An Integrated Strategy to Qualitatively Differentiate Components of Raw and Processed Viticis Fructus Based on NIR, HPLC and UPLC-MS Analysis.

PubMed

Diao, Jiayin; Xu, Can; Zheng, Huiting; He, Siyi; Wang, Shumei

2018-06-21

Viticis Fructus is a traditional Chinese herbal drug processed by various methods to achieve different clinical purposes. Thermal treatment potentially alters chemical composition, which may impact on effectiveness and toxicity. In order to interpret the constituent discrepancies of raw versus processed (stir-fried) Viticis Fructus, a multivariate detection method (NIR, HPLC, and UPLC-MS) based on metabonomics and chemometrics was developed. Firstly, synergy interval partial least squares and partial least squares-discriminant analysis were employed to screen the distinctive wavebands (4319 - 5459 cm -1 ) based on preprocessed near-infrared spectra. Then, HPLC with principal component analysis was performed to characterize the distinction. Subsequently, a total of 49 compounds were identified by UPLC-MS, among which 42 compounds were eventually characterized as having a significant change during processing via the semiquantitative volcano plot analysis. Moreover, based on the partial least squares-discriminant analysis, 16 compounds were chosen as characteristic markers that could be in close correlation with the discriminatory near-infrared wavebands. Together, all of these characterization techniques effectively discriminated raw and processed products of Viticis Fructus. In general, our work provides an integrated way of classifying Viticis Fructus, and a strategy to explore discriminatory chemical markers for other traditional Chinese herbs, thus ensuring safety and efficacy for consumers. Georg Thieme Verlag KG Stuttgart · New York.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

PubMed

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-25

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Liu, J.; Xie, H.; Zha, B.; Ding, W.; Luo, J.; Hu, C.

2018-03-01

A methodology is proposed to identify genetically modified sugarcane from non-genetically modified sugarcane by using terahertz spectroscopy and chemometrics techniques, including linear discriminant analysis (LDA), support vector machine-discriminant analysis (SVM-DA), and partial least squares-discriminant analysis (PLS-DA). The classification rate of the above mentioned methods is compared, and different types of preprocessing are considered. According to the experimental results, the best option is PLS-DA, with an identification rate of 98%. The results indicated that THz spectroscopy and chemometrics techniques are a powerful tool to identify genetically modified and non-genetically modified sugarcane.

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

PubMed

Han, Bangxing; Peng, Huasheng; Yan, Hui

2016-01-01

Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

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.

[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].

PubMed

Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin

2007-07-01

Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis.

PubMed

Garcia, E; Klaas, I; Amigo, J M; Bro, R; Enevoldsen, C

2014-12-01

Lameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology

NASA Astrophysics Data System (ADS)

Shao, Yongni; Jiang, Linjun; Zhou, Hong; Pan, Jian; He, Yong

2016-04-01

In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.

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.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Determination of five active compounds in Artemisia princeps and A. capillaris based on UPLC-DAD and discrimination of two species with multivariate analysis.

PubMed

Yang, Heejung; Lee, Dong Young; Jeon, Minji; Suh, Youngbae; Sung, Sang Hyun

2014-05-01

Five active compounds, chlorogenic acid, 3,5-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid, jaceosidin, and eupatilin, in Artemisia princeps (Compositae) were simultaneously determined by ultra-performance liquid chromatography connected to diode array detector. The morphological resemblance between A. princeps and A. capillaris makes it difficult to properly identify species properly. It occasionally leads to misuse or misapplication in Korean traditional medicine. In the study, the discrimination between A. princeps and A. capillaris was optimally performed by the developed validation method, which resulted in definitely a difference between two species. Also, it was developed the most reliable markers contributing to the discrimination of two species by the multivariate analysis methods, such as a principal component analysis and a partial least squares discrimination analysis.

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods.

PubMed

Durakli Velioglu, Serap; Ercioglu, Elif; Boyaci, Ismail Hakki

2017-05-01

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

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.

The role of critical ethnic awareness and social support in the discrimination-depression relationship among Asian Americans: path analysis.

PubMed

Kim, Isok

2014-01-01

This study used a path analytic technique to examine associations among critical ethnic awareness, racial discrimination, social support, and depressive symptoms. Using a convenience sample from online survey of Asian American adults (N = 405), the study tested 2 main hypotheses: First, based on the empowerment theory, critical ethnic awareness would be positively associated with racial discrimination experience; and second, based on the social support deterioration model, social support would partially mediate the relationship between racial discrimination and depressive symptoms. The result of the path analysis model showed that the proposed path model was a good fit based on global fit indices, χ²(2) = 4.70, p = .10; root mean square error of approximation = 0.06; comparative fit index = 0.97; Tucker-Lewis index = 0.92; and standardized root mean square residual = 0.03. The examinations of study hypotheses demonstrated that critical ethnic awareness was directly associated (b = .11, p < .05) with the racial discrimination experience, whereas social support had a significant indirect effect (b = .48; bias-corrected 95% confidence interval [0.02, 1.26]) between the racial discrimination experience and depressive symptoms. The proposed path model illustrated that both critical ethnic awareness and social support are important mechanisms for explaining the relationship between racial discrimination and depressive symptoms among this sample of Asian Americans. This study highlights the usefulness of the critical ethnic awareness concept as a way to better understand how Asian Americans might perceive and recognize racial discrimination experiences in relation to its mental health consequences.

Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis

PubMed Central

Lee, Byeong-Ju; Zhou, Yaoyao; Lee, Jae Soung; Shin, Byeung Kon; Seo, Jeong-Ah; Lee, Doyup; Kim, Young-Suk

2018-01-01

The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm–1 region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans. PMID:29689113

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.

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.

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

PubMed Central

Gu, Xinzhe; Wang, Zhenjie; Huang, Yangmin; Wei, Yingying; Zhang, Miaomiao; Tu, Kang

2015-01-01

This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10−4 and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS. PMID:26642054

An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

NASA Astrophysics Data System (ADS)

Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz

2017-03-01

The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.

Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

PubMed

Melucci, Dora; Bendini, Alessandra; Tesini, Federica; Barbieri, Sara; Zappi, Alessandro; Vichi, Stefania; Conte, Lanfranco; Gallina Toschi, Tullia

2016-08-01

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined. Copyright © 2016. Published by Elsevier Ltd.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

NASA Astrophysics Data System (ADS)

Liu, Wen; Zhang, Yuying; Yang, Si; Han, Donghai

2018-05-01

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5 THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

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

Varietal discrimination of hop pellets by near and mid infrared spectroscopy.

PubMed

Machado, Julio C; Faria, Miguel A; Ferreira, Isabel M P L V O; Páscoa, Ricardo N M J; Lopes, João A

2018-04-01

Hop is one of the most important ingredients of beer production and several varieties are commercialized. Therefore, it is important to find an eco-real-time-friendly-low-cost technique to distinguish and discriminate hop varieties. This paper describes the development of a method based on vibrational spectroscopy techniques, namely near- and mid-infrared spectroscopy, for the discrimination of 33 commercial hop varieties. A total of 165 samples (five for each hop variety) were analysed by both techniques. Principal component analysis, hierarchical cluster analysis and partial least squares discrimination analysis were the chemometric tools used to discriminate positively the hop varieties. After optimizing the spectral regions and pre-processing methods a total of 94.2% and 96.6% correct hop varieties discrimination were obtained for near- and mid-infrared spectroscopy, respectively. The results obtained demonstrate the suitability of these vibrational spectroscopy techniques to discriminate different hop varieties and consequently their potential to be used as an authenticity tool. Compared with the reference procedures normally used for hops variety discrimination these techniques are quicker, cost-effective, non-destructive and eco-friendly. Copyright © 2017 Elsevier B.V. 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.

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages by Fourier transform infrared (FTIR) spectroscopy and chemometrics.

PubMed

Xu, L; Cai, C B; Cui, H F; Ye, Z H; Yu, X P

2012-12-01

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages was developed by Fourier transform infrared (FTIR) spectrometry combined with chemometrics. Transmittance spectra ranging from 400 to 4000 cm⁻¹ of 73 Halal and 78 non-Halal Chinese ham sausages were measured. Sample preparation involved finely grinding of samples and formation of KBr disks (under 10 MPa for 5 min). The influence of data preprocessing methods including smoothing, taking derivatives and standard normal variate (SNV) on partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) was investigated. The results indicate removal of spectral background and baseline plays an important role in discrimination. Taking derivatives, SNV can improve classification accuracy and reduce the complexity of PLSDA. Possibly due to the loss of detailed high-frequency spectral information, smoothing degrades the model performance. For the best models, the sensitivity and specificity was 0.913 and 0.929 for PLSDA with SNV spectra, 0.957 and 0.929 for LS-SVM with second derivative spectra, respectively. Copyright © 2012 Elsevier Ltd. All rights reserved.

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

NASA Astrophysics Data System (ADS)

Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

2013-05-01

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

Psychometric Properties of the Death Anxiety Scale-Extended among Patients with End-Stage Renal Disease.

PubMed

Sharif Nia, Hamid; Pahlevan Sharif, Saeed; Koocher, Gerald P; Yaghoobzadeh, Ameneh; Haghdoost, Ali Akbar; Mar Win, Ma Thin; Soleimani, Mohammad Ali

2017-01-01

This study aimed to evaluate the validity and reliability of the Persian version of Death Anxiety Scale-Extended (DAS-E). A total of 507 patients with end-stage renal disease completed the DAS-E. The factor structure of the scale was evaluated using exploratory factor analysis with an oblique rotation and confirmatory factor analysis. The content and construct validity of the DAS-E were assessed. Average variance extracted, maximum shared squared variance, and average shared squared variance were estimated to assess discriminant and convergent validity. Reliability was assessed using Cronbach's alpha coefficient (α = .839 and .831), composite reliability (CR = .845 and .832), Theta (θ = .893 and .867), and McDonald Omega (Ω = .796 and .743). The analysis indicated a two-factor solution. Reliability and discriminant validity of the factors was established. Findings revealed that the present scale was a valid and reliable instrument that can be used in assessment of death anxiety in Iranian patients with end-stage renal disease.

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.

(1)H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis.

PubMed

Sun, L W; Zhang, H Y; Wu, L; Shu, S; Xia, C; Xu, C; Zheng, J S

2014-03-01

The purpose of this study was to assess the metabolic profile of plasma samples from cows with clinical and subclinical ketosis. According to clinical signs and 3-hydroxybutyrate plasma levels, 81 multiparous Holstein cows were selected from a dairy farm 7 to 21 d after calving. The cows were divided into 3 groups: cows with clinical ketosis, cows with subclinical ketosis, and healthy control cows. (1)H-Nuclear magnetic resonance-based metabolomics was used to assess the plasma metabolic profiles of the 3 groups. The data were analyzed by principal component analysis, partial least squares discriminant analysis, and orthogonal partial least-squares discriminant analysis. The differences in metabolites among the 3 groups were assessed. The orthogonal partial least-squares discriminant analysis model differentiated the 3 groups of plasma samples. The model predicted clinical ketosis with a sensitivity of 100% and a specificity of 100%. In the case of subclinical ketosis, the model had a sensitivity of 97.0% and specificity of 95.7%. Twenty-five metabolites, including acetoacetate, acetone, lactate, glucose, choline, glutamic acid, and glutamine, were different among the 3 groups. Among the 25 metabolites, 4 were upregulated, 7 were downregulated, and 14 were both upregulated and downregulated. The results indicated that plasma (1)H-nuclear magnetic resonance-based metabolomics, coupled with pattern recognition analytical methods, not only has the sensitivity and specificity to distinguish cows with clinical and subclinical ketosis from healthy controls, but also has the potential to be developed into a clinically useful diagnostic tool that could contribute to a further understanding of the disease mechanisms. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Discrimination of surface wear on obsidian tools using LSCM and RelA: pilot study results (area-scale analysis of obsidian tool surfaces).

PubMed

Stemp, W James; Chung, Steven

2011-01-01

This pilot study tests the reliability of laser scanning confocal microscopy (LSCM) to quantitatively measure wear on experimental obsidian tools. To our knowledge, this is the first use of confocal microscopy to study wear on stone flakes made from an amorphous silicate like obsidian. Three-dimensional surface roughness or texture area scans on three obsidian flakes used on different contact materials (hide, shell, wood) were documented using the LSCM to determine whether the worn surfaces could be discriminated using area-scale analysis, specifically relative area (RelA). When coupled with the F-test, this scale-sensitive fractal analysis could not only discriminate the used from unused surfaces on individual tools, but was also capable of discriminating the wear histories of tools used on different contact materials. Results indicate that such discriminations occur at different scales. Confidence levels for the discriminations at different scales were established using the F-test (mean square ratios or MSRs). In instances where discrimination of surface roughness or texture was not possible above the established confidence level based on MSRs, photomicrographs and RelA assisted in hypothesizing why this was so. Copyright © 2011 Wiley Periodicals, Inc.

A comparative UPLC-Q/TOF-MS-based metabolomics approach for distinguishing Zingiber officinale Roscoe of two geographical origins.

PubMed

Mais, Enos; Alolga, Raphael N; Wang, Shi-Lei; Linus, Loveth O; Yin, Xiaojin; Qi, Lian-Wen

2018-02-01

Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

PubMed

Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

2016-09-01

Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Histogram contrast analysis and the visual segregation of IID textures.

PubMed

Chubb, C; Econopouly, J; Landy, M S

1994-09-01

A new psychophysical methodology is introduced, histogram contrast analysis, that allows one to measure stimulus transformations, f, used by the visual system to draw distinctions between different image regions. The method involves the discrimination of images constructed by selecting texture micropatterns randomly and independently (across locations) on the basis of a given micropattern histogram. Different components of f are measured by use of different component functions to modulate the micropattern histogram until the resulting textures are discriminable. When no discrimination threshold can be obtained for a given modulating component function, a second titration technique may be used to measure the contribution of that component to f. The method includes several strong tests of its own assumptions. An example is given of the method applied to visual textures composed of small, uniform squares with randomly chosen gray levels. In particular, for a fixed mean gray level mu and a fixed gray-level variance sigma 2, histogram contrast analysis is used to establish that the class S of all textures composed of small squares with jointly independent, identically distributed gray levels with mean mu and variance sigma 2 is perceptually elementary in the following sense: there exists a single, real-valued function f S of gray level, such that two textures I and J in S are discriminable only if the average value of f S applied to the gray levels in I is significantly different from the average value of f S applied to the gray levels in J. Finally, histogram contrast analysis is used to obtain a seventh-order polynomial approximation of f S.

[Establishment of background color to discriminate among tablets: sharper and more feasible with color-weak simulation as access to safe medication].

PubMed

Ishizaki, Makiko; Maeda, Hatsuo; Okamoto, Ikuko

2014-01-01

Color-weak persons, who in Japan represent approximately 5% of male and 0.2% of female population, may not be able to discriminate among colors of tablets. Thus using color-weak simulation by Variantor™ we evaluated the effects of background colors (light, medium, and dark gray, purple, blue, and blue green) on discrimination among yellow, yellow red, red, and mixed group tablets by our established method. In addition, the influence of white 10-mm ruled squares on background sheets was examined, and the change in color of the tablets and background sheets through the simulation measured. Variance analysis of the data obtained from 42 volunteers demonstrated that with color-weak vision, the best discrimination among yellow, yellow red, or mixed group tablets was achieved on a dark gray background sheet, and a blue background sheet was useful to discriminate among each tablet group in all colors including red. These results were compared with those previously obtained with healthy and cataractous vision, suggesting that gap in color hue and chroma as well as value between background sheets and tablets affects discrimination with color-weak vision. The observed positive effects of white ruled squares, in contrast to those observed on healthy and cataractous vision, demonstrate that a background sheet arranged by two colors allows color-weak persons to discriminate among all sets of tablets in a sharp and feasible manner.

