B.; Adams, Sean H.

2016-01-01

Blood and urine acylcarnitine profiles are commonly used to diagnose long-chain fatty acid oxidation disorders (FAOD: i.e., long-chain hydroxy-acyl-CoA dehydrogenase [LCHAD] and carnitine palmitoyltransferase 2 [CPT2] deficiency), but the global metabolic impact of long-chain FAOD has not been reported. We utilized untargeted metabolomics to characterize plasma metabolites in 12 overnight-fasted individuals with FAOD (10 LCHAD, 2 CPT2) and 11 healthy age-, sex-, and body mass index (BMI)-matched controls, with the caveat that individuals with FAOD consume a low-fat diet supplemented with medium-chain triglycerides (MCT) while matched controls consume a typical American diet. 832 metabolites were identified in plasma, and partial least squared-discriminant analysis (PLS-DA) identified 114 non-acylcarnitine variables that discriminated FAOD subjects and controls. FAOD individuals had significantly higher triglycerides and lower specific phosphatidylethanolamines, ceramides and sphingomyelins. Differences in phosphatidylcholines were also found but the directionality differed by species. Further, there were few differences in non-lipid metabolites indicating the metabolic impact of FAOD specifically on lipid pathways. This analysis provides evidence that LCHAD/CPT2 deficiency significantly alters complex lipid pathway flux. This metabolic signature may provide powerful clinical tools capable of confirming or diagnosing FAOD, even in subjects with a mild phenotype, and provide clues regarding the biochemical and metabolic impact of FAOD that could be relevant to the etiology of FAOD symptoms. PMID:26907176

Investigation of the Hepatoprotective Effect of Prunus mume Sieb. et Zucc Extract in a Mouse Model of Alcoholic Liver Injury Through High-Resolution Metabolomics.

PubMed

Khan, Adnan; Pan, Jeong Hoon; Cho, Seongha; Lee, Sojung; Kim, Young Jun; Park, Youngja H

2017-08-01

This study aimed to identify the changes in the metabolomics profile of liver damage caused by alcohol consumption and verify the beneficial effect of Prunus mume Sieb. et Zucc extract (PME) in protection of alcohol-induced injury by attenuating the level of identified metabolites. Mice were treated with PME and saline or untreated once daily for 5 days, followed by alcohol injection. The plasma samples were analyzed using liquid chromatography-mass spectrometry-based high-resolution metabolomics followed by a multivariate statistical analysis using MetaboAnalyst 3.0 to obtain significantly expressed metabolites, using a false discovery rate threshold of q = 0.05. Metabolites were annotated using Metlin database and mapped through Kyoto Encyclopedia of Genes and Genomes (KEGG). Among 4999 total features, 101 features were significant among alcohol- and PME-treated mice groups. All the samples cluster showed a clear separation in the heat map, and the scores plot of orthogonal partial least squares-discriminant analysis (OPLS-DA) model discriminated the three groups. Phosphatidylcholine, Saikosaponin BK1, Ganoderiol I, and N-2-[4-(3,3-dimethylallyloxy) phenyl] ethylcinnamide were among the significant compounds with a low intensity in alcohol group compared to PME group, suggesting that these compounds have a relation in the development of PME's protective effect. The study confirms the hepatoprotective, antioxidant, and anti-inflammatory effects of PME against alcohol-induced liver steatosis, inflammation, and apoptosis.

Profiling of ARDS pulmonary edema fluid identifies a metabolically distinct subset.

PubMed

Rogers, Angela J; Contrepois, Kévin; Wu, Manhong; Zheng, Ming; Peltz, Gary; Ware, Lorraine B; Matthay, Michael A

2017-05-01

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This "high metabolite" endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS. Copyright © 2017 the American Physiological Society.

Profiling of ARDS pulmonary edema fluid identifies a metabolically distinct subset

PubMed Central

Contrepois, Kévin; Wu, Manhong; Zheng, Ming; Peltz, Gary; Ware, Lorraine B.; Matthay, Michael A.

2017-01-01

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This “high metabolite” endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS. PMID:28258106

New milk protein-derived peptides with potential antimicrobial activity: an approach based on bioinformatic studies.

PubMed

Dziuba, Bartłomiej; Dziuba, Marta

2014-08-20

New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs) from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM), random forest (RF), artificial neural networks (ANN) and discriminant analysis (DA) available in the Collection of Anti-Microbial Peptides (CAMP database). Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins.

New Milk Protein-Derived Peptides with Potential Antimicrobial Activity: An Approach Based on Bioinformatic Studies

PubMed Central

Dziuba, Bartłomiej; Dziuba, Marta

2014-01-01

New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs) from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM), random forest (RF), artificial neural networks (ANN) and discriminant analysis (DA) available in the Collection of Anti-Microbial Peptides (CAMP database). Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins. PMID:25141106

Development and Testing of an LED-Based Near-Infrared Sensor for Human Kidney Tumor Diagnostics

PubMed Central

Zabarylo, Urszula; Kirsanov, Dmitry; Belikova, Valeria; Ageev, Vladimir; Usenov, Iskander; Galyanin, Vladislav; Minet, Olaf; Sakharova, Tatiana; Danielyan, Georgy; Feliksberger, Elena; Artyushenko, Viacheslav

2017-01-01

Optical spectroscopy is increasingly used for cancer diagnostics. Tumor detection feasibility in human kidney samples using mid- and near-infrared (NIR) spectroscopy, fluorescence spectroscopy, and Raman spectroscopy has been reported (Artyushenko et al., Spectral fiber sensors for cancer diagnostics in vitro. In Proceedings of the European Conference on Biomedical Optics, Munich, Germany, 21–25 June 2015). In the present work, a simplification of the NIR spectroscopic analysis for cancer diagnostics was studied. The conventional high-resolution NIR spectroscopic method of kidney tumor diagnostics was replaced by a compact optical sensing device constructively represented by a set of four light-emitting diodes (LEDs) at selected wavelengths and one detecting photodiode. Two sensor prototypes were tested using 14 in vitro clinical samples of 7 different patients. Statistical data evaluation using principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) confirmed the general applicability of the LED-based sensing approach to kidney tumor detection. An additional validation of the results was performed by means of sample permutation. PMID:28825612

[Study on three different species tibetan medicine sea buckthorn by 1H-NMR-based metabonomics].

PubMed

Su, Yong-Wen; Tan, Er; Zhang, Jing; You, Jia-Li; Liu, Yue; Liu, Chuan; Zhou, Xiang-Dong; Zhang, Yi

2014-11-01

The 1H-NMR fingerprints of three different species tibetan medicine sea buckthorn were established by 1H-HMR metabolomics to find out different motablism which could provide a new method for the quality evaluation of sea buckthorn. The obtained free induction decay (FID) signal will be imported into MestReNova software and into divide segments. The data will be normalized and processed by principal component analysis and.partial least squares discriminant analysis to perform pattern recognition. The results showed that 25 metabolites belonging to different chemical types were detected from sea buckthorn,including flavonoids, triterpenoids, amino acids, carbohydrates, fatty acids, etc. PCA and PLS-DA analysis showed three different varietiest of sea buckthorn that can be clearly separated by the content of L-quebrachitol, malic acid and some unidentified sugars, which can be used as the differences metabolites of three species of sea buckthorn. 1H-NMR-based metabonomies method had a holistic characteristic with sample preparation and handling. The results of this study can offer an important reference for the species identification and quality control of sea buckthorn.

[Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry].

PubMed

Li, Meng-hua; Li, Jing-ming; Li, Jun-hui; Zhang, Lu-da; Zhao, Long-lian

2015-06-01

To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NIR) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NIR spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95.69% and validation set is 94.12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99.46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines.

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging.

PubMed

Lee, Hoonsoo; Kim, Moon S; Song, Yu-Rim; Oh, Chang-Sik; Lim, Hyoun-Sub; Lee, Wang-Hee; Kang, Jum-Soon; Cho, Byoung-Kwan

2017-03-01

There is a need to minimize economic damage by sorting infected seeds from healthy seeds before seeding. However, current methods of detecting infected seeds, such as seedling grow-out, enzyme-linked immunosorbent assays, the polymerase chain reaction (PCR) and the real-time PCR have a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to evaluate the potential of visible/near-infrared (Vis/NIR) hyperspectral imaging system for detecting bacteria-infected watermelon seeds. A hyperspectral Vis/NIR reflectance imaging system (spectral region of 400-1000 nm) was constructed to obtain hyperspectral reflectance images for 336 bacteria-infected watermelon seeds, which were then subjected to partial least square discriminant analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to classify bacteria-infected watermelon seeds from healthy watermelon seeds. The developed system detected bacteria-infected watermelon seeds with an accuracy > 90% (PLS-DA: 91.7%, LS-SVM: 90.5%), suggesting that the Vis/NIR hyperspectral imaging system is effective for quarantining bacteria-infected watermelon seeds. The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

LC-MS based metabolomics and chemometrics study of the toxic effects of copper on Saccharomyces cerevisiae.

PubMed

Farrés, Mireia; Piña, Benjamí; Tauler, Romà

2016-08-01

Copper containing fungicides are used to protect vineyards from fungal infections. Higher residues of copper in grapes at toxic concentrations are potentially toxic and affect the microorganisms living in vineyards, such as Saccharomyces cerevisiae. In this study, the response of the metabolic profiles of S. cerevisiae at different concentrations of copper sulphate (control, 1 mM, 3 mM and 6 mM) was analysed by liquid chromatography coupled to mass spectrometry (LC-MS) and multivariate curve resolution-alternating least squares (MCR-ALS) using an untargeted metabolomics approach. Peak areas of the MCR-ALS resolved elution profiles in control and in Cu(ii)-treated samples were compared using partial least squares regression (PLSR) and PLS-discriminant analysis (PLS-DA), and the intracellular metabolites best contributing to sample discrimination were selected and identified. Fourteen metabolites showed significant concentration changes upon Cu(ii) exposure, following a dose-response effect. The observed changes were consistent with the expected effects of Cu(ii) toxicity, including oxidative stress and DNA damage. This research confirmed that LC-MS based metabolomics coupled to chemometric methods are a powerful approach for discerning metabolomics changes in S. cerevisiae and for elucidating modes of toxicity of environmental stressors, including heavy metals like Cu(ii).

Application of metabonomic strategy to discover an unreported active ingredient in LiuWeiDiHuang pills suppressing beta-glucuronidase.

PubMed

Xie, Baogang; Zhang, Zhirong; Gong, Tao; Zhang, Ningning; Wang, Huiyun; Zou, Huiqing

2015-01-01

Identification of the bioactive ingredient from traditional Chinese medicine (TCM) remains a challenging task by traditional approach that focuses on chemical isolation coupled with biological activity screening. Here, we present a metabonomics-based approach for bioactive ingredient discovery in LiuWeiDiHuang pills (LWPs). First, a non-targeted high-performance liquid chromatography ultraviolet (HPLC-UV) profiling of rat urine was used to discriminate urinary profiling intervened by LWPs. Orthogonal partial least-squares discriminant analysis (OPLS-DA) revealed that eight chromatographic peaks made a significant contribution to the classification of the LWPs group and the control group. Five of these chromatographic peaks were successfully isolated and identified as hippurate, genistein (GT), daidzein (DZ), and glucuronide conjugate of GT and that of DZ by mass spectroscopy (MS). Subsequently, we found that LWPs significantly decreased the activity of intestinal β-glucuronidase by 18 % and exerted a dose-dependent inhibitory effect on rat liver lysosomal fraction, suggesting that LWPs were a β-glucuronidase inhibitor. In the end, by inhibiting β-glucuronidase-guided isolation, D-glucaro-1,4-lactone, a previously unreported ingredient of LWPs, was identified by MS, MS/MS, and nuclear magnetic resonance spectroscopy. Our findings indicated that metabonomics might increase research productivity toward the drug targets and/or bioactive compounds from TCM.