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.

[Establishment of the background color to make discrimination of domestic ethical tablets sharper and more feasible based on the analysis of their color distribution].

PubMed

Ishizaki, Makiko; Maeda, Hatsuo; Okamoto, Ikuko

2012-01-01

In Japan, pharmacists as well as patients often have problems distinguishing one ethical tablet from another because they can be very similar in color. In an attempt to solve this problem, we hypothesized using a background sheet of dark gray identified by N3.5 on the Munsell color system (Munsell CS). The colors of 369 and 656 ethical tablets in Japan and the USA, respectively, were measured. On the Munsell CS, the Japanese tablets were localized mostly in the range of hues between 10R∼10Y with values ≧ 8 and chroma ≦ 4, while the colors of the American tablets were scattered over the hue spectrum with a variety of values and chroma. Based on these findings, we examined the effects of background colors on discrimination between 5 tablets classified into yellow, yellow red, red, or mixed groups that represented typical domestic Japanese tablets. Background colors of light, medium, and dark gray, purple, blue, and blue green were selected based on a general concept on color discrimination. The influence of white 10 mm-ruled squares on background sheets was examined as well. Under JIS Z8723 conditions, 42 volunteers used a 4-point scale to evaluate how clearly they could discriminate between each set of tablets on each of the background sheets. Variance analysis of the obtained data with SPSS demonstrated that with healthy vision, use of a dark gray background sheet with or without ruled squares enabled the sharpest and most feasible discrimination between all sets of tablets. A similar test with dark gray and white clearly demonstrated that the former works as a practical background color for discrimination among different domestic Japanese tablets.

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.

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Discrimination of chicken seasonings and beef seasonings using electronic nose and sensory evaluation.

PubMed

Tian, Huaixiang; Li, Fenghua; Qin, Lan; Yu, Haiyan; Ma, Xia

2014-11-01

This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes. © 2014 Institute of Food Technologists®

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.

Discrimination and Measurements of Three Flavonols with Similar Structure Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Yan, Ling; Liu, Changhong; Qu, Hao; Liu, Wei; Zhang, Yan; Yang, Jianbo; Zheng, Lei

2018-03-01

Terahertz (THz) technique, a recently developed spectral method, has been researched and used for the rapid discrimination and measurements of food compositions due to its low-energy and non-ionizing characteristics. In this study, THz spectroscopy combined with chemometrics has been utilized for qualitative and quantitative analysis of myricetin, quercetin, and kaempferol with concentrations of 0.025, 0.05, and 0.1 mg/mL. The qualitative discrimination was achieved by KNN, ELM, and RF models with the spectra pre-treatments. An excellent discrimination (100% CCR in the prediction set) could be achieved using the RF model. Furthermore, the quantitative analyses were performed by partial least square regression (PLSR) and least squares support vector machine (LS-SVM). Comparing to the PLSR models, the LS-SVM yielded better results with low RMSEP (0.0044, 0.0039, and 0.0048), higher Rp (0.9601, 0.9688, and 0.9359), and higher RPD (8.6272, 9.6333, and 7.9083) for myricetin, quercetin, and kaempferol, respectively. Our results demonstrate that THz spectroscopy technique is a powerful tool for identification of three flavonols with similar chemical structures and quantitative determination of their concentrations.

Global and local processing in adult humans (Homo sapiens), 5-year-old children (Homo sapiens), and adult cotton-top tamarins (Saguinus oedipus).

PubMed

Neiworth, Julie J; Gleichman, Amy J; Olinick, Anne S; Lamp, Kristen E

2006-11-01

This study compared adults (Homo sapiens), young children (Homo sapiens), and adult tamarins (Saguinus oedipus) while they discriminated global and local properties of stimuli. Subjects were trained to discriminate a circle made of circle elements from a square made of square elements and were tested with circles made of squares and squares made of circles. Adult humans showed a global bias in testing that was unaffected by the density of the elements in the stimuli. Children showed a global bias with dense displays but discriminated by both local and global properties with sparse displays. Adult tamarins' biases matched those of the children. The striking similarity between the perceptual processing of adult monkeys and humans diagnosed with autism and the difference between this and normatively developing human perception is discussed.

Shape discrimination and concept formation in the jungle crow (Corvus macrorhynchos).

PubMed

Bogale, Bezawork Afework; Sugita, Shoei

2014-01-01

We investigated whether jungle crows can learn concepts by using printouts of shapes in a simultaneous two-alternative task. Jungle crows were first trained with a red triangle and red square until they reached the discrimination criterion (80% of correct choices in two blocks of 10 trials each). Then, we tested crows with successive transfer tests to investigate both the discrimination cues being used and concept formation ability, by using novel triangular and non-triangular stimuli. All of the jungle crows learnt to discriminate between the triangle and square during training. The discrimination performance was generally not affected either by changes in the colour of the stimuli or when both shape and colour cues conflicted, with the previously non-rewarded shape but matching colour (red square) versus rewarded shape but non-matching colour (green triangle). The use of only outlines of the familiar stimuli also did not affect discrimination behaviour of crows. In addition, crows significantly discriminated novel triangular shapes during the limited trials given, suggesting their ability to form the concept of triangularity. However, failure to discriminate when the novel stimuli size deviated from the original suggests that there is a limit to shape concept formation in a familiar-novel context in the jungle crow.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

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

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

PubMed

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

NMR-based metabolomic analysis of spatial variation in soft corals.

PubMed

He, Qing; Sun, Ruiqi; Liu, Huijuan; Geng, Zhufeng; Chen, Dawei; Li, Yinping; Han, Jiao; Lin, Wenhan; Du, Shushan; Deng, Zhiwei

2014-03-28

Soft corals are common marine organisms that inhabit tropical and subtropical oceans. They are shown to be rich source of secondary metabolites with biological activities. In this work, soft corals from two geographical locations were investigated using ¹H-NMR spectroscopy coupled with multivariate statistical analysis at the metabolic level. A partial least-squares discriminant analysis showed clear separation among extracts of soft corals grown in Sanya Bay and Weizhou Island. The specific markers that contributed to discrimination between soft corals in two origins belonged to terpenes, sterols and N-containing compounds. The satisfied precision of classification obtained indicates this approach using combined ¹H-NMR and chemometrics is effective to discriminate soft corals collected in different geographical locations. The results revealed that metabolites of soft corals evidently depended on living environmental condition, which would provide valuable information for further relevant coastal marine environment evaluation.

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.

Identification of offal adulteration in beef by laser induced breakdown spectroscopy (LIBS).

PubMed

Velioglu, Hasan Murat; Sezer, Banu; Bilge, Gonca; Baytur, Süleyman Efe; Boyaci, Ismail Hakki

2018-04-01

Minced meat is the major ingredient in sausages, beef burgers, and similar products; and thus it is the main product subjected to adulteration with meat offal. Determination of this kind of meat adulteration is crucial due to religious, economic and ethical concerns. The aim of the present study is to discriminate the beef meat and offal samples by using laser induced breakdown spectroscopy (LIBS). To this end, LIBS and multivariate data analysis were used to discriminate pure beef and offal samples qualitatively and to determine the offal mixture adulteration quantitatively. In this analysis, meat samples were frozen and LIBS analysis were performed. The results indicate that by using principal component analysis (PCA), discrimination of pure offal and offal mixture adulterated beef samples can be achieved successfully. Besides, adulteration ratio can be determined using partial least square analysis method (PLS) with 0.947 coefficient of determination (R 2 ) and 3.8% of limit of detection (LOD) values for offal mixture adulterated beef samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

PubMed Central

Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

2013-01-01

To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Shao, Yongni; Xie, Chuanqi; Jiang, Linjun; Shi, Jiahui; Zhu, Jiajin; He, Yong

2015-04-01

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-715 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits.

Discriminative factor analysis of juvenile delinquency in South Korea.

PubMed

Kim, Hyun Sil; Kim, Hun Soo

2006-12-01

The present study was intended to compare difference in research variables between delinquent adolescents and student adolescents, and to analyze discriminative factors of delinquent behaviors among Korean adolescents. The research design of this study was a questionnaire survey. Questionnaires were administered to 2,167 adolescents (1,196 students and 971 delinquents), sampled from 8 middle and high school and 6 juvenile corrective institutions, using the proportional stratified random sampling method. Statistical methods employed were Chi-square, t-test, and logistic regression analysis. The discriminative factors of delinquent behaviors were smoking, alcohol use, other drug use, being sexually abused, viewing time of media violence and pornography. Among these discriminative factors, the factor most strongly associated with delinquency was smoking (odds ratio: 32.32). That is, smoking adolescent has a 32-fold higher possibility of becoming a delinquent adolescent than a non-smoking adolescent. Our findings, that smoking was the strongest discriminative factor of delinquent behavior, suggest that educational strategies to prevent adolescent smoking may reduce the rate of juvenile delinquency. Antismoking educational efforts are therefore urgently needed in South Korea.

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.

Detection and discrimination of microorganisms on various substrates with quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Rios-Velazquez, Carlos; Vazquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2014-06-01

Investigations focusing on devising rapid and accurate methods for developing signatures for microorganisms that could be used as biological warfare agents' detection, identification, and discrimination have recently increased significantly. Quantum cascade laser (QCL)-based spectroscopic systems have revolutionized many areas of defense and security including this area of research. In this contribution, infrared spectroscopy detection based on QCL was used to obtain the mid-infrared (MIR) spectral signatures of Bacillus thuringiensis, Escherichia coli, and Staphylococcus epidermidis. These bacteria were used as microorganisms that simulate biothreats (biosimulants) very truthfully. The experiments were conducted in reflection mode with biosimulants deposited on various substrates including cardboard, glass, travel bags, wood, and stainless steel. Chemometrics multivariate statistical routines, such as principal component analysis regression and partial least squares coupled to discriminant analysis, were used to analyze the MIR spectra. Overall, the investigated infrared vibrational techniques were useful for detecting target microorganisms on the studied substrates, and the multivariate data analysis techniques proved to be very efficient for classifying the bacteria and discriminating them in the presence of highly IR-interfering media.

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.

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.

Citrus species and hybrids depicted by near- and mid-infrared spectroscopy.

PubMed

Páscoa, Ricardo Nmj; Moreira, Silvana; Lopes, João A; Sousa, Clara

2018-01-31

Citrus trees are among the most cultivated plants in the world, with a high economic impact. The wide sexual compatibility among relatives gave rise to a large number of hybrids that are difficult to discriminate. This work sought to explore the ability of infrared spectroscopy to discriminate among Citrus species and/or hybrids and to contribute to the elucidation of its relatedness. Adult leaves of 18 distinct Citrus plants were included in this work. Near- and mid-infrared (NIR and FTIR) spectra were acquired from leaves after harvesting and a drying period of 1 month. Spectra were modelled by principal component analysis and partial least squares discriminant analysis. Both techniques revealed a high discrimination potential (78.5-95.9%), being the best results achieved with NIR spectroscopy and air-dried leaves (95.9%). Infrared spectroscopy was able to successfully discriminate several Citrus species and/or hybrids. Our results contributed also to enhance insights regarding the studied Citrus species and/or hybrids. Despite the benefit of including additional samples, the results herein obtained clearly pointed infrared spectroscopy as a reliable technique for Citrus species and/or hybrid discrimination. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

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

Cen Haiyan; Bao Yidan; He Yong

2006-10-10

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it ismore » concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.« less

Frequency noise measurement of diode-pumped Nd:YAG ring lasers

NASA Technical Reports Server (NTRS)

Chen, Chien-Chung; Win, Moe Zaw

1990-01-01

The combined frequency noise spectrum of two model 120-01A nonplanar ring oscillator lasers was measured by first heterodyne detecting the IF signal and then measuring the IF frequency noise using an RF frequency discriminator. The results indicated the presence of a 1/f-squared noise component in the power-spectral density of the frequency fluctuations between 1 Hz and 1 kHz. After incorporating this 1/f-squared into the analysis of the optical phase tracking loop, the measured phase error variance closely matches the theoretical predictions.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

PubMed

Zhu, Ying; Tan, Tuck Lee

2016-04-15

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Rapid Elemental Analysis and Provenance Study of Blumea balsamifera DC Using Laser-Induced Breakdown Spectroscopy

PubMed Central

Liu, Xiaona; Zhang, Qiao; Wu, Zhisheng; Shi, Xinyuan; Zhao, Na; Qiao, Yanjiang

2015-01-01

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera. PMID:25558999

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Assessing the Effectiveness of Statistical Classification Techniques in Predicting Future Employment of Participants in the Temporary Assistance for Needy Families Program

ERIC Educational Resources Information Center

Montoya, Isaac D.

2008-01-01

Three classification techniques (Chi-square Automatic Interaction Detection [CHAID], Classification and Regression Tree [CART], and discriminant analysis) were tested to determine their accuracy in predicting Temporary Assistance for Needy Families program recipients' future employment. Technique evaluation was based on proportion of correctly…

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...

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®

Psychometric Properties of the Heart Disease Knowledge Scale: Evidence from Item and Confirmatory Factor Analyses

PubMed Central

Lim, Bee Chiu; Kueh, Yee Cheng; Arifin, Wan Nor; Ng, Kok Huan

2016-01-01

Background Heart disease knowledge is an important concept for health education, yet there is lack of evidence on proper validated instruments used to measure levels of heart disease knowledge in the Malaysian context. Methods A cross-sectional, survey design was conducted to examine the psychometric properties of the adapted English version of the Heart Disease Knowledge Questionnaire (HDKQ). Using proportionate cluster sampling, 788 undergraduate students at Universiti Sains Malaysia, Malaysia, were recruited and completed the HDKQ. Item analysis and confirmatory factor analysis (CFA) were used for the psychometric evaluation. Construct validity of the measurement model was included. Results Most of the students were Malay (48%), female (71%), and from the field of science (51%). An acceptable range was obtained with respect to both the difficulty and discrimination indices in the item analysis results. The difficulty index ranged from 0.12–0.91 and a discrimination index of ≥ 0.20 were reported for the final retained 23 items. The final CFA model showed an adequate fit to the data, yielding a 23-item, one-factor model [weighted least squares mean and variance adjusted scaled chi-square difference = 1.22, degrees of freedom = 2, P-value = 0.544, the root mean square error of approximation = 0.03 (90% confidence interval = 0.03, 0.04); close-fit P-value = > 0.950]. Conclusion Adequate psychometric values were obtained for Malaysian undergraduate university students using the 23-item, one-factor model of the adapted HDKQ. PMID:27660543

Psychometric Properties of the Heart Disease Knowledge Scale: Evidence from Item and Confirmatory Factor Analyses.

PubMed

Lim, Bee Chiu; Kueh, Yee Cheng; Arifin, Wan Nor; Ng, Kok Huan

2016-07-01

Heart disease knowledge is an important concept for health education, yet there is lack of evidence on proper validated instruments used to measure levels of heart disease knowledge in the Malaysian context. A cross-sectional, survey design was conducted to examine the psychometric properties of the adapted English version of the Heart Disease Knowledge Questionnaire (HDKQ). Using proportionate cluster sampling, 788 undergraduate students at Universiti Sains Malaysia, Malaysia, were recruited and completed the HDKQ. Item analysis and confirmatory factor analysis (CFA) were used for the psychometric evaluation. Construct validity of the measurement model was included. Most of the students were Malay (48%), female (71%), and from the field of science (51%). An acceptable range was obtained with respect to both the difficulty and discrimination indices in the item analysis results. The difficulty index ranged from 0.12-0.91 and a discrimination index of ≥ 0.20 were reported for the final retained 23 items. The final CFA model showed an adequate fit to the data, yielding a 23-item, one-factor model [weighted least squares mean and variance adjusted scaled chi-square difference = 1.22, degrees of freedom = 2, P-value = 0.544, the root mean square error of approximation = 0.03 (90% confidence interval = 0.03, 0.04); close-fit P-value = > 0.950]. Adequate psychometric values were obtained for Malaysian undergraduate university students using the 23-item, one-factor model of the adapted HDKQ.

Monitoring the ripening process of Cheddar cheese based on hydrophilic component profiling using gas chromatography-mass spectrometry.

PubMed

Ochi, H; Sakai, Y; Koishihara, H; Abe, F; Bamba, T; Fukusaki, E

2013-01-01

We proposed an application methodology that combines metabolic profiling with multiple appropriate multivariate analyses and verified it on the industrial scale of the ripening process of Cheddar cheese to make practical use of hydrophilic low-molecular-weight compound profiling using gas chromatography-mass spectrometry to design optimal conditions and quality monitoring of the cheese ripening process. Principal components analysis provided an overview of the effect of sodium chloride content and kind of lactic acid bacteria starter on the metabolic profile in the ripening process of Cheddar cheese and orthogonal partial least squares-discriminant analysis unveiled the difference in characteristic metabolites. When the sodium chloride contents were different (1.6 and 0.2%) but the same lactic acid bacteria starter was used, the 2 cheeses were classified by orthogonal partial least squares-discriminant analysis from their metabolic profiles, but were not given perfect discrimination. Not much difference existed in the metabolic profile between the 2 cheeses. Compounds including lactose, galactose, lactic acid, 4-aminobutyric acid, and phosphate were identified as contents that differed between the 2 cheeses. On the other hand, in the case of the same salt content of 1.6%, but different kinds of lactic acid bacteria starter, an excellent distinctive discrimination model was obtained, which showed that the difference of lactic acid bacteria starter caused an obvious difference in metabolic profiles. Compounds including lactic acid, lactose, urea, 4-aminobutyric acid, galactose, phosphate, proline, isoleucine, glycine, alanine, lysine, leucine, valine, and pyroglutamic acid were identified as contents that differed between the 2 cheeses. Then, a good sensory prediction model for "rich flavor," which was defined as "thick and rich, including umami taste and soy sauce-like flavor," was constructed based on the metabolic profile during ripening using partial least squares regression analysis. The amino acids proline, leucine, valine, isoleucine, pyroglutamic acid, alanine, glutamic acid, glycine, lysine, tyrosine, serine, phenylalanine, methionine, aspartic acid, and ornithine were extracted as ripening process markers. The present study is not limited to Cheddar cheese and can be applied to various maturation-type natural cheeses. This study provides the technical platform for designing optimal conditions and quality monitoring of the cheese ripening process. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares

NASA Astrophysics Data System (ADS)

Su, Rongguo; Chen, Xiaona; Wu, Zhenzhen; Yao, Peng; Shi, Xiaoyong

2015-07-01

The feasibility of using fluorescence excitation-emission matrix (EEM) along with parallel factor analysis (PARAFAC) and nonnegative least squares (NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis (BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis (HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios (CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

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.

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.

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. 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.

Evidence of Syndemics and Sexuality-Related Discrimination Among Young Sexual-Minority Women.

PubMed

Coulter, Robert W S; Kinsky, Suzanne M; Herrick, Amy L; Stall, Ron D; Bauermeister, José A

2015-09-01

Syndemics, or the co-occurrence and interaction of health problems, have been examined extensively among young men who have sex with men, but their existence remain unexamined, to our knowledge, among sexual-minority (i.e., lesbian, gay, and bisexual) women. Thus, we investigated if syndemics were present among young sexual-minority women, and if sexual-orientation discrimination was an independent variable of syndemic production. A total of 467 sexual-minority women between the ages of 18 and 24 completed a cross-sectional online survey regarding their substance use, mental health, sexual behaviors, height, weight, and experiences of discrimination. We used structural equation modeling to investigate the presence of syndemics and their relationship to sexual-orientation discrimination. Heavy episodic drinking, marijuana use, ecstasy use, hallucinogen use, depressive symptoms, multiple sexual partners, and history of sexually transmitted infections (STIs) comprised syndemics in this population (chi-square=24.989, P=.201; comparative fit index [CFI]=0.946; root mean square error of approximation [RMSEA]=0.023). Sexual-orientation discrimination is significantly and positively associated with the latent syndemic variable (unstandardized coefficient=0.095, P<.05), and this model fit the data well (chi-square=33.558, P=.059; CFI=0.914; RMSEA=0.029). The reverse causal model showed syndemics is not an independent variable of sexual-orientation discrimination (unstandardized coefficient=0.602, P>.05). Syndemics appear to be present and associated with sexual-orientation discrimination among young sexual-minority women. Interventions aimed at reducing discrimination or increasing healthy coping may help reduce substance use, depressive symptoms, and sexual risk behaviors in this population.

First-Order or Second-Order Kinetics? A Monte Carlo Answer

ERIC Educational Resources Information Center

Tellinghuisen, Joel

2005-01-01

Monte Carlo computational experiments reveal that the ability to discriminate between first- and second-order kinetics from least-squares analysis of time-dependent concentration data is better than implied in earlier discussions of the problem. The problem is rendered as simple as possible by assuming that the order must be either 1 or 2 and that…

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics.

PubMed

Shao, Yongni; Xie, Chuanqi; Jiang, Linjun; Shi, Jiahui; Zhu, Jiajin; He, Yong

2015-04-05

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-71 5 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits. Copyright © 2015 Elsevier B.V. All rights reserved.

The relationship between perceived discrimination and psychological distress among Chinese pulmonary tuberculosis patients: the moderating role of self-esteem.

PubMed

Feng, Danjun; Xu, Lingzhong

2015-01-01

This study described the prevalence of psychological distress and examined the moderating effect of self-esteem in the relationship between perceived discrimination and psychological distress among Chinese pulmonary tuberculosis (TB) patients. Seven hundred and twenty patients with TB from three cities of Shandong Province in eastern China participated in a cross-sectional survey. Patients were measured with the Kessler 10 (K10), the Rosenberg self-esteem scale, and a self-developed perceived discrimination questionnaire. A total of 58.6% of patients with TB scored above 16 on the K10, indicating moderate and serious psychological distress. Chi-square test revealed that female patients reported higher psychological distress than male patients. The structural equation modeling (SEM) analysis among the whole sample indicated that perceived discrimination was significantly related with psychological distress (β = .28, p ≤ .01). The multiple group analysis of SEM showed that perceived discrimination had a significantly substantial (β = .50, p ≤ .001), significantly moderate (β = .15, p ≤ .01), and insignificant effect (β = .05, p ≥ .05) on psychological distress among low self-esteem, moderate self-esteem, and high self-esteem patients with TB, respectively, which verified the moderating effect of self-esteem in the relationship between perceived discrimination and psychological distress.

Skill-related differences between athletes and nonathletes in speed discrimination.

PubMed

Thomson, Kaivo; Watt, Anthony; Liukkonen, Jarmo

2008-12-01

This study examined differences in decision-making time and accurscy as attributes of speed discrimination between participants skilled and less skilled in ball games. A total of 130 men, ages 18 to 28 years (M=21.2, SD=2.6), participated. The athlete sample (skilled group) comprised Estonian National League volleyball (n=26) and basketball players (n=27). The nonathlete sample (less skilled group) included 77 soldiers of the Estonian Defence Force with no reported top level experience in ball games. Speed-discrimination stimuli were images of red square shapes presented moving along the sagittal axis at four different virtual velocities on a computer (PC) screen which represented the frontal plane. Analysis indicated that only decision-making time was significantly different between the elite athlete and nonathlete groups. This finding suggests a possible effect of ball-game skills for decision-making time in speed discrimination.

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.

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

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.

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

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.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Inter-class sparsity based discriminative least square regression.

PubMed

Wen, Jie; Xu, Yong; Li, Zuoyong; Ma, Zhongli; Xu, Yuanrong

2018-06-01

Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero-one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero-one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evidence of Syndemics and Sexuality-Related Discrimination Among Young Sexual-Minority Women

PubMed Central

Kinsky, Suzanne M.; Herrick, Amy L.; Stall, Ron D.; Bauermeister, José A.

2015-01-01

Abstract Purpose: Syndemics, or the co-occurrence and interaction of health problems, have been examined extensively among young men who have sex with men, but their existence remain unexamined, to our knowledge, among sexual-minority (i.e., lesbian, gay, and bisexual) women. Thus, we investigated if syndemics were present among young sexual-minority women, and if sexual-orientation discrimination was an independent variable of syndemic production. Methods: A total of 467 sexual-minority women between the ages of 18 and 24 completed a cross-sectional online survey regarding their substance use, mental health, sexual behaviors, height, weight, and experiences of discrimination. We used structural equation modeling to investigate the presence of syndemics and their relationship to sexual-orientation discrimination. Results: Heavy episodic drinking, marijuana use, ecstasy use, hallucinogen use, depressive symptoms, multiple sexual partners, and history of sexually transmitted infections (STIs) comprised syndemics in this population (chi-square=24.989, P=.201; comparative fit index [CFI]=0.946; root mean square error of approximation [RMSEA]=0.023). Sexual-orientation discrimination is significantly and positively associated with the latent syndemic variable (unstandardized coefficient=0.095, P<.05), and this model fit the data well (chi-square=33.558, P=.059; CFI=0.914; RMSEA=0.029). The reverse causal model showed syndemics is not an independent variable of sexual-orientation discrimination (unstandardized coefficient=0.602, P>.05). Conclusions: Syndemics appear to be present and associated with sexual-orientation discrimination among young sexual-minority women. Interventions aimed at reducing discrimination or increasing healthy coping may help reduce substance use, depressive symptoms, and sexual risk behaviors in this population. PMID:26788674

Measuring implementation behaviour of menu guidelines in the childcare setting: confirmatory factor analysis of a theoretical domains framework questionnaire (TDFQ).

PubMed

Seward, Kirsty; Wolfenden, Luke; Wiggers, John; Finch, Meghan; Wyse, Rebecca; Oldmeadow, Christopher; Presseau, Justin; Clinton-McHarg, Tara; Yoong, Sze Lin

2017-04-04

While there are number of frameworks which focus on supporting the implementation of evidence based approaches, few psychometrically valid measures exist to assess constructs within these frameworks. This study aimed to develop and psychometrically assess a scale measuring each domain of the Theoretical Domains Framework for use in assessing the implementation of dietary guidelines within a non-health care setting (childcare services). A 75 item 14-domain Theoretical Domains Framework Questionnaire (TDFQ) was developed and administered via telephone interview to 202 centre based childcare service cooks who had a role in planning the service menu. Confirmatory factor analysis (CFA) was undertaken to assess the reliability, discriminant validity and goodness of fit of the 14-domain theoretical domain framework measure. For the CFA, five iterative processes of adjustment were undertaken where 14 items were removed, resulting in a final measure consisting of 14 domains and 61 items. For the final measure: the Chi-Square goodness of fit statistic was 3447.19; the Standardized Root Mean Square Residual (SRMR) was 0.070; the Root Mean Square Error of Approximation (RMSEA) was 0.072; and the Comparative Fit Index (CFI) had a value of 0.78. While only one of the three indices support goodness of fit of the measurement model tested, a 14-domain model with 61 items showed good discriminant validity and internally consistent items. Future research should aim to assess the psychometric properties of the developed TDFQ in other community-based settings.

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.

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®.

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.

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.

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

PubMed

de Almeida, Maurício Liberal; Saatkamp, Cassiano Junior; Fernandes, Adriana Barrinha; Pinheiro, Antonio Luiz Barbosa; Silveira, Landulfo

2016-09-01

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

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.

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.

Do Rats Use Shape to Solve "Shape Discriminations"?

ERIC Educational Resources Information Center

Minini, Loredana; Jeffery, Kathryn J.

2006-01-01

Visual discrimination tasks are increasingly used to explore the neurobiology of vision in rodents, but it remains unclear how the animals solve these tasks: Do they process shapes holistically, or by using low-level features such as luminance and angle acuity? In the present study we found that when discriminating triangles from squares, rats did…

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.

A Search for WIMP Dark Matter Using an Optimized Chi-square Technique on the Final Data from the Cryogenic Dark Matter Search Experiment (CDMS II)

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

Manungu Kiveni, Joseph

2012-12-01

This dissertation describes the results of a WIMP search using CDMS II data sets accumulated at the Soudan Underground Laboratory in Minnesota. Results from the original analysis of these data were published in 2009; two events were observed in the signal region with an expected leakage of 0.9 events. Further investigation revealed an issue with the ionization-pulse reconstruction algorithm leading to a software upgrade and a subsequent reanalysis of the data. As part of the reanalysis, I performed an advanced discrimination technique to better distinguish (potential) signal events from backgrounds using a 5-dimensional chi-square method. This dataanalysis technique combines themore » event information recorded for each WIMP-search event to derive a backgrounddiscrimination parameter capable of reducing the expected background to less than one event, while maintaining high efficiency for signal events. Furthermore, optimizing the cut positions of this 5-dimensional chi-square parameter for the 14 viable germanium detectors yields an improved expected sensitivity to WIMP interactions relative to previous CDMS results. This dissertation describes my improved (and optimized) discrimination technique and the results obtained from a blind application to the reanalyzed CDMS II WIMP-search data.« less

Difference magnitude is not measured by discrimination steps for order of point patterns.

PubMed

Protonotarios, Emmanouil D; Johnston, Alan; Griffin, Lewis D

2016-07-01

We have shown in previous work that the perception of order in point patterns is consistent with an interval scale structure (Protonotarios, Baum, Johnston, Hunter, & Griffin, 2014). The psychophysical scaling method used relies on the confusion between stimuli with similar levels of order, and the resulting discrimination scale is expressed in just-noticeable differences (jnds). As with other perceptual dimensions, an interesting question is whether suprathreshold (perceptual) differences are consistent with distances between stimuli on the discrimination scale. To test that, we collected discrimination data, and data based on comparison of perceptual differences. The stimuli were jittered square lattices of dots, covering the range from total disorder (Poisson) to perfect order (square lattice), roughly equally spaced on the discrimination scale. Observers picked the most ordered pattern from a pair, and the pair of patterns with the greatest difference in order from two pairs. Although the judgments of perceptual difference were found to be consistent with an interval scale, like the discrimination judgments, no common interval scale that could predict both sets of data was possible. In particular, the midpattern of the perceptual scale is 11 jnds away from the ordered end, and 5 jnds from the disordered end of the discrimination scale.

Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin.