Overexpression of the Vitronectin V10 Subunit in Patients with Nonalcoholic Steatohepatitis: Implications for Noninvasive Diagnosis of NASH.

PubMed

Del Ben, Maria; Overi, Diletta; Polimeni, Licia; Carpino, Guido; Labbadia, Giancarlo; Baratta, Francesco; Pastori, Daniele; Noce, Valeria; Gaudio, Eugenio; Angelico, Francesco; Mancone, Carmine

2018-02-18

Nonalcoholic steatohepatitis (NASH) is the critical stage of nonalcoholic fatty liver disease (NAFLD). The persistence of necroinflammatory lesions and fibrogenesis in NASH is the leading cause of liver cirrhosis and, ultimately, hepatocellular carcinoma. To date, the histological examination of liver biopsies, albeit invasive, remains the means to distinguish NASH from simple steatosis (NAFL). Therefore, a noninvasive diagnosis by serum biomarkers is eagerly needed. Here, by a proteomic approach, we analysed the soluble low-molecular-weight protein fragments flushed out from the liver tissue of NAFL and NASH patients. On the basis of the assumption that steatohepatitis leads to the remodelling of the liver extracellular matrix (ECM), NASH-specific fragments were in silico analysed for their involvement in the ECM molecular composition. The 10 kDa C-terminal fragment of the ECM protein vitronectin (VTN) was then selected as a promising circulating biomarker in discriminating NASH. The analysis of sera of patients provided these major findings: the circulating VTN fragment (i) is overexpressed in NASH patients and positively correlates with the NASH activity score (NAS); (ii) originates from the disulfide bond reduction between the V10 and the V65 subunits. In conclusion, V10 determination in the serum could represent a reliable tool for the noninvasive discrimination of NASH from simple steatosis.

Metabolomic Profiling of Plasma Samples from Women with Recurrent Spontaneous Abortion.

PubMed

Li, XiaoCui; Yin, MingHong; Gu, JinPing; Hou, YanYan; Tian, FuJu; Sun, Feng

2018-06-13

BACKGROUND Gas chromatography coupled with mass spectrometry (GC-MS) and liquid chromatography coupled with mass spectrometry (LC-MS) metabolomics have been deployed to detect novel differential metabolites in cases with recurrent spontaneous abortion (RSA). MATERIAL AND METHODS Fifty patients who had recurrent spontaneous abortions (RSAs) and 51 control patients (age, gestational age, and body mass index (BMI) match) were enrolled in this study. Untargeted GC-MS and targeted LC-MS were combined to discover and validate the different metabolomic profiles between groups. Score plots of orthogonal partial least-squares discriminant analysis (OPLS-DA) clearly separated the RSA group from the control group. The variable importance in projection (VIP) generated in OPLS-DA processing represented the contribution to the discrimination of each metabolite ion between groups. Variables with a VIP >1 and P<0.05 were considered to be different variables. We also used MetaboAnalyst 3.0 to analyze the pathway impact of potential metabolite biomarkers. RESULTS Fifty-four metabolites were significantly different between the two groups, as indicated by a VIP >1 and P<0.05. The metabolic pathways involving glycine, serine, threonine (P=0.00529, impact=0.26), beta-alanine (P=0.0284, impact=0.27), and phenylalanine metabolism (P=0.0217, impact=0.17), along with the tricarboxylic acid (TCA) cycle (P=0.0113, impact=0.19) and the glycolysis pathway (P=0.037, impact=0.1) are obviously related to RSA. Verification by LC-MS showed that the concentration of lactic acid in RSA was higher than that in the control group (P<0.05), while the concentration of 5-methoxytryptamine was significantly lower in the RSA group (P<0.05). CONCLUSIONS In our study, untargeted GC-MS was used to detect disturbance of metabolism occurs in RSA and targeted LC-MS further was used to show that plasma concentrations of two metabolites (lactic acid and 5-methoxytryptamine) were different in the RSA compared to the control group.

Efficiency of different techniques to identify changes in land use

NASA Astrophysics Data System (ADS)

Zornoza, Raúl; Mateix-Solera, Jorge; Gerrero, César

2013-04-01

The need for the development of sensitive and efficient methodologies for soil quality evaluation is increasing. The ability to assess soil quality and identify key soil properties that serve as indicators of soil function is complicated by the multiplicity of physical, chemical and biological factors that control soil processes. In the mountain region of the Mediterranean Basin of Spain, almond trees have been cultivated in terraced orchards for centuries. These crops are immersed in the Mediterranean forest scenery, configuring a mosaic landscape where orchards are integrated in the forest masses. In the last decades, almond orchards are being abandoned, leading to an increase in vegetation cover, since abandoned fields are naturally colonized by the surrounded natural vegetation. Soil processes and properties are expected to be associated with vegetation successional dynamics. Thus, the establishment of suitable parameters to monitor soil quality related to land use changes is particularly important to guarantee the regeneration of the mature community. In this study, we selected three land uses, constituted by forest, almond trees orchards, and orchards abandoned between 10 and 15 years previously to sampling. Sampling was carried out in four different locations in SE Spain. The main purpose was to evaluate if changes in management have significantly influenced different sets of soil characteristics. For this purpose, we used a discriminant analysis (DA). The different sets of soil characteristics tested in this study were 1: physical, chemical and biochemical properties; 2: soil near infrared (NIR) spectra; and 3: phospholipid fatty acids (PLFAs). After the DA performed with the sets 1 and 2, the three land uses were clearly separated by the two first discriminant functions, and more than 85 % of the samples were correctly classified (grouped). Using the sets 3 and 4 for DA resulted in a slightly better separation of land uses, being more than 85% of the samples correctly classified. These results suggest that the combination of properties of different nature is effective to show the state of soil quality, owing to the close interaction among physical, chemical and biochemical properties in soil. In addition, NIR spectra offer an integrate vision of soil quality, as they synthesize information regarding mineralogy, soil chemistry, soil biology, organic matter and physical attributes. With the DA developed with the PLFAs, the 100% of samples were correctly classified or grouped, indicating a clear impact of land management. This confirms the higher sensitivity of parameters related to soil microbial community structure to evaluate soil quality, perturbations and management. This result was expected as microbial communities respond very fast to changes in land use, faster than measurements of total microbial biomass and activity. Key Words: Land use changes; Phospholipids fatty acids; Near Infrared Spectroscopy

Markers of serotonergic function in the orbitofrontal cortex and dorsal raphé nucleus predict individual variation in spatial-discrimination serial reversal learning.

PubMed

Barlow, Rebecca L; Alsiö, Johan; Jupp, Bianca; Rabinovich, Rebecca; Shrestha, Saurav; Roberts, Angela C; Robbins, Trevor W; Dalley, Jeffrey W

2015-06-01

Dysfunction of the orbitofrontal cortex (OFC) impairs the ability of individuals to flexibly adapt behavior to changing stimulus-reward (S-R) contingencies. Impaired flexibility also results from interventions that alter serotonin (5-HT) and dopamine (DA) transmission in the OFC and dorsomedial striatum (DMS). However, it is unclear whether similar mechanisms underpin naturally occurring variations in behavioral flexibility. In the present study, we used a spatial-discrimination serial reversal procedure to investigate interindividual variability in behavioral flexibility in rats. We show that flexibility on this task is improved following systemic administration of the 5-HT reuptake inhibitor citalopram and by low doses of the DA reuptake inhibitor GBR12909. Rats in the upper quintile of the distribution of perseverative responses during repeated S-R reversals showed significantly reduced levels of the 5-HT metabolite, 5-hydroxy-indoleacetic acid, in the OFC. Additionally, 5-HT2A receptor binding in the OFC of mid- and high-quintile rats was significantly reduced compared with rats in the low-quintile group. These perturbations were accompanied by an increase in the expression of monoamine oxidase-A (MAO-A) and MAO-B in the lateral OFC and by a decrease in the expression of MAO-A, MAO-B, and tryptophan hydroxylase in the dorsal raphé nucleus of highly perseverative rats. We found no evidence of significant differences in markers of DA and 5-HT function in the DMS or MAO expression in the ventral tegmental area of low- vs high-perseverative rats. These findings indicate that diminished serotonergic tone in the OFC may be an endophenotype that predisposes to behavioral inflexibility and other forms of compulsive behavior.

Real-time sensing and discrimination of single chemicals using the channel of phi29 DNA packaging nanomotor.

PubMed

Haque, Farzin; Lunn, Jennifer; Fang, Huaming; Smithrud, David; Guo, Peixuan

2012-04-24

A highly sensitive and reliable method to sense and identify a single chemical at extremely low concentrations and high contamination is important for environmental surveillance, homeland security, athlete drug monitoring, toxin/drug screening, and earlier disease diagnosis. This article reports a method for precise detection of single chemicals. The hub of the bacteriophage phi29 DNA packaging motor is a connector consisting of 12 protein subunits encircled into a 3.6 nm channel as a path for dsDNA to enter during packaging and to exit during infection. The connector has previously been inserted into a lipid bilayer to serve as a membrane-embedded channel. Herein we report the modification of the phi29 channel to develop a class of sensors to detect single chemicals. The lysine-234 of each protein subunit was mutated to cysteine, generating 12-SH ring lining the channel wall. Chemicals passing through this robust channel and interactions with the SH group generated extremely reliable, precise, and sensitive current signatures as revealed by single channel conductance assays. Ethane (57 Da), thymine (167 Da), and benzene (105 Da) with reactive thioester moieties were clearly discriminated upon interaction with the available set of cysteine residues. The covalent attachment of each analyte induced discrete stepwise blockage in current signature with a corresponding decrease in conductance due to the physical blocking of the channel. Transient binding of the chemicals also produced characteristic fingerprints that were deduced from the unique blockage amplitude and pattern of the signals. This study shows that the phi29 connector can be used to sense chemicals with reactive thioesters or maleimide using single channel conduction assays based on their distinct fingerprints. The results demonstrated that this channel system could be further developed into very sensitive sensing devices.

Color vision predicts processing modes of goal activation during action cascading.

PubMed

Jongkees, Bryant J; Steenbergen, Laura; Colzato, Lorenza S

2017-09-01

One of the most important functions of cognitive control is action cascading: the ability to cope with multiple response options when confronted with various task goals. A recent study implicates a key role for dopamine (DA) in this process, suggesting higher D1 efficiency shifts the action cascading strategy toward a more serial processing mode, whereas higher D2 efficiency promotes a shift in the opposite direction by inducing a more parallel processing mode (Stock, Arning, Epplen, & Beste, 2014). Given that DA is found in high concentration in the retina and modulation of retinal DA release displays characteristics of D2-receptors (Peters, Schweibold, Przuntek, & Müller, 2000), color vision discrimination might serve as an index of D2 efficiency. We used color discrimination, assessed with the Lanthony Desaturated Panel D-15 test, to predict individual differences (N = 85) in a stop-change paradigm that provides a well-established measure of action cascading. In this task it is possible to calculate an individual slope value for each participant that estimates the degree of overlap in task goal activation. When the stopping process of a previous task goal has not finished at the time the change process toward a new task goal is initiated (parallel processing), the slope value becomes steeper. In case of less overlap (more serial processing), the slope value becomes flatter. As expected, participants showing better color vision were more prone to activate goals in a parallel manner as indicated by a steeper slope. Our findings suggest that color vision might represent a predictor of D2 efficiency and the predisposed processing mode of goal activation during action cascading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Markers of Serotonergic Function in the Orbitofrontal Cortex and Dorsal Raphé Nucleus Predict Individual Variation in Spatial-Discrimination Serial Reversal Learning

PubMed Central

Barlow, Rebecca L; Alsiö, Johan; Jupp, Bianca; Rabinovich, Rebecca; Shrestha, Saurav; Roberts, Angela C; Robbins, Trevor W; Dalley, Jeffrey W

2015-01-01

Dysfunction of the orbitofrontal cortex (OFC) impairs the ability of individuals to flexibly adapt behavior to changing stimulus-reward (S-R) contingencies. Impaired flexibility also results from interventions that alter serotonin (5-HT) and dopamine (DA) transmission in the OFC and dorsomedial striatum (DMS). However, it is unclear whether similar mechanisms underpin naturally occurring variations in behavioral flexibility. In the present study, we used a spatial-discrimination serial reversal procedure to investigate interindividual variability in behavioral flexibility in rats. We show that flexibility on this task is improved following systemic administration of the 5-HT reuptake inhibitor citalopram and by low doses of the DA reuptake inhibitor GBR12909. Rats in the upper quintile of the distribution of perseverative responses during repeated S-R reversals showed significantly reduced levels of the 5-HT metabolite, 5-hydroxy-indoleacetic acid, in the OFC. Additionally, 5-HT2A receptor binding in the OFC of mid- and high-quintile rats was significantly reduced compared with rats in the low-quintile group. These perturbations were accompanied by an increase in the expression of monoamine oxidase-A (MAO-A) and MAO-B in the lateral OFC and by a decrease in the expression of MAO-A, MAO-B, and tryptophan hydroxylase in the dorsal raphé nucleus of highly perseverative rats. We found no evidence of significant differences in markers of DA and 5-HT function in the DMS or MAO expression in the ventral tegmental area of low- vs high-perseverative rats. These findings indicate that diminished serotonergic tone in the OFC may be an endophenotype that predisposes to behavioral inflexibility and other forms of compulsive behavior. PMID:25567428

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

PubMed Central

Wang, Zhengfang; Chen, Pei; Yu, Liangli; Harrington, Peter de B.

2013-01-01

Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers. PMID:23398171

Legitimating Racial Discrimination: Emotions, Not Beliefs, Best Predict Discrimination in a Meta-Analysis

PubMed Central

Talaska, Cara A.; Chaiken, Shelly

2013-01-01

Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as stereotypes and beliefs are. Moreover, emotional prejudices are closely related to both observed and self-reported discrimination, whereas stereotypes and beliefs are related only to self-reported discrimination. Implications for justifying discrimination are discussed. PMID:24052687

Investigation of discriminant metabolites in tamoxifen-resistant and choline kinase-alpha-downregulated breast cancer cells using 1H-nuclear magnetic resonance spectroscopy.

PubMed

Kim, Hoe Suk; Tian, Lianji; Kim, Hyeonjin; Moon, Woo Kyung

2017-01-01

Metabolites linked to changes in choline kinase-α (CK-α) expression and drug resistance, which contribute to survival and autophagy mechanisms, are attractive targets for breast cancer therapies. We previously reported that autophagy played a causative role in driving tamoxifen (TAM) resistance of breast cancer cells (BCCs) and was also promoted by CK-α knockdown, resulting in the survival of TAM-resistant BCCs. There is no comparative study yet about the metabolites resulting from BCCs with TAM-resistance and CK-α knockdown. Therefore, the aim of this study was to explore the discriminant metabolic biomarkers responsible for TAM resistance as well as CK-α expression, which might be linked with autophagy through a protective role. A total of 33 intracellular metabolites, including a range of amino acids, energy metabolism-related molecules and others from cell extracts of the parental cells (MCF-7), TAM-resistant cells (MCF-7/TAM) and CK-α knockdown cells (MCF-7/shCK-α, MCF-7/TAM/shCK-α) were analyzed by proton nuclear magnetic resonance spectroscopy (1H-NMRS). Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) revealed the existence of differences in the intracellular metabolites to separate the 4 groups: MCF-7 cells, MCF-7/TAM cells, MCF-7-shCK-α cells, and MCF-7/TAM/shCK-α cells. The metabolites with VIP>1 contributed most to the differentiation of the cell groups, and they included fumarate, UA (unknown A), lactate, myo-inositol, glycine, phosphocholine, UE (unknown E), glutamine, formate, and AXP (AMP/ADP/ATP). Our results suggest that these altered metabolites would be promising metabolic biomarkers for a targeted therapeutic strategy in BCCs that exhibit TAM-resistance and aberrant CK-α expression, which triggers a survival and drug resistance mechanism.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination.

PubMed

Duraipandian, Shiyamala; Sylvest Bergholt, Mads; Zheng, Wei; Yu Ho, Khek; Teh, Ming; Guan Yeoh, Khay; Bok Yan So, Jimmy; Shabbir, Asim; Huang, Zhiwei

2012-08-01

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Sylvest Bergholt, Mads; Zheng, Wei; Yu Ho, Khek; Teh, Ming; Guan Yeoh, Khay; Bok Yan So, Jimmy; Shabbir, Asim; Huang, Zhiwei

2012-08-01

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Spectral discrimination of lithologic facies in the granite of the Pedra Branca Goias using LANDSAT 1 digital imagery

NASA Technical Reports Server (NTRS)

Parada, N. D. J.; Almeido, R., Jr.

1982-01-01

The applicability of LANDSAT MSS imagery for discriminating geobotanical associations observed in zones of cassiterite-rich metasomatic alterations in the granitic body of Serra da Pedra Branca was investigated. Computer compatible tapes of dry and rainy season imagery were analyzed. Image enlargement, corrections, linear contrast stretch, and ratioing of noncorrelated spectral bands were performed using the Image 100 with a grey scale of 256 levels between zero and 255. Only bands 5 and 7 were considered. Band ratioing of noncorrelated channels (5 and 7) of rainy season imagery permits distinction of areas with different vegetation coverage percentage, which corresponds to geobotanial associations in the area studied. The linear contrast stretch of channel 5, especially of the dry season image is very unsatisfactory in this area.

Phytochemical diversity of cranberry (Vaccinium macrocarpon Aiton) cultivars by anthocyanin determination and metabolomic profiling with chemometric analysis.

PubMed

Brown, Paula N; Murch, Susan J; Shipley, Paul

2012-01-11

Originally native to the eastern United States, American cranberry ( Vaccinium macrocarpon Aiton, family Ericaceae) cultivation of native and hybrid varieties has spread across North America. Herein is reported the phytochemical diversity of five cranberry cultivars (Stevens, Ben Lear, Bergman, Pilgrim, and GH1) collected in the Greater Vancouver Regional District, by anthocyanin content and UPLC-TOF-MS metabolomic profiling. The anthocyanin content for biological replicates (n = 5) was determined as 7.98 ± 5.83, Ben Lear; 7.02 ± 1.75, Bergman; 6.05 ± 2.51, GH1; 3.28 ± 1.88, Pilgrim; and 2.81 ± 0.81, Stevens. Using subtractive metabonomic algorithms 6481 compounds were found conserved across all varietals, with 136 (Ben Lear), 84 (Bergman), 91 (GH1), 128 (Pilgrim), and 165 (Stevens) unique compounds observed. Principal component analysis (PCA) did not differentiate varieties, whereas partial least-squares discriminate analysis (PLS-DA) exhibited clustering patterns. Univariate statistical approaches were applied to the data set, establishing significance of values and assessing quality of the models. Metabolomic profiling with chemometric analysis proved to be useful for characterizing metabonomic changes across cranberry varieties.

[Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].

PubMed

Zhang, Yan-nan; Chen, Lan-zhen; Xue, Xiao-feng; Wu, Li-ming; Li, Yi; Yang, Juan

2015-09-01

At present, the rice syrup as a low price of the sweeteners was often adulterated into acacia honey and the adulterated honeys were sold in honey markets, while there is no suitable and fast method to identify honey adulterated with rice syrup. In this study, Near infrared spectroscopy (NIR) combined with chemometric methods were used to discriminate authenticity of honey. 20 unprocessed acacia honey samples from the different honey producing areas, mixed? with different proportion of rice syrup, were prepared of seven different concentration gradient? including 121 samples. The near infrared spectrum (NIR) instrument and spectrum processing software have been applied in the? spectrum? scanning and data conversion on adulterant samples, respectively. Then it was analyzed by Principal component analysis (PCA) and canonical discriminant analysis methods in order to discriminating adulterated honey. The results showed that after principal components analysis, the first two principal components accounted for 97.23% of total variation, but the regionalism of the score plot of the first two PCs was not obvious, so the canonical discriminant analysis was used to make the further discrimination, all samples had been discriminated correctly, the first two discriminant functions accounted for 91.6% among the six canonical discriminant functions, Then the different concentration of adulterant samples can be discriminated correctly, it illustrate that canonical discriminant analysis method combined with NIR spectroscopy is not only feasible but also practical for rapid and effective discriminate of the rice syrup adulterant of acacia honey.

Association between the metabolome and low bone mineral density in Taiwanese women determined by (1)H NMR spectroscopy.

PubMed

You, Ying-Shu; Lin, Ching-Yu; Liang, Hao-Jan; Lee, Shen-Hung; Tsai, Keh-Sung; Chiou, Jeng-Min; Chen, Yen-Ching; Tsao, Chwen-Keng; Chen, Jen-Hau

2014-01-01

Osteoporosis is related to the alteration of specific circulating metabolites. However, previous studies on only a few metabolites inadequately explain the pathogenesis of this complex syndrome. To date, no study has related the metabolome to bone mineral density (BMD), which would provide an overview of metabolism status and may be useful in clinical practice. This cross-sectional study involved 601 healthy Taiwanese women aged 40 to 55 years recruited from MJ Health Management Institution between 2009 and 2010. Participants were classified according to high (2nd tertile plus 3rd tertile) and low (1st tertile) BMD groups. The plasma metabolome was evaluated by proton nuclear magnetic resonance spectroscopy ((1) H NMR). Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and logistic regression analysis were used to assess the association between the metabolome and BMD. The high and low BMD groups could be differentiated by PLS-DA but not PCA in postmenopausal women (Q(2)  = 0.05, ppermutation  = 0.04). Among postmenopausal women, elevated glutamine was significantly associated with low BMD (adjusted odds ratio [AOR] = 5.10); meanwhile, elevated lactate (AOR = 0.55), acetone (AOR = 0.51), lipids (AOR = 0.04), and very low-density lipoprotein (AOR = 0.49) protected against low BMD. To the best of our knowledge, this study is the first to identify a group of metabolites for characterizing low BMD in postmenopausal women using a (1) H NMR-based metabolomic approach. The metabolic profile may be useful for predicting the risk of osteoporosis in postmenopausal women at an early age. © 2014 American Society for Bone and Mineral Research.

Analysis of trace metal concentrations in raw cow's milk from three dairy farms in North Gondar, Ethiopia: chemometric approach.