PubMed

Moscetti, Roberto; Radicetti, Emanuele; Monarca, Danilo; Cecchini, Massimo; Massantini, Riccardo

2015-10-01

This study investigates the possibility of using near infrared spectroscopy for the authentication of the 'Nocciola Romana' hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively, were achieved. Various pretreatments, such as standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. Near infrared spectroscopy could classify hazelnut according to the PDO 'Nocciola Romana' designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions. © 2014 Society of Chemical Industry.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

Reassessment of the psychometric characteristics and factor structure of the 'Perceived Stress Questionnaire' (PSQ): analysis in a sample of dental students.

PubMed

Montero-Marin, Jesús; Piva Demarzo, Marcelo Marcos; Pereira, Joao Paulo; Olea, Marina; García-Campayo, Javier

2014-01-01

The training to become a dentist can create psychological distress. The present study evaluates the structure of the 'Perceived Stress Questionnaire' (PSQ), its internal consistency model and interrelatedness with burnout, anxiety, depression and resilience among dental students. The study employed a cross-sectional design. A sample of Spanish dental students (n = 314) completed the PSQ, the 'Goldberg Anxiety and Depression Scale' (GADS), 'Connor-Davidson Resilience Scale' (10-item CD-RISC) and 'Maslach Burnout Inventory-Student Survey' (MBI-SS). The structure was estimated using Parallel Analysis from polychoric correlations. Unweighted Least Squares was the method for factor extraction, using the Item Response Theory to evaluate the discriminative power of items. Internal consistency was assessed by squaring the correlation between the latent true variable and the observed variable. The relationships between the PSQ and the other constructs were analysed using Spearman's coefficient. The results showed a PSQ structure through two sub-factors ('frustration' and 'tenseness') with regard to one general factor ('perceived stress'). Items that did not satisfy discriminative capacity were rejected. The model fit were acceptable (GFI = 0.98; RSMR = 0.06; AGFI = 0.98; NFI = 0.98; RFI = 0.98). All the factors showed adequate internal consistency as measured by the congeneric model (≥0.91). High and significant associations were observed between perceived stress and burnout, anxiety, depression and resilience. The PSQ showed a hierarchical bi-factor structure among Spanish dental students. Using the questionnaire as a uni-dimensional scale may be useful in perceived stress level discrimination, while the sub-factors could help us to refine perceived stress analysis and improve therapeutic processes.

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.

Reassessment of the Psychometric Characteristics and Factor Structure of the ‘Perceived Stress Questionnaire’ (PSQ): Analysis in a Sample of Dental Students

PubMed Central

Montero-Marin, Jesús; Piva Demarzo, Marcelo Marcos; Pereira, Joao Paulo; Olea, Marina; García-Campayo, Javier

2014-01-01

Background The training to become a dentist can create psychological distress. The present study evaluates the structure of the ‘Perceived Stress Questionnaire’ (PSQ), its internal consistency model and interrelatedness with burnout, anxiety, depression and resilience among dental students. Methods The study employed a cross-sectional design. A sample of Spanish dental students (n = 314) completed the PSQ, the ‘Goldberg Anxiety and Depression Scale’ (GADS), ‘Connor-Davidson Resilience Scale’ (10-item CD-RISC) and ‘Maslach Burnout Inventory-Student Survey’ (MBI-SS). The structure was estimated using Parallel Analysis from polychoric correlations. Unweighted Least Squares was the method for factor extraction, using the Item Response Theory to evaluate the discriminative power of items. Internal consistency was assessed by squaring the correlation between the latent true variable and the observed variable. The relationships between the PSQ and the other constructs were analysed using Spearman’s coefficient. Results The results showed a PSQ structure through two sub-factors (‘frustration’ and ‘tenseness’) with regard to one general factor (‘perceived stress’). Items that did not satisfy discriminative capacity were rejected. The model fit were acceptable (GFI = 0.98; RSMR = 0.06; AGFI = 0.98; NFI = 0.98; RFI = 0.98). All the factors showed adequate internal consistency as measured by the congeneric model (≥0.91). High and significant associations were observed between perceived stress and burnout, anxiety, depression and resilience. Conclusions The PSQ showed a hierarchical bi-factor structure among Spanish dental students. Using the questionnaire as a uni-dimensional scale may be useful in perceived stress level discrimination, while the sub-factors could help us to refine perceived stress analysis and improve therapeutic processes. PMID:24466330

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy.

PubMed

De Girolamo, A; Lippolis, V; Nordkvist, E; Visconti, A

2009-06-01

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 microm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 microg kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 microg kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 microg kg(-1) DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.

Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue.

PubMed

Yu, HaiYan; Zhao, Jie; Li, Fenghua; Tian, Huaixiang; Ma, Xia

2015-08-01

To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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®

Application of multispectral reflectance for early detection of tomato disease

NASA Astrophysics Data System (ADS)

Xu, Huirong; Zhu, Shengpan; Ying, Yibin; Jiang, Huanyu

2006-10-01

Automatic diagnosis of plant disease is important for plant management and environmental preservation in the future. The objective of this study is to use multispectral reflectance measurements to make an early discrimination between the healthy and infected plants by the strain of tobacco mosaic virus (TMV-U1) infection. There were reflectance changes in the visible (VIS) and near infrared spectroscopy (NIR) between the healthy and infected plants. Discriminant models were developed using discriminant partial least squares (DPLS) and Mahalanobis distance (MD). The DPLS models had a root mean square error of calibration (RMSEC) of 0.397 and correlation coefficient (r) of 0.59 and the MD model correctly classified 86.7% healthy plants and up to 91.7% infected plants.

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.

Acculturation Predicts Negative Affect and Shortened Telomere Length.

PubMed

Ruiz, R Jeanne; Trzeciakowski, Jerome; Moore, Tiffany; Ayers, Kimberly S; Pickler, Rita H

2016-10-12

Chronic stress may accelerate cellular aging. Telomeres, protective "caps" at the end of chromosomes, modulate cellular aging and may be good biomarkers for the effects of chronic stress, including that associated with acculturation. The purpose of this analysis was to examine telomere length (TL) in acculturating Hispanic Mexican American women and to determine the associations among TL, acculturation, and psychological factors. As part of a larger cross-sectional study of 516 pregnant Hispanic Mexican American women, we analyzed DNA in blood samples (N = 56) collected at 22-24 weeks gestation for TL as an exploratory measure using monochrome multiplex quantitative telomere polymerase chain reaction (PCR). We measured acculturation with the Acculturation Rating Scale for Mexican Americans, depression with the Beck Depression Inventory, discrimination with the Experiences of Discrimination Scale, and stress with the Perceived Stress Scale. TL was negatively moderately correlated with two variables of acculturation: Anglo orientation and greater acculturation-level scores. We combined these scores for a latent variable, acculturation, and we combined depression, stress, and discrimination scores in another latent variable, "negative affectivity." Acculturation and negative affectivity were bidirectionally correlated. Acculturation significantly negatively predicted TL. Using structural equation modeling, we found the model had an excellent fit with the root mean square error of approximation estimate = .0001, comparative fit index = 1.0, Tucker-Lewis index = 1.0, and standardized root mean square residual = .05. The negative effects of acculturation on the health of Hispanic women have been previously demonstrated. Findings from this analysis suggest a link between acculturation and TL, which may indicate accelerated cellular aging associated with overall poor health outcomes. © The Author(s) 2016.

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

Rapid discrimination and determination of antibiotics drugs in plastic syringes using near infrared spectroscopy with chemometric analysis: Application to amoxicillin and penicillin.

PubMed

Lê, Laetitia Minh Mai; Eveleigh, Luc; Hasnaoui, Ikram; Prognon, Patrice; Baillet-Guffroy, Arlette; Caudron, Eric

2017-05-10

The aim of this study was to investigate near infrared spectroscopy (NIRS) combined to chemometric analysis to discriminate and quantify three antibiotics by direct measurement in plastic syringes.Solutions of benzylpenicillin (PENI), amoxicillin (AMOX) and amoxicillin/clavulanic acid (AMOX/CLAV) were analyzed at therapeutic concentrations in glass vials and plastic syringes with NIR spectrometer by direct measurement. Chemometric analysis using partial least squares regression and discriminative analysis was conducted to develop qualitative and quantitative calibration models. Discrimination of the three antibiotics was optimal for concentrated solutions with 100% of accuracy. For quantitative analysis, the three antibiotics furnished a linear response (R²>0.9994) for concentrations ranging from 0.05 to 0.2 g/mL for AMOX, 0.1 to 1.0 MUI/mL for PENI and 0.005 to 0.05 g/mL for AMOX/CLAV with excellent repeatability (maximum 1.3%) and intermediate precision (maximum of 3.2%). Based on proposed models, 94.4% of analyzed AMOX syringes, 80.0% of AMOX/CLAV syringes and 85.7% of PENI syringes were compliant with a relative error including the limit of ± 15%.NIRS as rapid, non-invasive and non-destructive analytical method represents a potentially powerful tool to further develop for securing the drug administration circuit of healthcare institutions to ensure that patients receive the correct product at the right dose. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Temporal discrimination threshold with healthy aging.

PubMed

Ramos, Vesper Fe Marie Llaneza; Esquenazi, Alina; Villegas, Monica Anne Faye; Wu, Tianxia; Hallett, Mark

2016-07-01

The temporal discrimination threshold (TDT) is the shortest interstimulus interval at which a subject can perceive successive stimuli as separate. To investigate the effects of aging on TDT, we studied tactile TDT using the method of limits with 120% of sensory threshold in each hand for each of 100 healthy volunteers, equally divided among men and women, across 10 age groups, from 18 to 79 years. Linear regression analysis showed that age was significantly related to left-hand mean, right-hand mean, and mean of 2 hands with R-square equal to 0.08, 0.164, and 0.132, respectively. Reliability analysis indicated that the 3 measures had fair-to-good reliability (intraclass correlation coefficient: 0.4-0.8). We conclude that TDT is affected by age and has fair-to-good reproducibility using our technique. Published by Elsevier Inc.

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.

Rapid Isolation and Detection for RNA Biomarkers for TBI Diagnostics

DTIC Science & Technology

2015-10-01

V., Grape and wine sensory attributes correlate with pattern- based discrimination of Cabernet Sauvignon wines by a peptidic sensor array, Tetrahedron... wine samples. Partial Least Squares Regression (PLSR) was used for the correlation of wine sensory attributes to the peptide-based receptor...responses. Data analysis was done using the software XLSTAT Addinsoft, NewYork) and R.Absorbance values due to wine without the sensing ensembles were

The Shame and Guilt Scales of the Test of Self-Conscious Affect-Adolescent (TOSCA-A): Factor Structure, Concurrent and Discriminant Validity, and Measurement and Structural Invariance Across Ratings of Males and Females.

PubMed

Watson, Shaun; Gomez, Rapson; Gullone, Eleonora

2017-06-01

This study examined various psychometric properties of the items comprising the shame and guilt scales of the Test of Self-Conscious Affect-Adolescent. A total of 563 adolescents (321 females and 242 males) completed these scales, and also measures of depression and empathy. Confirmatory factor analysis provided support for an oblique two-factor model, with the originally proposed shame and guilt items comprising shame and guilt factors, respectively. Also, shame correlated with depression positively and had no relation with empathy. Guilt correlated with depression negatively and with empathy positively. Thus, there was support for the convergent and discriminant validity of the shame and guilt factors. Multiple-group confirmatory factor analysis comparing females and males, based on the chi-square difference test, supported full metric invariance, the intercept invariance of 26 of the 30 shame and guilt items, and higher latent mean scores among females for both shame and guilt. Comparisons based on the difference in root mean squared error of approximation values supported full measurement invariance and no gender difference for latent mean scores. The psychometric and practical implications of the findings are discussed.

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.

Do rats use shape to solve “shape discriminations”?

PubMed Central

Minini, Loredana; Jeffery, Kathryn J.

2006-01-01

Visual discrimination tasks are increasingly used to explore the neurobiology of vision in rodents, but it remains unclear how the animals solve these tasks: Do they process shapes holistically, or by using low-level features such as luminance and angle acuity? In the present study we found that when discriminating triangles from squares, rats did not use shape but instead relied on local luminance differences in the lower hemifield. A second experiment prevented this strategy by using stimuli—squares and rectangles—that varied in size and location, and for which the only constant predictor of reward was aspect ratio (ratio of height to width: a simple descriptor of “shape”). Rats eventually learned to use aspect ratio but only when no other discriminand was available, and performance remained very poor even at asymptote. These results suggest that although rats can process both dimensions simultaneously, they do not naturally solve shape discrimination tasks this way. This may reflect either a failure to visually process global shape information or a failure to discover shape as the discriminative stimulus in a simultaneous discrimination. Either way, our results suggest that simultaneous shape discrimination is not a good task for studies of visual perception in rodents. PMID:16705141

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.

Discrimination of transgenic soybean seeds by terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Wei; Liu, Changhong; Chen, Feng; Yang, Jianbo; Zheng, Lei

2016-10-01

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

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.

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.

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.

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-Squares Estimation (IDEAL) Magnetic Resonance Imaging as a Biomarker for Symptomatic Multiple Myeloma

PubMed Central

Takasu, Miyuki; Kaichi, Yoko; Tani, Chihiro; Date, Shuji; Akiyama, Yuji; Kuroda, Yoshiaki; Sakai, Akira; Awai, Kazuo

2015-01-01

Introduction To evaluate the effectiveness of iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) magnetic resonance imaging (MRI) to discriminate between symptomatic and asymptomatic myeloma in lumbar bone marrow without visible focal lesions. Materials and Methods The lumbar spine was examined with 3-T MRI in 11 patients with asymptomatic myeloma and 24 patients with symptomatic myeloma. The fat-signal fraction was calculated from the ratio of the signal intensity in the fat image divided by the signal intensity of the corresponding ROI in the in-phase IDEAL image. The t test was used to compare the asymptomatic and symptomatic groups. ROC curves were constructed to determine the ability of variables to discriminate between symptomatic and asymptomatic myeloma. Results Univariate analysis showed that β2-microglobulin and bone marrow plasma cell percent (BMPC%) were significantly higher and fat-signal fraction was significantly lower with symptomatic myeloma than with asymptomatic myeloma. Areas under the curve were 0.847 for β2;-microglobulin, 0.834 for fat-signal fraction, and 0.759 for BMPC%. Conclusion The fat-signal fraction as a biomarker for multiple myeloma enables discrimination of symptomatic myeloma from asymptomatic myeloma. The fat-signal fraction offers superior sensitivity and specificity to BMPC% of biopsy specimens. PMID:25706753

Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods.

PubMed

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-06-15

Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

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.

Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectrometry combined with chemometrics.

PubMed

Longobardi, F; Casiello, G; Cortese, M; Perini, M; Camin, F; Catucci, L; Agostiano, A

2015-12-01

The aim of this study was to predict the geographic origin of lentils by using isotope ratio mass spectrometry (IRMS) in combination with chemometrics. Lentil samples from two origins, i.e. Italy and Canada, were analysed obtaining the stable isotope ratios of δ(13)C, δ(15)N, δ(2)H, δ(18)O, and δ(34)S. A comparison between median values (U-test) highlighted statistically significant differences (p<0.05) for all isotopic parameters between the lentils produced in these two different geographic areas, except for δ(15)N. Applying principal component analysis, grouping of samples was observed on the basis of origin but with overlapping zones; consequently, two supervised discriminant techniques, i.e. partial least squares discriminant analysis and k-nearest neighbours algorithm were used. Both models showed good performances with external prediction abilities of about 93% demonstrating the suitability of the methods developed. Subsequently, isotopic determinations were also performed on the protein and starch fractions and the relevant results are reported. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

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.