PubMed

Akele, M L; Abebe, D Z; Alemu, A K; Assefa, A G; Madhusudhan, A; de Oliveira, R R

2017-09-11

Concentrations of essential (Cu, Mn, and Zn) and toxic (Cr, Cd, and Pb) trace metals in 30 raw cow's milk samples were quantified using flame atomic absorption spectrometry. The samples were collected from the Nara-Awudarda, Tana-Abo, and Kosoye Amba-Rass sites in North Gondar, Ethiopia, preserved in a deep freezer (-20 °C), and then digested by Kjeldahl apparatus with HNO 3 /H 2 O 2 (5:2; v/v) at 300 °C for 2.5 h. The data were subject to principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Overall hazard quotient (HQ) and carcinogenic risk (CR) values were also estimated to assess metal-related health risks. The mean concentrations of Cr, Mn, Cu, Zn, Cd, and Pb in the milk samples ranged 0.468-0.828, 1.614-2.806, 0.840-1.532, 1.208-5.267, ND-0.330, and ND-0.186 mg/kg, respectively. The lowest values were obtained for Kosoye Amba-Rass milk samples, while the highest were found for those collected from Nara-Awudarda milk samples, probably due to high mineral enrichment and metal leaching (especially Cd and Pb) from coal deposits. PCA revealed clustering of samples with respect to their geographic origin. Validation of PLS-DA model showed 100% classification efficiency using external validation samples and detected Cd and Cu as trace metal markers. The HQ and CR values were within the safe level; however, the former is close to the alert threshold level for Nara-Awudarda milk samples. Thus, further studies on common foodstuffs, constituting a higher proportion in the local diet, are required in this area to provide a complete risk assessment.

Mass spectrometry for the characterization of brewing process.

PubMed

Vivian, Adriana Fu; Aoyagui, Caroline Tiemi; de Oliveira, Diogo Noin; Catharino, Rodrigo Ramos

2016-11-01

Beer is a carbonated alcoholic beverage produced by fermenting ingredients containing starch, especially malted cereals, and other compounds such as water, hops and yeast. The process comprises five main steps: malting, mashing, boiling, fermentation and maturation. There has been growing interest in the subject, since there is increasing demand for beer quality aspects and beer is a ubiquitous alcoholic beverage in the world. This study is based on the manufacturing process of a Brazilian craft brewery, which is characterized by withdrawing samples during key production stages and using electrospray ionization (ESI) high-resolution mass spectrometry (HRMS), a selective and reliable technique used in the identification of substances in an expeditious and practical way. Multivariate data analysis, namely partial least squares discriminant analysis (PLS-DA) is used to define its markers. In both positive and negative modes of PLS-DA score plot, it is possible to notice differences between each stage. VIP score analysis pointed out markers coherent with the process, such as barley components ((+)-catechin), small peptide varieties, hop content (humulone), yeast metabolic compounds and, in maturation, flavoring compounds (caproic acid, glutaric acid and 2,3-butanediol). Besides that, it was possible to identify other important substances such as off-flavor precursors and other different trace compounds, according to the focus given. This is an attractive alternative for the control of food and beverage industry, allowing a quick assessment of process status before it is finished, preventing higher production costs, ensuring quality and helping the control of desirable features, as flavor, foam stability and drinkability. Covering different classes of compounds, this approach suggests a novel analytical strategy: "processomics", aiming at understanding processes in detail, promoting control and being able to make improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration.

PubMed

Fadzlillah, Nurrulhidayah Ahmad; Rohman, Abdul; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2013-01-01

In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.

A comparison of different chemometrics approaches for the robust classification of electronic nose data.

PubMed

Gromski, Piotr S; Correa, Elon; Vaughan, Andrew A; Wedge, David C; Turner, Michael L; Goodacre, Royston

2014-11-01

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.

Assessment of nucleosides as putative tumor biomarkers in prostate cancer screening by CE-UV.

PubMed

Buzatto, Adriana Zardini; de Oliveira Silva, Mariana; Poppi, Ronei Jesus; Simionato, Ana Valéria Colnaghi

2017-05-01

Cancer is responsible for millions of deaths worldwide, but most base diseases may be cured if detected early. Screening tests may be used to identify early-stage malignant neoplasms. However, the major screening tool for prostate cancer, the prostate-specific antigen test, has unsuitable sensitivity. Since cancer cells may affect the pattern of consumption and excretion of nucleosides, such biomolecules are putative biomarkers that can be used for diagnosis and treatment evaluation. Using a previously validated method for the analysis of nucleosides in blood serum by capillary electrophoresis with UV-vis spectroscopy detection, we investigated 60 samples from healthy individuals and 42 samples from prostate cancer patients. The concentrations of nucleosides in both groups were compared and a multivariate partial least squares-discriminant analysis classification model was optimized for prediction of prostate cancer. The validation of the model with an independent sample set resulted in the correct classification of 82.4% of the samples, with sensitivity of 90.5% and specificity of 76.7%. A significant downregulation of 5-methyluridine and inosine was observed, which can be indicative of the carcinogenic process. Therefore, such analytes are potential candidates for prostate cancer screening. Graphical Abstract Separation of the studied nucleosides and the internal standard 8-Bromoguanosine by CE-UV (a); classification of the external validation samples (30 from healthy volunteers and 21 from prostate cancer patients) by the developed Partial Least Square - Discriminant Analysis (PLS-DA) model with accuracy of 82.4% (b); Receiver Operating Characteristics (ROC) curve (c); and Variable Importance in the Projection (VIP) values for the studied nucleosides (d). A significant down-regulation of 5- methyluridine (5mU) and inosine (I) was observed, which can be indicative of the presence of prostate tumors.

Sex differences in catechol contents in the olfactory bulb of control and unilaterally deprived rats.

PubMed

Gómez, C; Briñón, J G; Valero, J; Recio, J S; Murias, A R; Curto, G G; Orio, L; Colado, M I; Alonso, J R

2007-03-01

The dopaminergic system plays important roles in the modulation of olfactory transmission. The present study examines the distribution of dopaminergic cells and the content of dopamine (DA) and its metabolites in control and deprived olfactory bulbs (OB), focusing on the differences between sexes. The content of DA and of its metabolites, dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA), were measured by HPLC. The morphology and distribution of dopaminergic neurons were studied using tyrosine hydroxylase (TH) immunohistochemistry. Cells were typified with TH-parvalbumin, TH-cholecystokinin or TH-neurocalcin double-immunofluorescence assays. Biochemical analyses revealed sex differences in the content of DA and of its metabolites. In normal conditions, the OBs of male rats had higher concentrations of DA, DOPAC and HVA than the OBs of females. The immunohistochemical data pointed to sex differences in the number of TH-immunopositive cells (higher in male than in female rats). Colocalization analyses revealed that dopaminergic cells constitute a different cell subpopulation from those labelled after parvalbumin, cholecystokinin or neurocalcin immunostaining. Unilateral olfactory deprivation caused dramatic alterations in the dopaminergic system. The DA content and the density of dopaminergic cells decreased, the contents of DA and DOPAC as well as TH immunoreactivity were similar in deprived males and females and, finally, the metabolite/neurotransmitter ratio increased. Our results show that the dopaminergic modulation of olfactory transmission seems to differ between males and females and that it is regulated by peripheral olfactory activity. A possible role of the dopaminergic system in the sexually different olfactory sensitivity, discrimination and memory is discussed.

Low levels of serum serotonin and amino acids identified in migraine patients.

PubMed

Ren, Caixia; Liu, Jia; Zhou, Juntuo; Liang, Hui; Wang, Yayun; Sun, Yinping; Ma, Bin; Yin, Yuxin

2018-02-05

Migraine is a highly disabling primary headache associated with a high socioeconomic burden and a generally high prevalence. The clinical management of migraine remains a challenge. This study was undertaken to identify potential serum biomarkers of migraine. Using Liquid Chromatography coupled to Mass Spectrometry (LC-MS), the metabolomic profile of migraine was compared with healthy individuals. Principal component analysis (PCA) and Orthogonal partial least squares-discriminant analysis (orthoPLS-DA) showed the metabolomic profile of migraine is distinguishable from controls. Volcano plot analysis identified 10 serum metabolites significantly decreased during migraine. One of these was serotonin, and the other 9 were amino acids. Pathway analysis and enrichment analysis showed tryptophan metabolism (serotonin metabolism), arginine and proline metabolism, and aminoacyl-tRNA biosynthesis are the three most prominently altered pathways in migraine. ROC curve analysis indicated Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine are potential sensitive and specific biomarkers for migraine. Our results show Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine may be as specific or more specific for migraine than serotonin which is the traditional biomarker of migraine. We propose that therapeutic manipulation of these metabolites or metabolic pathways may be helpful in the prevention and treatment of migraine. Copyright © 2018 Elsevier Inc. All rights reserved.

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

Perceived Discrimination and Health: A Meta-Analytic Review

PubMed Central

Pascoe, Elizabeth A.; Richman, Laura Smart

2009-01-01

Perceived discrimination has been studied with regard to its impact on several types of health effects. This meta-analysis provides a comprehensive account of the relationships between multiple forms of perceived discrimination and both mental and physical health outcomes. In addition, this meta-analysis examines potential mechanisms by which perceiving discrimination may affect health, including through psychological and physiological stress responses and health behaviors. Analysis of 134 samples suggests that when weighting each study’s contribution by sample size, perceived discrimination has a significant negative effect on both mental and physical health. Perceived discrimination also produces significantly heightened stress responses and is related to participation in unhealthy and nonparticipation in healthy behaviors. These findings suggest potential pathways linking perceived discrimination to negative health outcomes. PMID:19586161

Preliminary construction of integral analysis for characteristic components in complex matrices by in-house fabricated solid-phase microextraction fibers combined with gas chromatography-mass spectrometry.

PubMed

Tang, Zhentao; Hou, Wenqian; Liu, Xiuming; Wang, Mingfeng; Duan, Yixiang

2016-08-26

Integral analysis plays an important role in study and quality control of substances with complex matrices in our daily life. As the preliminary construction of integral analysis of substances with complex matrices, developing a relatively comprehensive and sensitive methodology might offer more informative and reliable characteristic components. Flavoring mixtures belonging to the representatives of substances with complex matrices have now been widely used in various fields. To better study and control the quality of flavoring mixtures as additives in food industry, an in-house fabricated solid-phase microextraction (SPME) fiber was prepared based on sol-gel technology in this work. The active organic component of the fiber coating was multi-walled carbon nanotubes (MWCNTs) functionalized with hydroxyl-terminated polydimethyldiphenylsiloxane, which integrate the non-polar and polar chains of both materials. In this way, more sensitive extraction capability for a wider range of compounds can be obtained in comparison with commercial SPME fibers. Preliminarily integral analysis of three similar types of samples were realized by the optimized SPME-GC-MS method. With the obtained GC-MS data, a valid and well-fit model was established by partial least square discriminant analysis (PLS-DA) for classification of these samples (R2X=0.661, R2Y=0.996, Q2=0.986). The validity of the model (R2=0.266, Q2=-0.465) has also approved the potential to predict the "belongingness" of new samples. With the PLS-DA and SPSS method, further screening out the markers among three similar batches of samples may be helpful for monitoring and controlling the quality of the flavoring mixtures as additives in food industry. Conversely, the reliability and effectiveness of the GC-MS data has verified the comprehensive and efficient extraction performance of the in-house fabricated fiber. Copyright © 2016 Elsevier B.V. All rights reserved.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis.

PubMed

Li, Tao; Su, Chen

2018-06-02

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola. Copyright © 2018 Elsevier B.V. All rights reserved.

River water quality assessment using environmentric techniques: case study of Jakara River Basin.

PubMed

Mustapha, Adamu; Aris, Ahmad Zaharin; Juahir, Hafizan; Ramli, Mohammad Firuz; Kura, Nura Umar

2013-08-01

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.

Genetic determinants of time perception mediated by the serotonergic system.

PubMed

Sysoeva, Olga V; Tonevitsky, Alexander G; Wackermann, Jirí

2010-09-17

The present study investigates neurobiological underpinnings of individual differences in time perception. Forty-four right-handed Russian Caucasian males (18-35 years old) participated in the experiment. The polymorphism of the genes related to the activity of serotonin (5-HT) and dopamine (DA)-systems (such as 5-HTT, 5HT2a, MAOA, DAT, DRD2, COMT) was determined upon the basis of DNA analysis according to a standard procedure. Time perception in the supra-second range (mean duration 4.8 s) was studied, using the duration discrimination task and parametric fitting of psychometric functions, resulting in individual determination of the point of subjective equality (PSE). Assuming the 'dual klepsydra model' of internal duration representation, the PSE values were transformed into equivalent values of the parameter κ (kappa), which is a measure of the 'loss rate' of the duration representation. An association between time representation parameters (PSE and κ, respectively) and 5-HT-related genes was found, but not with DA-related genes. Higher 'loss rate' (κ) of the cumulative duration representation were found for the carriers of genotypes characterized by higher 5-HT transmission, i.e., 1) lower 5-HT reuptake, known for the 5-HTTLPR SS polymorphism compared with LL, 2) lower 5-HT degradation, described for the 'low expression' variant of MAOA VNTR gene compared with 'high expression' variant, and 3) higher 5-HT2a receptor density, proposed for the TT polymorphism of 5-HT2a T102C gene compared with CC. Convergent findings of the present study and previous psychopharmacological studies suggest an action path from 5-HT-activity-related genes, via activity of 5-HT in the brain, to time perception. An involvement of the DA-system in the encoding of durations in the supra-second range is questioned.

Targeted isolation and identification of bioactive compounds lowering cholesterol in the crude extracts of crabapples using UPLC-DAD-MS-SPE/NMR based on pharmacology-guided PLS-DA.

PubMed

Wen, Chao; Wang, Dongshan; Li, Xing; Huang, Tao; Huang, Cheng; Hu, Kaifeng

2018-02-20

The anti-hyperlipidemic effects of crude crabapple extracts derived from Malus 'Red jade', Malus hupehensis (Pamp.) Rehd. and Malus prunifolia (Willd.) Borkh. were evaluated on high-fat diet induced obese (HF DIO) mice. The results revealed that some of these extracts could lower serum cholesterol levels in HF DIO mice. The same extracts were also parallelly analyzed by LC-MS in both positive and negative ionization modes. Based on the pharmacological results, 22 LC-MS variables were identified to be correlated with the anti-hyperlipidemic effects using partial least square discriminant analysis (PLS-DA) and independent samples t-test. Further, under the guidance of the bioactivity-correlated LC-MS signals, 10 compounds were targetedly isolated and enriched using UPLC-DAD-MS-SPE and identified/elucidated by NMR together with MS/MS as citric acid(1), p-coumaric acid(2), hyperoside(3), myricetin(4), naringenin(5), quercetin(6), kaempferol(7), gentiopicroside(8), ursolic acid(9) and 8-epiloganic acid(10). Among these 10 compounds, 6 compounds, hyperoside(3), myricetin(4), naringenin(5), quercetin(6), kaempferol(7) and ursolic acid(9), were individually studied and reported to indeed have effects on lowering the serum lipid levels. These results demonstrated the efficiency of this strategy for drug discovery. In contrast to traditional routes to discover bioactive compounds in the plant extracts, targeted isolation and identification of bioactive compounds in the crude plant extracts using UPLC-DAD-MS-SPE/NMR based on pharmacology-guided PLS-DA of LC-MS data brings forward a new efficient dereplicated approach to natural products research for drug discovery. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling.

PubMed

Povey, Jane F; O'Malley, Christopher J; Root, Tracy; Martin, Elaine B; Montague, Gary A; Feary, Marc; Trim, Carol; Lang, Dietmar A; Alldread, Richard; Racher, Andrew J; Smales, C Mark

2014-08-20

Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10L bioreactor model of Lonza's large-scale (up to 20,000L) fed-batch cell culture processes. Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements. Copyright © 2014 Elsevier B.V. All rights reserved.

Exploring the Prevalence of Disrespect and Abuse during Childbirth in Kenya

PubMed Central

Abuya, Timothy; Warren, Charlotte E.; Miller, Nora; Njuki, Rebecca; Ndwiga, Charity; Maranga, Alice; Mbehero, Faith; Njeru, Anne; Bellows, Ben

2015-01-01

Background Poor quality of care including fear of disrespect and abuse (D&A) perpetuated by health workers influences women’s decisions to seek maternity care. Key manifestations of D&A include: physical abuse, non-consented care, non-confidential care, non-dignified care, discrimination, abandonment, and detention in facilities. This paper describes manifestations of D&A experienced in Kenya and measures their prevalence. Methods This paper is based on baseline data collected during a before-and-after study designed to measure the effect of a package of interventions to reduce the prevalence of D&A experienced by women during labor and delivery in thirteen Kenyan health facilities. Data were collected through an exit survey of 641 women discharged from postnatal wards. We present percentages of D&A manifestations and odds ratios of its relationship with demographic characteristics using a multivariate fixed effects logistic regression model. Results Twenty percent of women reported any form of D&A. Manifestations of D&A includes: non-confidential care (8.5%), non-dignified care (18%), neglect or abandonment (14.3%), Non-consensual care (4.3%) physical abuse (4.2%) and, detainment for non-payment of fees (8.1). Women aged 20-29 years were less likely to experience non-confidential care compared to those under 19; OR: [0.6 95% CI (0.36, 0.90); p=0.017]. Clients with no companion during delivery were less likely to experience inappropriate demands for payment; OR: [0.49 (0.26, 0.95); p=0.037]; while women with higher parities were three times more likely to be detained for lack of payment and five times more likely to be bribed compared to those experiencing there first birth. Conclusion One out of five women experienced feeling humiliated during labor and delivery. Six categories of D&A during childbirth in Kenya were reported. Understanding the prevalence of D&A is critical in developing interventions at national, health facility and community levels to address the factors and drivers that influence D&A in facilities and to encourage clients’ future facility utilization. PMID:25884566

Exploring the prevalence of disrespect and abuse during childbirth in Kenya.

PubMed

Abuya, Timothy; Warren, Charlotte E; Miller, Nora; Njuki, Rebecca; Ndwiga, Charity; Maranga, Alice; Mbehero, Faith; Njeru, Anne; Bellows, Ben

2015-01-01

Poor quality of care including fear of disrespect and abuse (D&A) perpetuated by health workers influences women's decisions to seek maternity care. Key manifestations of D&A include: physical abuse, non-consented care, non-confidential care, non-dignified care, discrimination, abandonment, and detention in facilities. This paper describes manifestations of D&A experienced in Kenya and measures their prevalence. This paper is based on baseline data collected during a before-and-after study designed to measure the effect of a package of interventions to reduce the prevalence of D&A experienced by women during labor and delivery in thirteen Kenyan health facilities. Data were collected through an exit survey of 641 women discharged from postnatal wards. We present percentages of D&A manifestations and odds ratios of its relationship with demographic characteristics using a multivariate fixed effects logistic regression model. Twenty percent of women reported any form of D&A. Manifestations of D&A includes: non-confidential care (8.5%), non-dignified care (18%), neglect or abandonment (14.3%), Non-consensual care (4.3%) physical abuse (4.2%) and, detainment for non-payment of fees (8.1). Women aged 20-29 years were less likely to experience non-confidential care compared to those under 19; OR: [0.6 95% CI (0.36, 0.90); p=0.017]. Clients with no companion during delivery were less likely to experience inappropriate demands for payment; OR: [0.49 (0.26, 0.95); p=0.037]; while women with higher parities were three times more likely to be detained for lack of payment and five times more likely to be bribed compared to those experiencing there first birth. One out of five women experienced feeling humiliated during labor and delivery. Six categories of D&A during childbirth in Kenya were reported. Understanding the prevalence of D&A is critical in developing interventions at national, health facility and community levels to address the factors and drivers that influence D&A in facilities and to encourage clients' future facility utilization.

Amygdala response to faces parallels social behavior in Williams syndrome

PubMed Central

Snyder, Abraham Z.; Haist, Frank; Raichle, Marcus E.; Bellugi, Ursula; Stiles, Joan

2009-01-01

Individuals with Williams syndrome (WS), a genetically determined disorder, show relatively strong face-processing abilities despite poor visuospatial skills and depressed intellectual function. Interestingly, beginning early in childhood they also show an unusually high level of interest in face-to-face social interaction. We employed functional magnetic resonance imaging (fMRI) to investigate physiological responses in face-sensitive brain regions, including ventral occipito-temporal cortex and the amygdala, in this unique genetic disorder. Participants included 17 individuals with WS, 17 age- and gender-matched healthy adults (chronological age-matched controls, CA) and 17 typically developing 8- to 9-year-old children (developmental age controls, DA). While engaged in a face discrimination task, WS participants failed to recruit the amygdala, unlike both CA and DA controls. WS fMRI responses in ventral occipito-temporal cortex, however, were comparable to those of DA controls. Given the integral role of the amygdala in social behavior, the failure of WS participants to recruit this region during face processing may be a neural correlate of the abnormally high sociability that characterizes this disorder. PMID:19633063

Norepinephrine and Stimulant Addiction

PubMed Central

Sofuoglu, Mehmet; Sewell, R. Andrew

2008-01-01

No pharmacotherapies are approved for stimulant use disorders, which are an important public health problem. Stimulants increase synaptic levels of the monoamines dopamine (DA), serotonin (5-HT), and norepinephrine (NE). Stimulant reward is attributable mostly to increased DA in the reward circuitry, although DA stimulation alone cannot explain the rewarding effects of stimulants. The noradrenergic system, which uses NE as the main chemical messenger, serves multiple brain functions including arousal, attention, mood, learning, memory, and stress response. In preclinical models of addiction, NE is critically involved in mediating stimulant effects including sensitization, drug discrimination, and reinstatement of drug seeking. In clinical studies, adrenergic blockers have shown promise as treatments for cocaine abuse and dependence, especially in patients experiencing severe withdrawal symptoms. Disulfiram, which blocks NE synthesis, increased the number of cocaine-negative urines in five randomized clinical trials. Lofexidine, an α2-adrenergic agonist, reduces the craving induced by stress and drug cues in drug users. In addition, the norepinephrine transporter (NET) inhibitor atomoxetine attenuates some of d-amphetamine’s subjective and physiological effects in humans. These findings warrant further studies evaluating noradrenergic medications as treatments for stimulant addiction. PMID:18811678

A strong static-magnetic field alters operant responding by rats.

PubMed

Nakagawa, M; Matsuda, Y

1988-01-01

Forty male rats of the Wistar ST strain were trained and observed for Sidman avoidance (SA) for 7 weeks or for discriminative avoidance (DA) for 14 weeks to determine the effects of exposure to a strong static-magnetic field. Before avoidance conditioning was completed, rats in the SA group were exposed to the static field at 0.6 T, 16 h/day for 4 days during the fifth week, and those in the DA group were exposed for 6 h/day for 4 days during the seventh week. In the SA conditioning, frequency of lever-pressing by exposed rats gradually decreased during 1 week of exposure and stayed low for at least 2 weeks after exposure. Frequencies of electric shocks received by the rats increased dramatically during the second day of exposure and consistently stayed higher than those of control rats. In the DA condition, exposed rats responded at lower rates than did control rats throughout the observation period. They received more shocks during the 2 weeks following exposure. The data indicate that performance of avoidance responses was inhibited by a comparatively long exposure to a strong magnetic field.

Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation.

PubMed

Almeida, Mariana R; Fidelis, Carlos H V; Barata, Lauro E S; Poppi, Ronei J

2013-12-15

The Amazon tree Aniba rosaeodora Ducke (rosewood) provides an essential oil valuable for the perfume industry, but after decades of predatory extraction it is at risk of extinction. The extraction of the essential oil from wood implies the cutting of the tree, and then the study of oil extracted from the leaves is important as a sustainable alternative. The goal of this study was to test the applicability of Raman spectroscopy and Partial Least Square Discriminant Analysis (PLS-DA) as means to classify the essential oil extracted from different parties (wood, leaves and branches) of the Brazilian tree A. rosaeodora. For the development of classification models, the Raman spectra were split into two sets: training and test. The value of the limit that separates the classes was calculated based on the distribution of samples of training. This value was calculated in a manner that the classes are divided with a lower probability of incorrect classification for future estimates. The best model presented sensitivity and specificity of 100%, predictive accuracy and efficiency of 100%. These results give an overall vision of the behavior of the model, but do not give information about individual samples; in this case, the confidence interval for each sample of classification was also calculated using the resampling bootstrap technique. The methodology developed have the potential to be an alternative for standard procedures used for oil analysis and it can be employed as screening method, since it is fast, non-destructive and robust. © 2013 Elsevier B.V. All rights reserved.

Metabonomics study of the effects of pretreatment with glycyrrhetinic acid on mesaconitine-induced toxicity in rats.

PubMed

Sun, Bo; Zhang, Ming; Zhang, Qi; Ma, Kunpeng; Li, Haijing; Li, Famei; Dong, Fangting; Yan, Xianzhong

2014-07-03

Aconitum carmichaelii Debx. (Fuzi), a commonly use traditional Chinese medicine (TCM), has often been used in combination with Rhizoma Glycyrrhizae (Gancao) to reduce its toxicity due to diester diterpenoid alkaloids aconitine, mesaconitine, and hypaconitine. However, the mechanism of detoxication is still unclear. Glycyrrhetinic acid (GA) is the metabolite of glycyrrhizinic acid (GL), the major component of Gancao. In present study, the effect of GA on the changes of metabolic profiles induced by mesaconitine was investigated using NMR-based metabolomic approaches. Fifteen male Wistar rats were divided into a control group, a group administered mesaconitine alone, and a group administered mesaconitine with one pretreatment with GA. Their urine samples were used for NMR spectroscopic metabolic profiling. Statistical analyses such as orthogonal projections to latent structures-discriminant analysis (OPLS-DA), t-test, hierarchical cluster, and pathway analysis were used to detect the effects of pretreatment with GA on mesaconitine-induced toxicity. The OPLS-DA score plots showed the metabolic profiles of GA-pretreated rats apparently approach to those of normal rats compared to mesaconitine-induced rats. From the t-test and boxplot results, the concentrations of leucine/isoleucine, lactate, acetate, succinate, trimethylamine (TMA), dimethylglycine (DMG), 2-oxo-glutarate, creatinine/creatine, glycine, hippurate, tyrosine and benzoate were significantly changed in metabolic profiles of mesaconitine-induced rats. The disturbed metabolic pathways include amino acid biosynthesis and metabolism. GA-pretreatment can mitigate the metabolic changes caused by mesaconitine-treatment on rats, indicating that prophylaxis with GA could reduce the toxicity of mesaconitine at the metabolic level. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Tissue metabolic changes for effects of pirfenidone in rats of acute paraquat poisoning by GC-MS.

PubMed

Ma, Jianshe; Sun, Fa; Chen, Bingbao; Tu, Xiaoting; Peng, Xiufa; Wen, Congcong; Hu, Lufeng; Wang, Xianqin

2017-12-01

We developed a metabolomic method to evaluate the effect of pirfenidone on rats with acute paraquat (PQ) poisoning, through the analysis of various tissues (lung, liver, kidney, and heart), by gas chromatography-mass spectrometry (GC-MS). Thirty-eight rats were randomly divided into a control group, an acute PQ (20 mg kg -1 ) poisoning group, a pirfenidone (20 mg kg -1 ) treatment group, and a pirfenidone (40 mg kg -1 ) treatment group. Partial least squares-discriminate analysis (PLS-DA) revealed metabolic alterations in rat tissue samples from the two pirfenidone treatment groups after acute PQ poisoning. The PLS-DA 3D score chart showed that the rats in the acute PQ poisoning group were clearly distinguished from the rats in the control group. Also, the two pirfenidone treatment groups were distinguished from the acute PQ poisoning group and control group. Additionally, the pirfenidone (40 mg kg -1 ) treatment group was separated farther than the pirfenidone (20 mg kg -1 ) treatment group from the acute PQ poisoning group. Evaluation of the pathological changes in the rat tissues revealed that treatment with pirfenidone appeared to decrease pulmonary fibrosis in the acute PQ poisoning rats. The results indicate that pirfenidone induced beneficial metabolic alterations in the tissues of rats with acute PQ poisoning. Rats with acute PQ poisoning exhibited a certain reduction in biochemical indicators after treatment with pirfenidone, indicating that pirfenidone could protect liver and kidney function. Accordingly, the developed metabolomic approach proved to be useful to elucidate the effect of pirfenidone in rats of acute PQ poisoning.

Social subordination produces distinct stress-related phenotypes in female rhesus monkeys

PubMed Central

Michopoulos, Vasiliki; Higgins, Melinda; Toufexis, Donna; Wilson, Mark E

2012-01-01

Social subordination in female macaques is imposed by harassment and the threat of aggression and produces reduced control over one's social and physical environment and a dysregulation of the limbic-hypothalamic-pituitary-adrenal axis resembling that observed in people suffering from psychopathologies. These effects support the contention that this particular animal model is an ethologically relevant paradigm in which to investigate the etiology of stress-induced psychological illness related to women. Here, we sought to expand this model by performing a discriminate analysis (DA) on 33 variables within three domains; behavioral, metabolic/anthropomorphic, and neuroendocrine, collected from socially housed female rhesus monkeys in order to assess whether exposure to social subordination produces a distinct phenotype. A receiver operating characteristic (ROC) curve was also calculated to determine each domain's classification accuracy. DA found significant markers within each domain that differentiated dominant and subordinate females. Subordinate females received more aggression, showed more submissive behavior, and received less of affiliation from others than did dominant females. Metabolic differences included increased leptin, and reduced adiponectin in dominant compared to subordinate females. Dominant females exhibited increased sensitivity to hormonal stimulation with higher serum LH in response to estradiol, cortisol in response to ACTH, and increased glucocorticoid negative feedback. Serum oxytocin, CSF DOPAC and serum PACAP were all significantly higher in dominant females. ROC curve analysis accurately predicted social status in all three domains. Results suggest that socially house rhesus monkeys represent a cogent animal model in which to study the physiology and behavioral consequences of chronic psychosocial stress in humans. PMID:22244748

Log-ratio transformed major element based multidimensional classification for altered High-Mg igneous rocks

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Quiroz-Ruiz, Alfredo

2016-12-01

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High-Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High-Mg and 32,948 "Common") relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log-ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High-Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High-Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High-Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%-100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass-balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs.

PubMed

Huang, Y; Andueza, D; de Oliveira, L; Zawadzki, F; Prache, S

2015-11-01

Since consumers are showing increased interest in the origin and method of production of their food, it is important to be able to authenticate dietary history of animals by rapid and robust methods used in the ruminant products. Promising breakthroughs have been made in the use of spectroscopic methods on fat to discriminate pasture-fed and concentrate-fed lambs. However, questions remained on their discriminatory ability in more complex feeding conditions, such as concentrate-finishing after pasture-feeding. We compared the ability of visible reflectance spectroscopy (Vis RS, wavelength range: 400 to 700 nm) with that of visible-near-infrared reflectance spectroscopy (Vis-NIR RS, wavelength range: 400 to 2500 nm) to differentiate between carcasses of lambs reared with three feeding regimes, using partial least square discriminant analysis (PLS-DA) as a classification method. The sample set comprised perirenal fat of Romane male lambs fattened at pasture (P, n = 69), stall-fattened indoors on commercial concentrate and straw (S, n = 55) and finished indoors with concentrate and straw for 28 days after pasture-feeding (PS, n = 65). The overall correct classification rate was better for Vis-NIR RS than for Vis RS (99.0% v. 95.1%, P < 0.05). Vis-NIR RS allowed a correct classification rate of 98.6%, 100.0% and 98.5% for P, S and PS lambs, respectively, whereas Vis RS allowed a correct classification rate of 98.6%, 94.5% and 92.3% for P, S and PS lambs, respectively. This study suggests the likely implication of molecules absorbing light in the non-visible part of the Vis-NIR spectra (possibly fatty acids), together with carotenoid and haem pigments, in the discrimination of the three feeding regimes.

Study on bayes discriminant analysis of EEG data.

PubMed

Shi, Yuan; He, DanDan; Qin, Fang

2014-01-01

In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.

Chemical and Genetic Discrimination of Cistanches Herba Based on UPLC-QTOF/MS and DNA Barcoding

PubMed Central

Zheng, Sihao; Jiang, Xue; Wu, Labin; Wang, Zenghui; Huang, Linfang

2014-01-01

Cistanches Herba (Rou Cong Rong), known as “Ginseng of the desert”, has a striking curative effect on strength and nourishment, especially in kidney reinforcement to strengthen yang. However, the two plant origins of Cistanches Herba, Cistanche deserticola and Cistanche tubulosa, vary in terms of pharmacological action and chemical components. To discriminate the plant origin of Cistanches Herba, a combined method system of chemical and genetic –UPLC-QTOF/MS technology and DNA barcoding–were firstly employed in this study. The results indicated that three potential marker compounds (isomer of campneoside II, cistanoside C, and cistanoside A) were obtained to discriminate the two origins by PCA and OPLS-DA analyses. DNA barcoding enabled to differentiate two origins accurately. NJ tree showed that two origins clustered into two clades. Our findings demonstrate that the two origins of Cistanches Herba possess different chemical compositions and genetic variation. This is the first reported evaluation of two origins of Cistanches Herba, and the finding will facilitate quality control and its clinical application. PMID:24854031

Social status correlates of reporting gender discrimination and racial discrimination among racially diverse women.

PubMed

Ro, Annie E; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in North California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES

PubMed Central

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D.

2009-01-01

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component’s discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies. PMID:20582334

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES.

PubMed

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D

2008-05-12

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component's discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies.

Health literacy in pregnant women facing prenatal screening may explain their intention to use a patient decision aid: a short report.