Source Attribution of Cyanides Using Anionic Impurity Profiling, Stable Isotope Ratios, Trace Elemental Analysis and Chemometrics.

PubMed

Mirjankar, Nikhil S; Fraga, Carlos G; Carman, April J; Moran, James J

2016-02-02

Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ(13)C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

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.

[Rapid discriminating hogwash oil and edible vegetable oil using near infrared optical fiber spectrometer technique].

PubMed

Zhang, Bing-Fang; Yuan, Li-Bo; Kong, Qing-Ming; Shen, Wei-Zheng; Zhang, Bing-Xiu; Liu, Cheng-Hai

2014-10-01

In the present study, a new method using near infrared spectroscopy combined with optical fiber sensing technology was applied to the analysis of hogwash oil in blended oil. The 50 samples were a blend of frying oil and "nine three" soybean oil according to a certain volume ratio. The near infrared transmission spectroscopies were collected and the quantitative analysis model of frying oil was established by partial least squares (PLS) and BP artificial neural network The coefficients of determina- tion of calibration sets were 0.908 and 0.934 respectively. The coefficients of determination of validation sets were 0.961 and 0.952, the root mean square error of calibrations (RMSEC) was 0.184 and 0.136, and the root mean square error of predictions (RMSEP) was all 0.111 6. They conform to the model application requirement. At the same time, frying oil and qualified edible oil were identified with the principal component analysis (PCA), and the accurate rate was 100%. The experiment proved that near infrared spectral technology not only can quickly and accurately identify hogwash oil, but also can quantitatively detect hog- wash oil. This method has a wide application prospect in the detection of oil.

Effective Identification of Low-Gliadin Wheat Lines by Near Infrared Spectroscopy (NIRS): Implications for the Development and Analysis of Foodstuffs Suitable for Celiac Patients.

PubMed

García-Molina, María Dolores; García-Olmo, Juan; Barro, Francisco

2016-01-01

The aim of this work was to assess the ability of Near Infrared Spectroscopy (NIRS) to distinguish wheat lines with low gliadin content, obtained by RNA interference (RNAi), from non-transgenic wheat lines. The discriminant analysis was performed using both whole grain and flour. The transgenic sample set included 409 samples for whole grain sorting and 414 samples for flour experiments, while the non-transgenic set consisted of 126 and 156 samples for whole grain and flour, respectively. Samples were scanned using a Foss-NIR Systems 6500 System II instrument. Discrimination models were developed using the entire spectral range (400-2500 nm) and ranges of 400-780 nm, 800-1098 nm and 1100-2500 nm, followed by analysis of means of partial least square (PLS). Two external validations were made, using samples from the years 2013 and 2014 and a minimum of 99% of the flour samples and 96% of the whole grain samples were classified correctly. The results demonstrate the ability of NIRS to successfully discriminate between wheat samples with low-gliadin content and wild types. These findings are important for the development and analysis of foodstuff for celiac disease (CD) patients to achieve better dietary composition and a reduction in disease incidence.

Quality Evaluation and Chemical Markers Screening of Salvia miltiorrhiza Bge. (Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics.

PubMed

Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen

2017-03-17

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

PubMed Central

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan

2015-01-01

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines. PMID:26345990

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.

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

PubMed Central

Yu, Ke-Qiang; Zhao, Yan-Ru; Liu, Fei; He, Yong

2016-01-01

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil. PMID:27279284

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.

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.

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

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.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

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.

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates

NASA Astrophysics Data System (ADS)

Crivori, Patrizia; Zamora, Ismael; Speed, Bill; Orrenius, Christian; Poggesi, Italo

2004-03-01

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds. Abbreviations: ADME - absorption, distribution, metabolism and excretion; CYP - cytochrome P450; MIFs - molecular interaction fields; HTS - high throughput screening; DDI - drug-drug interactions; 3D - three-dimensional; PCA - principal components analysis; CPCA - consensus principal components analysis; PLS - partial least squares; PLSD - partial least squares discriminant; GRIND - grid independent descriptors; GRID - software originally created and developed by Professor Peter Goodford.

The ITE Land classification: Providing an environmental stratification of Great Britain.

PubMed

Bunce, R G; Barr, C J; Gillespie, M K; Howard, D C

1996-01-01

The surface of Great Britain (GB) varies continuously in land cover from one area to another. The objective of any environmentally based land classification is to produce classes that match the patterns that are present by helping to define clear boundaries. The more appropriate the analysis and data used, the better the classes will fit the natural patterns. The observation of inter-correlations between ecological factors is the basis for interpreting ecological patterns in the field, and the Institute of Terrestrial Ecology (ITE) Land Classification formalises such subjective ideas. The data inevitably comprise a large number of factors in order to describe the environment adequately. Single factors, such as altitude, would only be useful on a national basis if they were the only dominant causative agent of ecological variation.The ITE Land Classification has defined 32 environmental categories called 'land classes', initially based on a sample of 1-km squares in Great Britain but subsequently extended to all 240 000 1-km squares. The original classification was produced using multivariate analysis of 75 environmental variables. The extension to all squares in GB was performed using a combination of logistic discrimination and discriminant functions. The classes have provided a stratification for successive ecological surveys, the results of which have characterised the classes in terms of botanical, zoological and landscape features.The classification has also been applied to integrate diverse datasets including satellite imagery, soils and socio-economic information. A variety of models have used the structure of the classification, for example to show potential land use change under different economic conditions. The principal data sets relevant for planning purposes have been incorporated into a user-friendly computer package, called the 'Countryside Information System'.

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.

Classification of java tea (Orthosiphon aristatus) quality using FTIR spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Heryanto, R.; Pradono, D. I.; Marlina, E.; Darusman, L. K.

2017-05-01

Java tea (Orthosiphon aristatus) is a plant that widely used as a medicinal herb in Indonesia. Its quality is varying depends on various factors, such as cultivating area, climate and harvesting time. This study aimed to investigate the effectiveness of FTIR spectroscopy coupled with chemometrics for discriminating the quality of java tea from different cultivating area. FTIR spectra of ethanolic extracts were collected from five different regions of origin of java tea. Prior to chemometrics evaluation, spectra were pre-processed by using baselining, normalization and derivatization. Principal Components Analysis (PCA) was used to reduce the spectra to two PCs, which explained 73% of the total variance. Score plot of two PCs showed groupings of the samples according to their regions of origin. Furthermore, Partial Least Squares-Discriminant Analysis (PLSDA) was applied to the pre-processed data. The approach produced an external validation success rate of 100%. This study shows that FTIR analysis and chemometrics has discriminatory power to classify java tea based on its quality related to the region of origin.

Forensic Comparison of Soil Samples Using Nondestructive Elemental Analysis.

PubMed

Uitdehaag, Stefan; Wiarda, Wim; Donders, Timme; Kuiper, Irene

2017-07-01

Soil can play an important role in forensic cases in linking suspects or objects to a crime scene by comparing samples from the crime scene with samples derived from items. This study uses an adapted ED-XRF analysis (sieving instead of grinding to prevent destruction of microfossils) to produce elemental composition data of 20 elements. Different data processing techniques and statistical distances were evaluated using data from 50 samples and the log-LR cost (C llr ). The best performing combination, Canberra distance, relative data, and square root values, is used to construct a discriminative model. Examples of the spatial resolution of the method in crime scenes are shown for three locations, and sampling strategy is discussed. Twelve test cases were analyzed, and results showed that the method is applicable. The study shows how the combination of an analysis technique, a database, and a discriminative model can be used to compare multiple soil samples quickly. © 2016 American Academy of Forensic Sciences.

Factor structure and psychometric properties of the Fertility Problem Inventory–Short Form

PubMed Central

Zurlo, Maria Clelia; Cattaneo Della Volta, Maria Franscesca; Vallone, Federica

2017-01-01

The study analyses factor structure and psychometric properties of the Italian version of the Fertility Problem Inventory–Short Form. A sample of 206 infertile couples completed the Italian version of Fertility Problem Inventory (46 items) with demographics, State Anxiety Scale of State-Trait Anxiety Inventory (Form Y), Edinburgh Depression Scale and Dyadic Adjustment Scale, used to assess convergent and discriminant validity. Confirmatory factor analysis was unsatisfactory (comparative fit index = 0.87; Tucker-Lewis Index = 0.83; root mean square error of approximation = 0.17), and Cronbach’s α (0.95) revealed a redundancy of items. Exploratory factor analysis was carried out deleting cross-loading items, and Mokken scale analysis was applied to verify the items homogeneity within the reduced subscales of the questionnaire. The Fertility Problem Inventory–Short Form consists of 27 items, tapping four meaningful and reliable factors. Convergent and discriminant validity were confirmed. Findings indicated that the Fertility Problem Inventory–Short Form is a valid and reliable measure to assess infertility-related stress dimensions. PMID:29379625

Authentication and Quantitation of Fraud in Extra Virgin Olive Oils Based on HPLC-UV Fingerprinting and Multivariate Calibration

PubMed Central

Carranco, Núria; Farrés-Cebrián, Mireia; Saurina, Javier

2018-01-01

High performance liquid chromatography method with ultra-violet detection (HPLC-UV) fingerprinting was applied for the analysis and characterization of olive oils, and was performed using a Zorbax Eclipse XDB-C8 reversed-phase column under gradient elution, employing 0.1% formic acid aqueous solution and methanol as mobile phase. More than 130 edible oils, including monovarietal extra-virgin olive oils (EVOOs) and other vegetable oils, were analyzed. Principal component analysis results showed a noticeable discrimination between olive oils and other vegetable oils using raw HPLC-UV chromatographic profiles as data descriptors. However, selected HPLC-UV chromatographic time-window segments were necessary to achieve discrimination among monovarietal EVOOs. Partial least square (PLS) regression was employed to tackle olive oil authentication of Arbequina EVOO adulterated with Picual EVOO, a refined olive oil, and sunflower oil. Highly satisfactory results were obtained after PLS analysis, with overall errors in the quantitation of adulteration in the Arbequina EVOO (minimum 2.5% adulterant) below 2.9%. PMID:29561820

Short wavelength Raman spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages.

PubMed

Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A

2015-01-01

Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.

[Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

PubMed

Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

2015-11-01

Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect feature of 86 Hawthorn could be recognized. Lastly, the detect precision of bruised, insect damage and two-defect samples is 95.65%, 86.67% and 100%, respectively. This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise, insect damage, calyx, and stem-end in hawthorn fruit in qualitative analysis and feature detection which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.

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.

[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.

Role of Gamma-Band Synchronization in Priming of Form Discrimination for Multiobject Displays

ERIC Educational Resources Information Center

Lu, Hongjing; Morrison, Robert G.; Hummel, John E.; Holyoak, Keith J.

2006-01-01

Previous research has shown that synchronized flicker can facilitate detection of a single Kanizsa square. The present study investigated the role of temporally structured priming in discrimination tasks involving perceptual relations between multiple Kanizsa-type figures. Results indicate that visual information presented as temporally structured…

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

DNA barcoding the native flowering plants and conifers of Wales.

PubMed

de Vere, Natasha; Rich, Tim C G; Ford, Col R; Trinder, Sarah A; Long, Charlotte; Moore, Chris W; Satterthwaite, Danielle; Davies, Helena; Allainguillaume, Joel; Ronca, Sandra; Tatarinova, Tatiana; Garbett, Hannah; Walker, Kevin; Wilkinson, Mike J

2012-01-01

We present the first national DNA barcode resource that covers the native flowering plants and conifers for the nation of Wales (1143 species). Using the plant DNA barcode markers rbcL and matK, we have assembled 97.7% coverage for rbcL, 90.2% for matK, and a dual-locus barcode for 89.7% of the native Welsh flora. We have sampled multiple individuals for each species, resulting in 3304 rbcL and 2419 matK sequences. The majority of our samples (85%) are from DNA extracted from herbarium specimens. Recoverability of DNA barcodes is lower using herbarium specimens, compared to freshly collected material, mostly due to lower amplification success, but this is balanced by the increased efficiency of sampling species that have already been collected, identified, and verified by taxonomic experts. The effectiveness of the DNA barcodes for identification (level of discrimination) is assessed using four approaches: the presence of a barcode gap (using pairwise and multiple alignments), formation of monophyletic groups using Neighbour-Joining trees, and sequence similarity in BLASTn searches. These approaches yield similar results, providing relative discrimination levels of 69.4 to 74.9% of all species and 98.6 to 99.8% of genera using both markers. Species discrimination can be further improved using spatially explicit sampling. Mean species discrimination using barcode gap analysis (with a multiple alignment) is 81.6% within 10×10 km squares and 93.3% for 2×2 km squares. Our database of DNA barcodes for Welsh native flowering plants and conifers represents the most complete coverage of any national flora, and offers a valuable platform for a wide range of applications that require accurate species identification.

DNA Barcoding the Native Flowering Plants and Conifers of Wales

PubMed Central

de Vere, Natasha; Rich, Tim C. G.; Ford, Col R.; Trinder, Sarah A.; Long, Charlotte; Moore, Chris W.; Satterthwaite, Danielle; Davies, Helena; Allainguillaume, Joel; Ronca, Sandra; Tatarinova, Tatiana; Garbett, Hannah; Walker, Kevin; Wilkinson, Mike J.

2012-01-01

We present the first national DNA barcode resource that covers the native flowering plants and conifers for the nation of Wales (1143 species). Using the plant DNA barcode markers rbcL and matK, we have assembled 97.7% coverage for rbcL, 90.2% for matK, and a dual-locus barcode for 89.7% of the native Welsh flora. We have sampled multiple individuals for each species, resulting in 3304 rbcL and 2419 matK sequences. The majority of our samples (85%) are from DNA extracted from herbarium specimens. Recoverability of DNA barcodes is lower using herbarium specimens, compared to freshly collected material, mostly due to lower amplification success, but this is balanced by the increased efficiency of sampling species that have already been collected, identified, and verified by taxonomic experts. The effectiveness of the DNA barcodes for identification (level of discrimination) is assessed using four approaches: the presence of a barcode gap (using pairwise and multiple alignments), formation of monophyletic groups using Neighbour-Joining trees, and sequence similarity in BLASTn searches. These approaches yield similar results, providing relative discrimination levels of 69.4 to 74.9% of all species and 98.6 to 99.8% of genera using both markers. Species discrimination can be further improved using spatially explicit sampling. Mean species discrimination using barcode gap analysis (with a multiple alignment) is 81.6% within 10×10 km squares and 93.3% for 2×2 km squares. Our database of DNA barcodes for Welsh native flowering plants and conifers represents the most complete coverage of any national flora, and offers a valuable platform for a wide range of applications that require accurate species identification. PMID:22701588

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.

Nontargeted metabolomics approach for the differentiation of cultivation ages of mountain cultivated ginseng leaves using UHPLC/QTOF-MS.