PubMed

Delanoë, Agathe; Lépine, Johanie; Leiva Portocarrero, Maria Esther; Robitaille, Hubert; Turcotte, Stéphane; Lévesque, Isabelle; Wilson, Brenda J; Giguère, Anik M C; Légaré, France

2016-07-11

It has been suggested that health literacy may impact the use of decision aids (DAs) among patients facing difficult decisions. Embedded in the pilot test of a questionnaire, this study aimed to measure the association between health literacy and pregnant women's intention to use a DA to decide about prenatal screening. We recruited a convenience sample of 45 pregnant women in three clinical sites (family practice teaching unit, birthing center and obstetrical ambulatory care clinic). We asked participating women to complete a self-administered questionnaire assessing their intention to use a DA to decide about prenatal screening and assessed their health literacy levels using one subjective and two objective scales. Two of the three scales discriminated between levels of health literacy (three numeracy questions and three health literacy questions). We found a positive correlation between pregnant women's intention to use a DA and subjective health literacy (Spearman coefficient, Rho 0.32, P = 0.04) but not objective health literacy (Spearman coefficient, Rho 0.07, P = 0.65). Hence subjective health literacy may affect the intention to use a DA among pregnant women facing a decision about prenatal screening. Special attention should be given to pregnant women with lower health literacy levels to increase their intention to use a DA and ensure that every pregnant women can give informed and value-based consent to prenatal screening.

Discrimination of speech and non-speech sounds following theta-burst stimulation of the motor cortex

PubMed Central

Rogers, Jack C.; Möttönen, Riikka; Boyles, Rowan; Watkins, Kate E.

2014-01-01

Perceiving speech engages parts of the motor system involved in speech production. The role of the motor cortex in speech perception has been demonstrated using low-frequency repetitive transcranial magnetic stimulation (rTMS) to suppress motor excitability in the lip representation and disrupt discrimination of lip-articulated speech sounds (Möttönen and Watkins, 2009). Another form of rTMS, continuous theta-burst stimulation (cTBS), can produce longer-lasting disruptive effects following a brief train of stimulation. We investigated the effects of cTBS on motor excitability and discrimination of speech and non-speech sounds. cTBS was applied for 40 s over either the hand or the lip representation of motor cortex. Motor-evoked potentials recorded from the lip and hand muscles in response to single pulses of TMS revealed no measurable change in motor excitability due to cTBS. This failure to replicate previous findings may reflect the unreliability of measurements of motor excitability related to inter-individual variability. We also measured the effects of cTBS on a listener’s ability to discriminate: (1) lip-articulated speech sounds from sounds not articulated by the lips (“ba” vs. “da”); (2) two speech sounds not articulated by the lips (“ga” vs. “da”); and (3) non-speech sounds produced by the hands (“claps” vs. “clicks”). Discrimination of lip-articulated speech sounds was impaired between 20 and 35 min after cTBS over the lip motor representation. Specifically, discrimination of across-category ba–da sounds presented with an 800-ms inter-stimulus interval was reduced to chance level performance. This effect was absent for speech sounds that do not require the lips for articulation and non-speech sounds. Stimulation over the hand motor representation did not affect discrimination of speech or non-speech sounds. These findings show that stimulation of the lip motor representation disrupts discrimination of speech sounds in an articulatory feature-specific way. PMID:25076928

Changes in plasma protein levels as an early indication of a bloodstream infection

PubMed Central

Joenväärä, Sakari; Kaartinen, Johanna; Järvinen, Asko; Renkonen, Risto

2017-01-01

Blood culture is the primary diagnostic test performed in a suspicion of bloodstream infection to detect the presence of microorganisms and direct the treatment. However, blood culture is slow and time consuming method to detect blood stream infections or separate septic and/or bacteremic patients from others with less serious febrile disease. Plasma proteomics, despite its challenges, remains an important source for early biomarkers for systemic diseases and might show changes before direct evidence from bacteria can be obtained. We have performed a plasma proteomic analysis, simultaneously at the time of blood culture sampling from ten blood culture positive and ten blood culture negative patients, and quantified 172 proteins with two or more unique peptides. Principal components analysis, Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and ROC curve analysis were performed to select protein(s) features which can classify the two groups of samples. We propose a number of candidates which qualify as potential biomarkers to select the blood culture positive cases from negative ones. Pathway analysis by two methods revealed complement activation, phagocytosis pathway and alterations in lipid metabolism as enriched pathways which are relevant for the condition. Data are available via ProteomeXchange with identifier PXD005022. PMID:28235076

Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

ERIC Educational Resources Information Center

Lee, Debbiesiu L.; Ahn, Soyeon

2012-01-01

This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy

NASA Astrophysics Data System (ADS)

Wang, Jianfeng; Lin, Kan; Zheng, Wei; Yu Ho, Khek; Teh, Ming; Guan Yeoh, Khay; Huang, Zhiwei

2015-08-01

This work aims to evaluate clinical value of a fiber-optic Raman spectroscopy technique developed for in vivo diagnosis of esophageal squamous cell carcinoma (ESCC) during clinical endoscopy. We have developed a rapid fiber-optic Raman endoscopic system capable of simultaneously acquiring both fingerprint (FP)(800-1800 cm-1) and high-wavenumber (HW)(2800-3600 cm-1) Raman spectra from esophageal tissue in vivo. A total of 1172 in vivo FP/HW Raman spectra were acquired from 48 esophageal patients undergoing endoscopic examination. The total Raman dataset was split into two parts: 80% for training; while 20% for testing. Partial least squares-discriminant analysis (PLS-DA) and leave-one patient-out, cross validation (LOPCV) were implemented on training dataset to develop diagnostic algorithms for tissue classification. PLS-DA-LOPCV shows that simultaneous FP/HW Raman spectroscopy on training dataset provides a diagnostic sensitivity of 97.0% and specificity of 97.4% for ESCC classification. Further, the diagnostic algorithm applied to the independent testing dataset based on simultaneous FP/HW Raman technique gives a predictive diagnostic sensitivity of 92.7% and specificity of 93.6% for ESCC identification, which is superior to either FP or HW Raman technique alone. This work demonstrates that the simultaneous FP/HW fiber-optic Raman spectroscopy technique improves real-time in vivo diagnosis of esophageal neoplasia at endoscopy.

Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics.

PubMed

Wang, Jie; Zeng, Hao-Long; Du, Hongying; Liu, Zeyuan; Cheng, Ji; Liu, Taotao; Hu, Ting; Kamal, Ghulam Mustafa; Li, Xihai; Liu, Huili; Xu, Fuqiang

2018-03-01

Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPLS-DA, PLS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data. Copyright © 2017 Elsevier B.V. All rights reserved.

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

PubMed

Kharbach, Mourad; Kamal, Rabie; Mansouri, Mohammed Alaoui; Marmouzi, Ilias; Viaene, Johan; Cherrah, Yahia; Alaoui, Katim; Vercammen, Joeri; Bouklouze, Abdelaziz; Vander Heyden, Yvan

2018-10-15

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H 3 O + , NO + and O 2 + ). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Variation of Root Exudates from the Hyperaccumulator Sedum alfredii under Cadmium Stress: Metabonomics Analysis

PubMed Central

Luo, Qing; Sun, Lina; Hu, Xiaomin; Zhou, Ruiren

2014-01-01

Hydroponic experiments were conducted to investigate the variation of root exudates from the hyperaccumulator Sedum alfredii under the stress of cadmium (Cd). S. alfredii was cultured for 4 days in the nutrient solution spiked with CdCl2 at concentrations of 0, 5, 10, 40, and 400 µM Cd after the pre-culture. The root exudates were collected and analyzed by GC-MS, and 62 compounds were identified. Of these compounds, the orthogonal partial least-squares discrimination analysis (OPLS-DA) showed that there were a distinct difference among the root exudates with different Cd treatments and 20 compounds resulting in this difference were found out. Changing tendencies in the relative content of these 20 compounds under the different Cd treatments were analyzed. These results indicated that trehalose, erythritol, naphthalene, d-pinitol and n-octacosane might be closely related to the Cd stabilization, phosphoric acid, tetradecanoic acid, oxalic acid, threonic acid and glycine could be attributed to the Cd mobilization, and mannitol, oleic acid, 3-hydroxybutanoic acid, fructose, octacosanol and ribitol could copy well with the Cd stress. PMID:25545686

Effect of management (organic vs conventional) on volatile profiles of six plum cultivars (Prunus salicina Lindl.). A chemometric approach for varietal classification and determination of potential markers.

PubMed

Cuevas, F J; Moreno-Rojas, J M; Arroyo, F; Daza, A; Ruiz-Moreno, M J

2016-05-15

The volatile profiles of six plum cultivars ('Laetitia', 'Primetime', 'Sapphire', 'Showtime', 'Songold' and 'Souvenir') produced under two management systems (conventional and organic) and harvested in two consecutive years were obtained by HS-SPME-GC-MS. Twenty-five metabolites were determined, five of which (pentanal, (E)-2-heptenal, 1-octanol, eucalyptol and 2-pentylfuran) are reported for the first time in Prunus salicina Lindl. Hexanal stood out as a major volatile compound affected by the management system. In addition, partial least square discriminant analysis (PLS-DA) achieved an effective classification of genotypes based on their volatile profiles. A high classification accuracy model was obtained with a sensitivity of 97.9% and a specificity of 99.6%. Furthermore, the application of a dual criterion, based on a method of variable selection, VIP (variable importance in projection) and the results of a univariate analysis (ANOVA), allowed the identification of potential volatile markers in 'Primetime', 'Showtime' and 'Souvenir' genotypes (cultivars not characterised to date). Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Gómez-Romero, María; Ajal, El Amine; Bagur-González, María Gracia; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2017-01-15

High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using "Low-level" and "Mid-Level" data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination "chromatographic dataset+chemometric technique" allowing the most accurate classification for each monovarietal extra-VOO was highlighted. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer-aided classification of rheumatoid arthritis in finger joints using frequency domain optical tomography

NASA Astrophysics Data System (ADS)

Klose, C. D.; Kim, H. K.; Netz, U.; Blaschke, S.; Zwaka, P. A.; Mueller, G. A.; Beuthan, J.; Hielscher, A. H.

2009-02-01

Novel methods that can help in the diagnosis and monitoring of joint disease are essential for efficient use of novel arthritis therapies that are currently emerging. Building on previous studies that involved continuous wave imaging systems we present here first clinical data obtained with a new frequency-domain imaging system. Three-dimensional tomographic data sets of absorption and scattering coefficients were generated for 107 fingers. The data were analyzed using ANOVA, MANOVA, Discriminant Analysis DA, and a machine-learning algorithm that is based on self-organizing mapping (SOM) for clustering data in 2-dimensional parameter spaces. Overall we found that the SOM algorithm outperforms the more traditional analysis methods in terms of correctly classifying finger joints. Using SOM, healthy and affected joints can now be separated with a sensitivity of 0.97 and specificity of 0.91. Furthermore, preliminary results suggest that if a combination of multiple image properties is used, statistical significant differences can be found between RA-affected finger joints that show different clinical features (e.g. effusion, synovitis or erosion).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Christopher A.; Martinez, Alonzo; McNamara, Bruce K.