PubMed

Chang, Xiangwei; Zhang, Juanjuan; Li, Dekun; Zhou, Dazheng; Zhang, Yuling; Wang, Jincheng; Hu, Bing; Ju, Aichun; Ye, Zhengliang

2017-07-15

The adulteration or falsification of the cultivation age of mountain cultivated ginseng (MCG) has been a serious problem in the commercial MCG market. To develop an efficient discrimination tool for the cultivation age and to explore potential age-dependent markers, an optimized ultra high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS)-based metabolomics approach was applied in the global metabolite profiling of 156 MCG leaf (MGL) samples aged from 6 to 18 years. Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to compare the derived patterns between MGL samples of different cultivation ages. The present study demonstrated that 6-18-year-old MGL samples can be successfully discriminated using two simple successive steps, together with four PLS-DA discrimination models. Furthermore, 39 robust age-dependent markers enabling differentiation among the 6-18-year-old MGL samples were discovered. The results were validated by a permutation test and an external test set to verify the predictability and reliability of the established discrimination models. More importantly, without destroying the MCG roots, the proposed approach could also be applied to discriminate MCG root ages indirectly, using a minimum amount of homophyletic MGL samples combined with the established four PLS-DA models and identified markers. Additionally, to the best of our knowledge, this is the first study in which 6-18-year-old MCG root ages have been nondestructively differentiated by analyzing homophyletic MGL samples using UHPLC/QTOF-MS analysis and two simple successive steps together with four PLS-DA models. The method developed in this study can be used as a standard protocol for discriminating and predicting MGL ages directly and homophyletic MCG root ages indirectly. Copyright © 2017 Elsevier B.V. All rights reserved.

Crop/weed discrimination using near-infrared reflectance spectroscopy (NIRS)

NASA Astrophysics Data System (ADS)

Zhang, Yun; He, Yong

2006-09-01

The traditional uniform herbicide application often results in an over chemical residues on soil, crop plants and agriculture produce, which have imperiled the environment and food security. Near-infrared reflectance spectroscopy (NIRS) offers a promising means for weed detection and site-specific herbicide application. In laboratory, a total of 90 samples (30 for each species) of the detached leaves of two weeds, i.e., threeseeded mercury (Acalypha australis L.) and fourleafed duckweed (Marsilea quadrfolia L.), and one crop soybean (Glycine max) was investigated for NIRS on 325- 1075 nm using a field spectroradiometer. 20 absorbance samples of each species after pretreatment were exported and the lacked Y variables were assigned independent values for partial least squares (PLS) analysis. During the combined principle component analysis (PCA) on 400-1000 nm, the PC1 and PC2 could together explain over 91% of the total variance and detect the three plant species with 98.3% accuracy. The full-cross validation results of PLS, i.e., standard error of prediction (SEP) 0.247, correlation coefficient (r) 0.954 and root mean square error of prediction (RMSEP) 0.245, indicated an optimum model for weed identification. By predicting the remaining 10 samples of each species in the PLS model, the results with deviation presented a 100% crop/weed detection rate. Thus, it could be concluded that PLS was an available alternative of for qualitative weed discrimination on NTRS.

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.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice

PubMed Central

Nakamura, Toshihide; Sekiyama, Yasuyo; Kikuchi, Jun

2017-01-01

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods. PMID:28759594

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

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.

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.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

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.

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%.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

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.

Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine.

PubMed

Edmands, William M B; Ferrari, Pietro; Scalbert, Augustin

2014-11-04

Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

PubMed Central

Kong, Wenwen; Zhang, Chu; Huang, Weihao

2018-01-01

Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems. PMID:29300315

Fourier transform near-infrared spectroscopy application for sea salt quality evaluation.

PubMed

Galvis-Sánchez, Andrea C; Lopes, João Almeida; Delgadillo, Ivonne; Rangel, António O S S

2011-10-26

Near-infrared (NIR) spectroscopy in diffuse reflectance mode was explored with the objective of discriminating sea salts according to their quality type (traditional salt vs "flower of salt") and geographical origin (Atlantic vs Mediterranean). Sea salts were also analyzed in terms of Ca(2+), Mg(2+), K(+), alkalinity, and sulfate concentrations to support spectroscopic results. High concentrations of Mg(2+) and K(+) characterized Atlantic samples, while a high Ca(2+) content was observed in traditional sea salts. A partial least-squares discriminant analysis model considering the 8500-7500 cm(-1) region permitted the discrimination of salts by quality types. The regions 4650-4350 and 5900-5500 cm(-1) allowed salts classification according to their geographical origin. It was possible to classify correctly 85.3 and 94.8% of the analyzed samples according to the salt type and to the geographical origin, respectively. These results demonstrated that NIR spectroscopy is a suitable and very efficient tool for sea salt quality evaluation.

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.

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.

Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data

PubMed Central

Lelong, Camille C. D.; Roger, Jean-Michel; Brégand, Simon; Dubertret, Fabrice; Lanore, Mathieu; Sitorus, Nurul A.; Raharjo, Doni A.; Caliman, Jean-Pierre

2010-01-01

Fungal disease detection in perennial crops is a major issue in estate management and production. However, nowadays such diagnostics are long and difficult when only made from visual symptom observation, and very expensive and damaging when based on root or stem tissue chemical analysis. As an alternative, we propose in this study to evaluate the potential of hyperspectral reflectance data to help detecting the disease efficiently without destruction of tissues. This study focuses on the calibration of a statistical model of discrimination between several stages of Ganoderma attack on oil palm trees, based on field hyperspectral measurements at tree scale. Field protocol and measurements are first described. Then, combinations of pre-processing, partial least square regression and linear discriminant analysis are tested on about hundred samples to prove the efficiency of canopy reflectance in providing information about the plant sanitary status. A robust algorithm is thus derived, allowing classifying oil-palm in a 4-level typology, based on disease severity from healthy to critically sick stages, with a global performance close to 94%. Moreover, this model discriminates sick from healthy trees with a confidence level of almost 98%. Applications and further improvements of this experiment are finally discussed. PMID:22315565

Joint Entropy for Space and Spatial Frequency Domains Estimated from Psychometric Functions of Achromatic Discrimination

PubMed Central

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised. PMID:24466158

Joint entropy for space and spatial frequency domains estimated from psychometric functions of achromatic discrimination.

PubMed

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised.

Effective Identification of Low-Gliadin Wheat Lines by Near Infrared Spectroscopy (NIRS): Implications for the Development and Analysis of Foodstuffs Suitable for Celiac Patients

PubMed Central

García-Molina, María Dolores; García-Olmo, Juan; Barro, Francisco

2016-01-01

Scope The aim of this work was to assess the ability of Near Infrared Spectroscopy (NIRS) to distinguish wheat lines with low gliadin content, obtained by RNA interference (RNAi), from non-transgenic wheat lines. The discriminant analysis was performed using both whole grain and flour. The transgenic sample set included 409 samples for whole grain sorting and 414 samples for flour experiments, while the non-transgenic set consisted of 126 and 156 samples for whole grain and flour, respectively. Methods and Results Samples were scanned using a Foss-NIR Systems 6500 System II instrument. Discrimination models were developed using the entire spectral range (400–2500 nm) and ranges of 400–780 nm, 800–1098 nm and 1100–2500 nm, followed by analysis of means of partial least square (PLS). Two external validations were made, using samples from the years 2013 and 2014 and a minimum of 99% of the flour samples and 96% of the whole grain samples were classified correctly. Conclusions The results demonstrate the ability of NIRS to successfully discriminate between wheat samples with low-gliadin content and wild types. These findings are important for the development and analysis of foodstuff for celiac disease (CD) patients to achieve better dietary composition and a reduction in disease incidence. PMID:27018786

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.

Evaluation of objective structured clinical examination for advanced orthodontic education 12 years after introduction.

PubMed

Fields, Henry W; Kim, Do-Gyoon; Jeon, Minjeong; Firestone, Allen R; Sun, Zongyang; Shanker, Shiva; Mercado, Ana M; Deguchi, Toru; Vig, Katherine W L

2017-05-01

Advanced education programs in orthodontics must ensure student competency in clinical skills. An objective structure clinical examination has been used in 1 program for over a decade. The results were analyzed cross-sectionally and longitudinally to provide insights regarding the achievement of competency, student growth, question difficulty, question discrimination, and question predictive ability. In this study, we analyzed 218 (82 first-year, 68 second-year, and 68 third-year classes) scores of each station from 85 orthodontic students. The grades originated from 13 stations and were collected anonymously for 12 consecutive years during the first 2 decades of the 2000s. The stations tested knowledge and skills regarding dental relationships, analyzing a cephalometric tracing, performing a diagnostic skill, identifying cephalometric points, bracket placement, placing first-order and second-order bends, forming a loop, placing accentuated third-order bends, identifying problems and planning mixed dentition treatment, identifying problems and planning adolescent dentition treatment, identifying problems and planning nongrowing skeletal treatment, superimposing cephalometric tracings, and interpreting cephalometric superimpositions. Results were evaluated using multivariate analysis of variance, chi-square tests, and latent growth analysis. The multivariate analysis of variance showed that all stations except 3 (analyzing a cephalometric tracing, forming a loop, and identifying cephalometric points) had significantly lower mean scores for the first-year student class than the second- and third-year classes (P <0.028); scores between the second- and third-year student classes were not significantly different (P >0.108). The chi-square analysis of the distribution of the number of noncompetent item responses decreased from the first to the second years (P <0.0003), from the second to the third years (P <0.0042), and from the first to the third years (P <0.00003). The latent growth analysis showed a wide range of difficulty and discrimination between questions. It also showed continuous growth for some areas and the ability of 6 questions to predict competency at greater than the 80% level. Objective structure clinical examinations can provide a method of evaluating student performance and curriculum impact over time, but cross-sectional and longitudinal analyses of the results may not be complementary. Significant learning appears to occur during all years of a 3-year program. Valuable questions were both easy and difficult, discriminating and not discriminating, and came from all domains: diagnostic, technical, and evaluation/synthesis. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

PubMed Central

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-01-01

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation. PMID:25384107

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy.

PubMed

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-11-06

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50-16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%-90% and 3%-7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of <24%, 24-30%, >30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

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.

Mental Health Disparities Within the LGBT Population: A Comparison Between Transgender and Nontransgender Individuals

PubMed Central

Su, Dejun; Irwin, Jay A.; Fisher, Christopher; Ramos, Athena; Kelley, Megan; Mendoza, Diana Ariss Rogel; Coleman, Jason D.

2016-01-01

Abstract Purpose: This study assessed within a Midwestern LGBT population whether, and the extent to which, transgender identity was associated with elevated odds of reported discrimination, depression symptoms, and suicide attempts. Methods: Based on survey data collected online from respondents who self-identified as lesbian, gay, bisexual, and/or transgender persons over the age of 19 in Nebraska in 2010, this study performed bivariate t- or chi-square tests and multivariate logistic regression analysis to examine differences in reported discrimination, depression symptoms, suicide attempts, and self-acceptance of LGBT identity between 91 transgender and 676 nontransgender respondents. Results: After controlling for the effects of selected confounders, transgender identity was associated with higher odds of reported discrimination (OR=2.63, p<0.01), depression symptoms (OR=2.33, p<0.05), and attempted suicides (OR=2.59, p<0.01) when compared with nontransgender individuals. Self-acceptance of LGBT identity was associated with substantially lower odds of reporting depression symptoms (OR=0.46, p<0.001). Conclusion: Relative to nontransgender LGB individuals, transgender individuals were more likely to report discrimination, depression symptoms, and attempted suicides. Lack of self-acceptance of LGBT identity was associated with depression symptoms among transgender individuals. PMID:29159294

Mental Health Disparities Within the LGBT Population: A Comparison Between Transgender and Nontransgender Individuals.

PubMed

Su, Dejun; Irwin, Jay A; Fisher, Christopher; Ramos, Athena; Kelley, Megan; Mendoza, Diana Ariss Rogel; Coleman, Jason D

2016-01-01

Purpose: This study assessed within a Midwestern LGBT population whether, and the extent to which, transgender identity was associated with elevated odds of reported discrimination, depression symptoms, and suicide attempts. Methods: Based on survey data collected online from respondents who self-identified as lesbian, gay, bisexual, and/or transgender persons over the age of 19 in Nebraska in 2010, this study performed bivariate t - or chi-square tests and multivariate logistic regression analysis to examine differences in reported discrimination, depression symptoms, suicide attempts, and self-acceptance of LGBT identity between 91 transgender and 676 nontransgender respondents. Results: After controlling for the effects of selected confounders, transgender identity was associated with higher odds of reported discrimination (OR=2.63, p <0.01), depression symptoms (OR=2.33, p <0.05), and attempted suicides (OR=2.59, p <0.01) when compared with nontransgender individuals. Self-acceptance of LGBT identity was associated with substantially lower odds of reporting depression symptoms (OR=0.46, p <0.001). Conclusion: Relative to nontransgender LGB individuals, transgender individuals were more likely to report discrimination, depression symptoms, and attempted suicides. Lack of self-acceptance of LGBT identity was associated with depression symptoms among transgender individuals.

[External therapy of plasma cell mastitis by jiuyi powder using partial least-squares discriminant analysis: a safety analysis].

PubMed

Ye, Mei-na; Yang, Ming; Cheng, Yi-qin; Wang, Bing; Zhu, Ying; Xia, Ya-ru; Meng, Tian; Chen, Hao; Chen, Li-ying; Cheng, Hong-feng

2015-04-01

To evaluate the safety and the clinical value of external use of jiuyi Powder (JP) in treating plasma cell mastitis using partial least-squares discriminant analysis (PLSDA). Totally 50 patients with plasma cell mastitis treated by external use of JP were observed and biochemical examinations of blood and urine detected before application, at day 4 after application, at day 1 and 14 after discontinuation. Blood mercury and urinary mercury were detected before application, at day 1, 4, and 7 after application, at day 1 and 14 after discontinuation. Urinary mercury was also detected at 28 after discontinuation and 3 months after discontinuation. The information of wound, days of external application and the total dosage of external application were recorded before application, at day 1, 4, and 7 after application, as well as at day 1 after discontinuation. Then a discriminant model covering potential safety factors was set up by PLSDA after screening safety indices with important effects. The applicability of the model was assessed using area under ROC curve. Potential safety factors were assessed using variable importance in the projection (VIP). Urinary β2-microglobulin (β2-MG), urinary N-acetyl-β-D-glucosaminidase (NAG), 24 h urinary protein, and urinary α1-microglobulin (α1-MG) were greatly affected by external use of JP in treating plasma cell mastitis. The accuracy rate of PLSDA discriminate model was 74. 00%. The sensitivity, specificity, and the area under ROC curve was 0. 7826, 0. 7037, and 0. 8084, respectively. Three factors with greater effect on the potential safety were screened as follows: pre-application volume of the sore cavity, days of external application, and the total dosage of external application. PLSDA method could be used in analyzing bioinformation of clinical Chinese medicine. Urinary β2-MG and urinary NAG were two main safety monitoring indices. Days of external application and the total dosage of external application were main factors influencing blood mercury and urine mercury. A safety classification simulation model of treating plasma cell mastitis by external therapy of JP was established by the two factors, which could be used to assess the safety of external application of JP to some extent.

Discrimination of serum Raman spectroscopy between normal and colorectal cancer

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Yu, Ting; Li, Siqi

2011-07-01

Raman spectroscopy of tissues has been widely studied for the diagnosis of various cancers, but biofluids were seldom used as the analyte because of the low concentration. Herein, serum of 30 normal people, 46 colon cancer, and 44 rectum cancer patients were measured Raman spectra and analyzed. The information of Raman peaks (intensity and width) and that of the fluorescence background (baseline function coefficients) were selected as parameters for statistical analysis. Principal component regression (PCR) and partial least square regression (PLSR) were used on the selected parameters separately to see the performance of the parameters. PCR performed better than PLSR in our spectral data. Then linear discriminant analysis (LDA) was used on the principal components (PCs) of the two regression method on the selected parameters, and a diagnostic accuracy of 88% and 83% were obtained. The conclusion is that the selected features can maintain the information of original spectra well and Raman spectroscopy of serum has the potential for the diagnosis of colorectal cancer.

Age, Body Mass Index, and Daytime and Nocturnal Hypoxia as Predictors of Hypertension in Patients With Obstructive Sleep Apnea.

PubMed

Natsios, Georgios; Pastaka, Chaido; Vavougios, Georgios; Zarogiannis, Sotirios G; Tsolaki, Vasiliki; Dimoulis, Andreas; Seitanidis, Georgios; Gourgoulianis, Konstantinos I

2016-02-01

A growing body of evidence links obstructive sleep apnea (OSA) with hypertension. The authors performed a retrospective cohort study using the University Hospital of Larissa Sleep Apnea Database (1501 patients) to determine predictors of in-laboratory diagnosed OSA for development of hypertension. Differences in continuous variables were assessed via independent samples t test, whereas discrete variables were compared by Pearson's chi-square test. Multivariate analysis was performed via discriminant function analysis. There were several significant differences between hypertensive and normotensive patients. Age, body mass index, comorbidity, daytime oxygen saturation, and indices of hypoxia during sleep were deemed the most accurate predictors of hypertension, whereas apnea-hypopnea index and desaturation index were not. The single derived discriminant function was statistically significant (Wilk's lambda=0.771, χ(2) =289.070, P<.0001). Daytime and nocturnal hypoxia as consequences of chronic intermittent hypoxia play a central role in OSA-related hypertension and should be further evaluated as possible severity markers in OSA. ©2015 Wiley Periodicals, Inc.

Improved neutron-gamma discrimination for a {sup 6}Li-glass neutron detector using digital signal analysis methods

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

Wang, C. L., E-mail: wangc@ornl.gov; Riedel, R. A.

2016-01-15

A {sup 6}Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at Spallation Neutron Source. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10{sup 4}. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, six digital signal analysis methods of individual waveforms acquired from photomultiplier tubes were proposed using (i) charge integration, (ii) pulse-amplitude histograms, (iii) power spectrum analysis combined with the maximum pulse-amplitude, (iv) two event parameters (a{sub 1}, b{submore » 0}) obtained from a Wiener filter, (v) an effective amplitude (m) obtained from an adaptive least-mean-square filter, and (vi) a cross-correlation coefficient between individual and reference waveforms. The NGD ratios are about 70 times those from the traditional PHA method. Our results indicate the NGD capabilities of neutron Anger cameras based on GS20 scintillators can be significantly improved with digital signal analysis methods.« less

Colour discrimination ellipses in choroideremia.

PubMed

Seitz, Immanuel P; Jolly, Jasleen K; Dominik Fischer, M; Simunovic, Matthew P

2018-04-01

The purpose of this study was to characterise alterations in colour discrimination in a cohort of patients with choroideremia prior to gene therapy, using a test previously validated for use in patients with retinal dystrophies. We tested 20 eyes of 10 patients with a diagnosis of choroideremia and an age-matched cohort of 10 eyes of 10 normal controls using the "Cambridge Colour Test" (CCT), in which subjects are required to distinguish the gap in a C presented in one of 4 orientations in a Stilling-type array. Colour discrimination was probed along eight axes in the CIE L*u*v* colour space, and the resulting data were plotted in the CIE 1976 chromaticity diagram and fitted with least-squares ellipses. Subsequently, we estimated the achromatic area for each subject by calculating the area of the resultant discrimination ellipse and calculated sensitivity thresholds along relevant colour confusion axes. Colour discrimination-as quantified by log 10 of the ellipse area expressed in square 1/1000th 2 units in CIE 1976-was 2.26 (range 1.82 to 2.67) for normal subjects and 3.85 (range 2.35 to 5.41) for choroideremia patients. There was a statistically significant correlation between both achromatic area and red-green colour discrimination at the CCT and BCVA, and to a lesser degree between blue colour discrimination at the CCT and BCVA. The majority of ellipses in choroideremia were aligned close to the tritan axis, and loss of sensitivity was significantly larger in the tritan direction than in the red-green. The majority of our patients demonstrated greater loss in tritan discrimination than in red-green colour discrimination using the CCT. There was a significant correlation between achromatic area and BCVA. In keeping with our current understanding of the machinery of colour vision, there was a significant correlation between BCVA and colour discrimination thresholds, which was stronger for red-green colour discrimination, than for tritan colour discrimination. We propose that this and similar tests of colour discrimination may prove to be suitable tools for assessing functional outcomes in gene therapy trials for choroideremia.

Identification of milk origin and process-induced changes in milk by stable isotope ratio mass spectrometry.

PubMed

Scampicchio, Matteo; Mimmo, Tanja; Capici, Calogero; Huck, Christian; Innocente, Nadia; Drusch, Stephan; Cesco, Stefano

2012-11-14

Stable isotope values were used to develop a new analytical approach enabling the simultaneous identification of milk samples either processed with different heating regimens or from different geographical origins. The samples consisted of raw, pasteurized (HTST), and ultrapasteurized (UHT) milk from different Italian origins. The approach consisted of the analysis of the isotope ratio of δ¹³C and δ¹⁵N for the milk samples and their fractions (fat, casein, and whey). The main finding of this work is that as the heat processing affects the composition of the milk fractions, changes in δ¹³C and δ¹⁵N were also observed. These changes were used as markers to develop pattern recognition maps based on principal component analysis and supervised classification models, such as linear discriminant analysis (LDA), multivariate regression (MLR), principal component regression (PCR), and partial least-squares (PLS). The results give proof of the concept that isotope ratio mass spectroscopy can discriminate simultaneously between milk samples according to their geographical origin and type of processing.

Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression

NASA Astrophysics Data System (ADS)

Zhao, Jing; Jung, Yang-Hee; Jang, Choon-Gon; Chun, Kwang-Hoon; Kwon, Sung Won; Lee, Jeongmi

2015-03-01

Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses. Body weight measurement and behavior tests including an open field test and the forced swimming test were completed with the mice as a measure of the phenotypes of depression and antidepressive effects. As a result, 23 metabolites that had been differentially expressed among the control, CMS, and antidepressant-treated groups demonstrated that amino acid metabolism, energy metabolism, adenosine receptors, and neurotransmitters are commonly perturbed by drug treatment. Potential predictive markers for treatment effect were identified: myo-inositol for fluoxetine and lysine and oleic acid for imipramine. Collectively, the current study provides insights into the molecular mechanisms of the antidepressant effects of two widely used medications.

[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.

Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile.

PubMed

Jing, Fangyu; Hu, Xin; Cao, Yunfeng; Xu, Minghao; Wang, Yuanyuan; Jing, Yu; Hu, Xiaodan; Gao, Yu; Zhu, Zhitu

2018-06-01

Patients with gastric ulcer (GU) have a significantly higher risk of developing gastric cancer (GC), especially within 2 years after diagnosis. The main way to improve the prognosis of GC is to predict the tumorigenesis and metastasis in the early stage. The objective of this study was to demonstrate the ability of human plasma amino acid metabolic profile for discriminating GC and GU. In this study, we first used liquid chromatography-tandem mass spectrometry technique to characterize the plasma amino acid metabolism in GC and GU patients. Plasma samples were collected from 84 GC patients and 82 GU patients, and 22 amino acids were detected in each patient. Partial least squares-discriminant analysis model was performed to analyze the data of these amino acids. We observed seven differential amino acids between GC and GU. A regression analysis model was established using these seven amino acids. Finally, a panel of five differential amino acids, including glutamine, ornithine, histidine, arginine and tryptophan, was identified for discriminating GC and GU with good specificity and sensitivity. The receiver operating characteristic curve was used to evaluate diagnostic ability of the regression model and area under the curve was 0.922. In conclusion, this study demonstrated the potential values of plasma amino acid metabolic profile and metabolomic analysis technique in assisting diagnosis of GC. More studies are needed to highlight the theoretical strengths of metabolomics to understand the potential metabolic mechanisms in GC. © 2018 IUBMB Life, 70(6):553-562, 2018. © 2018 International Union of Biochemistry and Molecular Biology.

Hyperspectral Imaging and Support Vector Machine: A Powerful Combination to Differentiate Black Cohosh (Actaea racemosa) from Other Cohosh Species.

PubMed

Tankeu, Sidonie; Vermaak, Ilze; Chen, Weiyang; Sandasi, Maxleene; Kamatou, Guy; Viljoen, Alvaro

2018-04-01

Actaea racemosa (black cohosh) has a history of traditional use in the treatment of general gynecological problems. However, the plant is known to be vulnerable to adulteration with other cohosh species. This study evaluated the use of shortwave infrared hyperspectral imaging (SWIR-HSI) in tandem with chemometric data analysis as a fast alternative method for the discrimination of four cohosh species ( Actaea racemosa, Actaea podocarpa, Actaea pachypoda, Actaea cimicifuga ) and 36 commercial products labelled as black cohosh. The raw material and commercial products were analyzed using SWIR-HSI and ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) followed by chemometric modeling. From SWIR-HSI data (920 - 2514 nm), the range containing the discriminating information of the four species was identified as 1204 - 1480 nm using Matlab software. After reduction of the data set range, partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models with coefficients of determination ( R2 ) of ≥ 0.8 were created. The novel SVM-DA model showed better predictions and was used to predict the commercial product content. Seven out of 36 commercial products were recognized by the SVM-DA model as being true black cohosh while 29 products indicated adulteration. Analysis of the UHPLC-MS data demonstrated that six commercial products could be authentic black cohosh. This was confirmed using the fragmentation patterns of three black cohosh markers (cimiracemoside C; 12- β ,21-dihydroxycimigenol-3- O -L-arabinoside; and 24- O -acetylhydroshengmanol-3- O - β -D-xylopyranoside). SWIR-HSI in conjunction with chemometric tools (SVM-DA) could identify 80% adulteration of commercial products labelled as black cohosh. Georg Thieme Verlag KG Stuttgart · New York.

Chemometrics comparison of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry Daphnia magna metabolic profiles exposed to salinity.

PubMed

Parastar, Hadi; Garreta-Lara, Elba; Campos, Bruno; Barata, Carlos; Lacorte, Silvia; Tauler, Roma

2018-06-01

The performances of gas chromatography with mass spectrometry and of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry are examined through the comparison of Daphnia magna metabolic profiles. Gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with mass spectrometry were used to compare the concentration changes of metabolites under saline conditions. In this regard, a chemometric strategy based on wavelet compression and multivariate curve resolution-alternating least squares is used to compare the performances of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry for the untargeted metabolic profiling of Daphnia magna in control and salinity-exposed samples. Examination of the results confirmed the outperformance of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry over gas chromatography with mass spectrometry for the detection of metabolites in D. magna samples. The peak areas of multivariate curve resolution-alternating least squares resolved elution profiles in every sample analyzed by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry were arranged in a new data matrix that was then modeled by partial least squares discriminant analysis. The control and salt-exposed daphnids samples were discriminated and the most relevant metabolites were estimated using variable importance in projection and selectivity ratio values. Salinity de-regulated 18 metabolites from metabolic pathways involved in protein translation, transmembrane cell transport, carbon metabolism, secondary metabolism, glycolysis, and osmoregulation. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Discriminative least squares regression for multiclass classification and feature selection.

PubMed

Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

2012-11-01

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

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).

Near-infrared spectroscopy for the detection and quantification of bacterial contaminations in pharmaceutical products.

PubMed

Quintelas, Cristina; Mesquita, Daniela P; Lopes, João A; Ferreira, Eugénio C; Sousa, Clara

2015-08-15

Accurate detection and quantification of microbiological contaminations remains an issue mainly due the lack of rapid and precise analytical techniques. Standard methods are expensive and time-consuming being associated to high economic losses and public health threats. In the context of pharmaceutical industry, the development of fast analytical techniques able to overcome these limitations is crucial and spectroscopic techniques might constitute a reliable alternative. In this work we proved the ability of Fourier transform near infrared spectroscopy (FT-NIRS) to detect and quantify bacteria (Bacillus subtilis, Escherichia coli, Pseudomonas fluorescens, Salmonella enterica, Staphylococcus epidermidis) from 10 to 10(8) CFUs/mL in sterile saline solutions (NaCl 0.9%). Partial least squares discriminant analysis (PLSDA) models showed that FT-NIRS was able to discriminate between sterile and contaminated solutions for all bacteria as well as to identify the contaminant bacteria. Partial least squares (PLS) models allowed bacterial quantification with limits of detection ranging from 5.1 to 9 CFU/mL for E. coli and B. subtilis, respectively. This methodology was successfully validated in three pharmaceutical preparations (contact lens solution, cough syrup and topic anti-inflammatory solution) proving that this technique possess a high potential to be routinely used for the detection and quantification of bacterial contaminations. Copyright © 2015 Elsevier B.V. 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.

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.

ADA perceived disability claims: a decision-tree analysis.

PubMed

Draper, William R; Hawley, Carolyn E; McMahon, Brian T; Reid, Christine A; Barbir, Lara A

2014-06-01

The purpose of this study is to examine the possible interactions of predictor variables pertaining to perceived disability claims contained in a large governmental database. Specifically, it is a retrospective analysis of US Equal Employment Opportunity Commission (EEOC) data for the entire population of workplace discrimination claims based on the "regarded as disabled" prong of the Americans with Disabilities Act (ADA) definition of disability. The study utilized records extracted from a "master database" of over two million charges of workplace discrimination in the Integrated Mission System of the EEOC. This database includes all ADA-related discrimination allegations filed from July 26, 1992 through December 31, 2008. Chi squared automatic interaction detection (CHAID) was employed to analyze interaction effects of relevant variables, such as issue (grievance) and industry type. The research question addressed by CHAID is: What combination of factors are associated with merit outcomes for people making ADA EEOC allegations who are "regarded as" having disabilities? The CHAID analysis shows how merit outcome is predicted by the interaction of relevant variables. Issue was found to be the most prominent variable in determining merit outcome, followed by industry type, but the picture is made more complex by qualifications regarding age and race data. Although discharge was the most frequent grievance among charging parties in the perceived disability group, its merit outcome was significantly less than that for the leading factor of hiring.

Discrimination, Racial Bias, and Telomere Length in African-American Men

PubMed Central

Chae, David H.; Nuru-Jeter, Amani M.; Adler, Nancy E.; Brody, Gene H.; Lin, Jue; Blackburn, Elizabeth H.; Epel, Elissa S.