International Atom Energy Agency (IAEA) safeguard verification measures in gaseous centrifuge enrichment plants (GCEPs) rely on environmental sampling, non-destructive assay (NDA), and destructive assay (DA) sampling and analysis to determine uranium enrichment. UF6 bias defect measurements are made by DA sampling and analysis to assure that enrichment is consistent with declarations. DA samples are collected from a limited number of cylinders for high precision, offsite mass spectrometer analysis. Samples are typically drawn from a sampling tap into a UF6 sample bottle, then packaged, sealed, and shipped under IAEA chain of custody to an offsite analytical laboratory. Future DA safeguard measuresmore » may require improvements in efficiency and effectiveness as GCEP capacities increase and UF6 shipping regulations become increasingly more restrictive. The Pacific Northwest National Laboratory (PNNL) DA sampler concept and Laser Ablation Absorption Ratio Spectrometry (LAARS) assay method are under development to potentially provide DA safeguard tools that increase inspection effectiveness and reduce sample shipping constraints. The PNNL DA sampler concept uses a handheld sampler to collect DA samples for either onsite LAARS assay or offsite laboratory analysis. The DA sampler design will use a small sampling planchet that is coated with an adsorptive film to collect controlled quantities of UF6 gas directly from a cylinder or process sampling tap. Development efforts are currently underway at PNNL to enhance LAARS assay performance to allow high-precision onsite bias defect measurements. In this paper, we report on the experimental investigation to develop adsorptive films for the PNNL DA sampler concept. These films are intended to efficiently capture UF6 and then stabilize the collected DA sample prior to onsite LAARS or offsite laboratory analysis. Several porous material composite films were investigated, including a film designed to maximize the chemical adsorption and binding of gaseous UF6 onto the sampling planchet.« less

Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

PubMed

Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

2017-04-15

HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metabolomics study on the hepatoprotective effect of scoparone using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry.

PubMed

Zhang, Aihua; Sun, Hui; Dou, Shengshan; Sun, Wenjun; Wu, Xiuhong; Wang, Ping; Wang, Xijun

2013-01-07

Scoparone is an important constituent of Yinchenhao (Artemisia annua L.), a famous medicinal plant, and displayed bright prospects in the prevention and therapy of liver injury. However, the precise molecular mechanism of hepatoprotective effects has not been comprehensively explored. Here, metabolomics techniques are the comprehensive assessment of endogenous metabolites in a biological system and may provide additional insight into the mechanisms. The present investigation was designed to assess the effects and possible mechanisms of scoparone against carbon tetrachloride-induced liver injury. Ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) combined with pattern recognition approaches including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were integrated to discover differentiating metabolites. Results indicate five ions in the positive mode as differentiating metabolites. Functional pathway analysis revealed that the alterations in these metabolites were associated with primary bile acid biosynthesis, pyrimidine metabolism. Of note, scoparone has a potential pharmacological effect through regulating multiple perturbed pathways to the normal state. Our findings also showed that the robust metabolomics techniques are promising for getting biomarkers and clarifying mechanisms of disease, highlighting insights into drug discovery.

Identification of pulmonary edema in forensic autopsy cases of fatal anaphylactic shock using Fourier transform infrared microspectroscopy.

PubMed

Lin, Hancheng; Luo, Yiwen; Wang, Lei; Deng, Kaifei; Sun, Qiran; Fang, Ruoxi; Wei, Xin; Zha, Shuai; Wang, Zhenyuan; Huang, Ping

2018-03-01

Anaphylaxis is a rapid allergic reaction that may cause sudden death. Currently, postmortem diagnosis of anaphylactic shock is sometimes difficult and often achieved through exclusion. The aim of our study was to investigate whether Fourier transform infrared (FTIR) microspectroscopy combined with pattern recognition methods would be complementary to traditional methods and provide a more accurate postmortem diagnosis of fatal anaphylactic shock. First, the results of spectral peak area analysis showed that the pulmonary edema fluid of the fatal anaphylactic shock group was richer in protein components than the control group, which included mechanical asphyxia, brain injury, and acute cardiac death. Subsequently, principle component analysis (PCA) was performed and showed that the anaphylactic shock group contained more turn and α-helix protein structures as well as less tyrosine-rich proteins than the control group. Ultimately, a partial least-square discriminant analysis (PLS-DA) model combined with a variables selection method called the genetic algorithm (GA) was built and demonstrated good separation between these two groups. This pilot study demonstrates that FTIR microspectroscopy has the potential to be an effective aid for postmortem diagnosis of fatal anaphylactic shock.

Social Status Correlates of Reporting Racial Discrimination and Gender Discrimination among Racially Diverse Women

PubMed Central

Ro, Annie E.; Choi, Kyung-Hee

2009-01-01

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design. PMID:19485231

Cocaine Self-Administration Experience Induces Pathological Phasic Accumbens Dopamine Signals and Abnormal Incentive Behaviors in Drug-Abstinent Rats

PubMed Central

Wang, Xuefei; Sugam, Jonathan A.; Carelli, Regina M.

2016-01-01

Chronic exposure to drugs of abuse is linked to long-lasting alterations in the function of limbic system structures, including the nucleus accumbens (NAc). Although cocaine acts via dopaminergic mechanisms within the NAc, less is known about whether phasic dopamine (DA) signaling in the NAc is altered in animals with cocaine self-administration experience or if these animals learn and interact normally with stimuli in their environment. Here, separate groups of rats self-administered either intravenous cocaine or water to a receptacle (controls), followed by 30 d of enforced abstinence. Next, all rats learned an appetitive Pavlovian discrimination and voltammetric recordings of real-time DA release were taken in either the NAc core or shell of cocaine and control subjects. Cocaine experience differentially impaired DA signaling in the core and shell relative to controls. Although phasic DA signals in the shell were essentially abolished for all stimuli, in the core, DA did not distinguish between cues and was abnormally biased toward reward delivery. Further, cocaine rats were unable to learn higher-order associations and even altered simple conditioned approach behaviors, displaying enhanced preoccupation with cue-associated stimuli (sign-tracking; ST) but diminished time at the food cup awaiting reward delivery (goal-tracking). Critically, whereas control DA signaling correlated with ST behaviors, cocaine experience abolished this relationship. These findings show that cocaine has persistent, differential, and pathological effects on both DA signaling and DA-dependent behaviors and suggest that psychostimulant experience may remodel the very circuits that bias organisms toward repeated relapse. SIGNIFICANCE STATEMENT Relapsing to drug abuse despite periods of abstinence and sincere attempts to quit is one of the most pernicious facets of addiction. Unfortunately, little is known about how the dopamine (DA) system functions after periods of drug abstinence, particularly its role in behavior in nondrug situations. Here, rats learned about food-paired stimuli after prolonged abstinence from cocaine self-administration. Using voltammetry, we found that real-time DA signals in cocaine-experienced rats were strikingly altered relative to controls. Further, cocaine-experienced animals found reward-predictive stimuli abnormally salient and spent more time interacting with cues. Therefore, cocaine induces neuroplastic changes in the DA system that biases animals toward salient stimuli (including reward-associated cues), putting addicts at increasing risk to relapse as addiction increases in severity. PMID:26740664

Discriminative-Stimulus, Subject-rated and Physiological Effects of Methamphetamine in Humans Pretreated with Aripiprazole

PubMed Central

Sevak, Rajkumar J.; Vansickel, Andrea R.; Stoops, William W.; Glaser, Paul E. A.; Hays, Lon R.; Rush, Craig R.

2013-01-01

Methamphetamine is thought to produce its behavioral effects by releasing dopamine (DA), serotonin (5-HT) and norepinephrine. Results from animal studies support this notion, while results from human laboratory studies have not consistently demonstrated the importance of monoamine systems in the behavioral effects of methamphetamine. Human laboratory procedures of drug-discrimination are well suited to assess neuropharmacological mechanisms of the training drug by studying pharmacological manipulation. In this human laboratory study, six participants with a history of recreational stimulant use learned to discriminate 10 mg oral methamphetamine. After acquiring the discrimination (i.e., ≥80% correct responding on 4 consecutive sessions), the effects of a range of doses of methamphetamine (0, 2.5, 5, 10 and 15 mg), alone and in combination with 0 and 20 mg aripiprazole (a partial agonist at D2 and 5-HT1A receptors), were assessed. Methamphetamine alone functioned as a discriminative stimulus, produced prototypical stimulant-like subject-rated drug effects (e.g., increased ratings of Good Effects, Talkative-Friendly, and Willing to Pay For) and elevated cardiovascular indices. These effects were generally a function of dose. Aripiprazole alone did not occasion methamphetamine-appropriate responding or produce subject-rated effects, but modestly impaired performance. Administration of aripiprazole significantly attenuated the discriminative-stimulus and cardiovascular effects of methamphetamine, as well as some of the subject-rated drug effects. These results indicate that monoamine systems likely play a role in the behavioral effects of methamphetamine in humans. Moreover, given the concordance between past results with d-amphetamine and the present findings, d-amphetamine can likely serve as a model for the pharmacological effects of methamphetamine. PMID:21694622

Classification of debtor credit status and determination amount of credit risk by using linier discriminant function

NASA Astrophysics Data System (ADS)

Aidi, Muhammad Nur; Sari, Resty Indah

2012-05-01

A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.

Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields

NASA Astrophysics Data System (ADS)

Hahn, Federico

1996-03-01

Statistical discriminative analysis and neural networks were used to prove that crop/weed/soil discrimination by optical reflectance was feasible. The wavelengths selected as inputs on those neural networks were ten nanometers width, reducing the total collected radiation for the sensor. Spectral data collected from several farms having different weed populations were introduced to discriminant analysis. The best discriminant wavelengths were used to build a wavelength histogram which selected the three best spectral broadbands for broccoli/weed/soil discrimination. The broadbands were analyzed using a new single broadband discriminator index named the discriminative integration index, DII, and the DII values obtained were used to train a neural network. This paper introduces the index concept, its results and its use for minimizing artificial lightning requirements with broadband spectral measurements for broccoli/weed/soil discrimination.

Tuning Optoelectronic Properties of Organic Semiconductors Via Donor-Acceptor Cocrystals and Interfacial Composites

NASA Astrophysics Data System (ADS)

Wang, Chen

Organic donor-acceptor (D-A) interaction has attracted intensive research interest because of the promising applications in electronic devices and renewable energy. Depending on the interaction process, the optoelectronic properties of organic semiconductors may change dramatically. To improve their performance and expand the applications, we have investigated the structure-property relationship in D-A cocrystals and nanofibril composites. These materials provide unique D-A interface structures, thus allowing tunable charge transfer across the interface, which can be modified and controlled by exquisite molecule design and supramolecular assembly. In Chapter 2, we studied the fabrication, conductivity, and chemiresistive sensor performance of tetrathiafulvalene (TTF) - 7,7,8,8-tetracyanoquinodimethane (TCNQ) charge transfer cocrystal microfibers. Compared to TCNQ and TTF, TTF-TCNQ cocrystal has much higher conductivity under ambient conditions, due to the high yield of charge separation, which also induces high polarization at the interface, resulting in different binding intensity towards alkyl and aromatic amines. Based on this investment, we developed a TTF-TCNQ chemiresistive sensor to efficiently discriminate alkyl and aromatic amine vapors. In Chapter 3, we further designed a new series of D-A cocrystals, and studied the coassembly and optical properties. The cocrystal is composed of coronene and perylene diimide at 1:1 molar ratio and belongs to the triclinic system, as confirmed by X-ray analysis. The donor and acceptor molecules perform an alternate pi-pi stacking along the (100) direction, leading to the strong one-dimensional growth tendency of macroscopic cocrystal. Additionally, due to the charge transfer interaction, the cocrystal shows a new and largely red-shifted photoluminescence band, compared to the crystals of the components. In Chapter 4, we alternatively developed a series of donor-acceptor nanofibril composites, in which the donor and acceptor nanofibers become the building blocks. By changing the side chains into alkyl groups, the composite forms a homogeneous film with a large donor-acceptor interface and favorable photoinduced charge transfer, leading to a high photoconductivity enhancement, which is a three order magnification of the photoconductivity of the donor and acceptor nanofibers. Furthermore, our measurement proved the D-A interface with alkyl chains interdigitating is compatible and tunable to external alkane vapors, making the composite suitable for chemiresistive sensors for alkane detection.