2013-01-01

Background Leukocyte telomere length (LTL) is an indicator of general systemic aging, with shorter LTL being associated with several chronic diseases of aging and earlier mortality. Identifying factors related to LTL among African Americans may yield insights into mechanisms underlying racial disparities in health. Purpose To test whether the combination of more frequent reports of racial discrimination and holding a greater implicit anti-black racial bias is associated with shorter LTL among African-American men. Methods Cross-sectional study of a community sample of 92 African-American men aged between 30 and 50 years. Participants were recruited from February to May 2010. Ordinary least squares regressions were used to examine LTL in kilobase pairs in relation to racial discrimination and implicit racial bias. Data analysis was completed in July 2013. Results After controlling for chronologic age, socioeconomic, and health-related characteristics, the interaction between racial discrimination and implicit racial bias was significantly associated with LTL (b= −0.10, SE=0.04, p=0.02). Those demonstrating a stronger implicit anti-black bias and reporting higher levels of racial discrimination had the shortest LTL. Household income-to-poverty threshold ratio was also associated with LTL (b=0.05, SE=0.02, p<0.01). Conclusions Results suggest that multiple levels of racism, including interpersonal experiences of racial discrimination and the internalization of negative racial bias, operate jointly to accelerate biological aging among African-American men. Societal efforts to address racial discrimination in concert with efforts to promote positive in-group racial attitudes may protect against premature biological aging in this population. PMID:24439343

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.

Differences and discriminatory power of water polo game-related statistics in men in international championships and their relationship with the phase of the competition.

PubMed

Escalante, Yolanda; Saavedra, Jose M; Tella, Victor; Mansilla, Mirella; García-Hermoso, Antonio; Domínguez, Ana M

2013-04-01

The aims of this study were (a) to compare water polo game-related statistics by context (winning and losing teams) and phase (preliminary, classification, and semifinal/bronze medal/gold medal), and (b) identify characteristics that discriminate performances for each phase. The game-related statistics of the 230 men's matches played in World Championships (2007, 2009, and 2011) and European Championships (2008 and 2010) were analyzed. Differences between contexts (winning or losing teams) in each phase (preliminary, classification, and semifinal/bronze medal/gold medal) were determined using the chi-squared statistic, also calculating the effect sizes of the differences. A discriminant analysis was then performed after the sample-splitting method according to context (winning and losing teams) in each of the 3 phases. It was found that the game-related statistics differentiate the winning from the losing teams in each phase of an international championship. The differentiating variables are both offensive and defensive, including action shots, sprints, goalkeeper-blocked shots, and goalkeeper-blocked action shots. However, the number of discriminatory variables decreases as the phase becomes more demanding and the teams become more equally matched. The discriminant analysis showed the game-related statistics to discriminate performance in all phases (preliminary, classificatory, and semifinal/bronze medal/gold medal phase) with high percentages (91, 90, and 73%, respectively). Again, the model selected both defensive and offensive variables.

A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction.

PubMed

Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W S; Li, Bing

2014-07-01

Dealing with high-dimensional data has always been a major problem in research of pattern recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it only uses labeled samples while neglecting unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimension reduction method, called "SL-LDA", by using unlabeled samples to enhance the performance of LDA. The new method first propagates label information from the labeled set to the unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called "soft labels", can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimension reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. We further propose an efficient approach for solving SL-LDA under a least squares framework, and a flexible method of SL-LDA (FSL-LDA) to better cope with datasets sampled from a nonlinear manifold. Extensive simulations are carried out on several datasets, and the results show the effectiveness of the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia

PubMed Central

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100–300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making. PMID:29095932

Metabolomics based predictive biomarker model of ARDS: A systemic measure of clinical hypoxemia.

PubMed

Viswan, Akhila; Singh, Chandan; Rai, Ratan Kumar; Azim, Afzal; Sinha, Neeraj; Baronia, Arvind Kumar

2017-01-01

Despite advancements in ventilator technologies, lung supportive and rescue therapies, the outcome and prognostication in acute respiratory distress syndrome (ARDS) remains incremental and ambiguous. Metabolomics is a potential insightful measure to the diagnostic approaches practiced in critical disease settings. In our study patients diagnosed with mild and moderate/severe ARDS clinically governed by hypoxemic P/F ratio between 100-300 but with indistinct molecular phenotype were discriminated employing nuclear magnetic resonance (NMR) based metabolomics of mini bronchoalveolar lavage fluid (mBALF). Resulting biomarker prototype comprising six metabolites was substantiated highlighting ARDS susceptibility/recovery. Both the groups (mild and moderate/severe ARDS) showed distinct biochemical profile based on 83.3% classification by discriminant function analysis and cross validated accuracy of 91% using partial least squares discriminant analysis as major classifier. The predictive performance of narrowed down six metabolites were found analogous with chemometrics. The proposed biomarker model consisting of six metabolites proline, lysine/arginine, taurine, threonine and glutamate were found characteristic of ARDS sub-stages with aberrant metabolism observed mainly in arginine, proline metabolism, lysine synthesis and so forth correlating to diseased metabotype. Thus NMR based metabolomics has provided new insight into ARDS sub-stages and conclusively a precise biomarker model proposed, reflecting underlying metabolic dysfunction aiding prior clinical decision making.

Advanced signal processing analysis of laser-induced breakdown spectroscopy data for the discrimination of obsidian sources.

PubMed

Remus, Jeremiah J; Harmon, Russell S; Hark, Richard R; Haverstock, Gregory; Baron, Dirk; Potter, Ian K; Bristol, Samantha K; East, Lucille J

2012-03-01

Obsidian is a natural glass of volcanic origin and a primary resource used by indigenous peoples across North America for making tools. Geochemical studies of obsidian enhance understanding of artifact production and procurement and remain a priority activity within the archaeological community. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique being examined as a means for identifying obsidian from different sources on the basis of its 'geochemical fingerprint'. This study tested whether two major California obsidian centers could be distinguished from other obsidian localities and the extent to which subsources could be recognized within each of these centers. LIBS data sets were collected in two different spectral bands (350±130 nm and 690±115 nm) using a Nd:YAG 1064 nm laser operated at ~23 mJ, a Czerny-Turner spectrograph with 0.2-0.3 nm spectral resolution and a high performance imaging charge couple device (ICCD) detector. Classification of the samples was performed using partial least-squares discriminant analysis (PLSDA), a common chemometric technique for performing statistical regression on high-dimensional data. Discrimination of samples from the Coso Volcanic Field, Bodie Hills, and other major obsidian areas in north-central California was possible with an accuracy of greater than 90% using either spectral band. © 2012 Optical Society of America

Monitoring of beer fermentation based on hybrid electronic tongue.

PubMed

Kutyła-Olesiuk, Anna; Zaborowski, Michał; Prokaryn, Piotr; Ciosek, Patrycja

2012-10-01

Monitoring of biotechnological processes, including fermentation is extremely important because of the rapidly occurring changes in the composition of the samples during the production. In the case of beer, the analysis of physicochemical parameters allows for the determination of the stage of fermentation process and the control of its possible perturbations. As a tool to control the beer production process a sensor array can be used, composed of potentiometric and voltammetric sensors (so-called hybrid Electronic Tongue, h-ET). The aim of this study is to apply electronic tongue system to distinguish samples obtained during alcoholic fermentation. The samples originate from batch of homemade beer fermentation and from two stages of the process: fermentation reaction and maturation of beer. The applied sensor array consists of 10 miniaturized ion-selective electrodes (potentiometric ET) and silicon based 3-electrode voltammetric transducers (voltammetric ET). The obtained results were processed using Partial Least Squares (PLS) and Partial Least Squares-Discriminant Analysis (PLS-DA). For potentiometric data, voltammetric data, and combined potentiometric and voltammetric data, comparison of the classification ability was conducted based on Root Mean Squared Error (RMSE), sensitivity, specificity, and coefficient F calculation. It is shown, that in the contrast to the separately used techniques, the developed hybrid system allowed for a better characterization of the beer samples. Data fusion in hybrid ET enables to obtain better results both in qualitative analysis (RMSE, specificity, sensitivity) and in quantitative analysis (RMSE, R(2), a, b). Copyright © 2012 Elsevier B.V. All rights reserved.

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.

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.

Psychometric characteristics of the Functional Assessment of Cancer Therapy-General when applied to Brazilian cancer patients: a cross-cultural adaptation and validation.

PubMed

Campos, Juliana Alvares Duarte Bonini; Spexoto, Maria Cláudia Bernardes; Serrano, Sergio Vicente; Maroco, João

2016-01-13

The psychometric properties of an instrument should be evaluated routinely when using different samples. This study evaluated the psychometric properties of the Functional Assessment of Cancer Therapy-General (FACT-G) when applied to a sample of Brazilian cancer patients. The face, content, and construct (factorial, convergent, and discriminant) validities of the FACT-G were estimated. Confirmatory factor analysis (CFA) was conducted the ratio chi-square by degrees of freedom (χ (2)/df), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA) as indices. The invariance of the best model was assessed with multi-group analysis using the difference of chi-squares method (Δχ(2)). Convergent validity was assessed using Average Variance Extracted (AVE) and discriminant validity was determined via correlational analysis. Internal consistency was assessed using the Cronbach's alpha (α) coefficient, and the Composite Reliability (CR) was estimated. A total of 975 cancer patients participated in the study, with a mean age of 53.3 (SD = 13.0) years. Of these participants, 61.5 % were women. In CFA, five correlations between errors were included to fit the FACT-G to the sample (χ (2)/df = 8.611, CFI = .913, TLI = .902, RMSEA = .088). The model did not indicate invariant independent samples (Δχ(2): μ: p < .001, i: p < .958, Cov: p < .001, Res: p < .001). While there was adequate convergent validity for the physical well-being (AVE = .54) and social and family Well-being factors (AVE = .55), there was low convergent validity for the other factors. Reliability was adequate (CR = .76-.89 and α = .71-.82). Functional well-being, emotional well-being, and physical well-being were the factors that demonstrated a strong contribution to patients' health-related quality of life (β = -.99, .88, and .64, respectively). The FACT-G was found to be a valid and reliable assessment of health-related quality of life in a Brazilian sample of patients with cancer.

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.

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.

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

PubMed

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

Metabolite profiling and bioactivity of rice koji fermented by Aspergillus strains.

PubMed

Kim, Ah-Jin; Choi, Jung-Nam; Kim, Jiyoung; Kim, Hyang Yeon; Park, Sait-Byul; Yeo, Soo-Hwan; Choi, Ji-Ho; Liu, Kwang-Hyeon; Lee, Choong Hwan

2012-01-01

In this study, the metabolite profiles of three Aspergillus strains during rice koji fermentation were compared. In the partial least squares discriminant analysis-based gas chromatography-mass spectrometry data sets, the metabolite patterns of A. oryzae (KCCM 60345) were clearly distinguished from A. kawachii (KCCM 60552) and only marginal differences were observed for A. oryzae (KCCM 60551) fermentation. In the 2 days fermentation samples, the overall metabolite levels of A. oryzae (KCCM 60345) were similar to the A. oryzae (KCCM 60551) levels and lower than the A. kawachii (KCCM 60552) levels. In addition, we identified discriminators that were mainly contributing tyrosinase inhibition (kojic acid) and antioxidant activities (pyranonigrin A) in A. oryzae (KCCM 60345) and A. kawachii (KCCM 60552) inoculated rice koji, respectively. In this study, we demonstrated that the optimal inoculant Aspergillus strains and fermentation time for functional rice koji could be determined through a metabolomics approach with bioactivity correlations.

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.

Rapid detection of peptide markers for authentication purposes in raw and cooked meat using ambient liquid extraction surface analysis mass spectrometry.

PubMed

Montowska, Magdalena; Alexander, Morgan R; Tucker, Gregory A; Barrett, David A

2014-10-21

In this Article, our previously developed ambient LESA-MS methodology is implemented to analyze five types of thermally treated meat species, namely, beef, pork, horse, chicken, and turkey meat, to select and identify heat-stable and species-specific peptide markers. In-solution tryptic digests of cooked meats were deposited onto a polymer surface, followed by LESA-MS analysis and evaluation using multivariate data analysis and tandem electrospray MS. The five types of cooked meat were clearly discriminated using principal component analysis and orthogonal partial least-squares discriminant analysis. 23 heat stable peptide markers unique to species and muscle protein were identified following data-dependent tandem LESA-MS analysis. Surface extraction and direct ambient MS analysis of mixtures of cooked meat species was performed for the first time and enabled detection of 10% (w/w) of pork, horse, and turkey meat and 5% (w/w) of chicken meat in beef, using the developed LESA-MS/MS analysis. The study shows, for the first time, that ambient LESA-MS methodology displays specificity sufficient to be implemented effectively for the analysis of processed and complex peptide digests. The proposed approach is much faster and simpler than other measurement tools for meat speciation; it has potential for application in other areas of meat science or food production.

Rapid determination of chemical composition and classification of bamboo fractions using visible-near infrared spectroscopy coupled with multivariate data analysis.

PubMed

Yang, Zhong; Li, Kang; Zhang, Maomao; Xin, Donglin; Zhang, Junhua

2016-01-01

During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible-near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible-near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of principal component analysis of mature bamboo, juvenile bamboo, and different fractions of mature bamboo were obviously distinguished in raw spectra. Based on partial least squares discriminant analysis, the classification accuracies of mature bamboo, juvenile bamboo, and different fractions of bamboo (bamboo green, bamboo timber, bamboo yellow, and bamboo branch) all reached 100 %. In addition, high accuracies of evaluation of the enzymatic digestibilities of bamboo fractions after pretreatment with aqueous ammonia were also observed. The results showed the potential of visible-near infrared spectroscopy in combination with multivariate analysis in efficiently analyzing the chemical composition and hydrolysabilities of lignocellulosic biomass, such as bamboo fractions.

[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.

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.

Improved neutron-gamma discrimination for a 6Li-glass neutron detector using digital signal analysis methods

DOE PAGES

Wang, Cai -Lin; Riedel, Richard A.

2016-01-14

A 6Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at SNS. Traditional pulse-height analysis (PHA) for neutron-gamma discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10 4. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, five digital signal analysis methods of individual waveforms from PMTs were proposed using: i). pulse-amplitude histogram; ii). power spectrum analysis combined with the maximum pulse amplitude; iii). two event parameters (a 1, b 0) obtained from Wiener filter; iv). anmore » effective amplitude (m) obtained from an adaptive least-mean-square (LMS) filter; and v). a cross-correlation (CC) coefficient between an individual waveform and a reference. The NGD ratios can be 1-102 times those from traditional PHA method. A brighter scintillator GS2 has better NGD ratio than GS20, but lower neutron detection efficiency. The ultimate NGD ratio is related to the ambient, high-energy background events. Moreover, our results indicate the NGD capability of neutron Anger cameras can be improved using digital signal analysis methods and brighter neutron scintillators.« less

Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

PubMed

Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

2018-02-01

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

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

Detection of indoor biological hazards using the man-portable laser induced breakdown spectrometer

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

Munson, Chase A.; Gottfried, Jennifer L.; Snyder, Emily Gibb

2008-11-01

The performance of a man-portable laser induced breakdown spectrometer was evaluated for the detection of biological powders on indoor office surfaces and wipe materials. Identification of pure unknown powders was performed by comparing against a library of spectra containing biological agent surrogates and confusant materials, such as dusts, diesel soot, natural and artificial sweeteners, and drink powders, using linear correlation analysis. Simple models constructed using a second technique, partial least squares discriminant analysis, successfully identified Bacillus subtilis (BG) spores on wipe materials and office surfaces. Furthermore, these models were able to identify BG on materials not used in the trainingmore » of the model.« less

A cross-sectional study of associations between casual partner, friend discrimination, social support and anxiety symptoms among Chinese transgender women.

PubMed

Yang, Xiaoshi; Wang, Lie; Gu, Yuan; Song, Wei; Hao, Chun; Zhou, Jinling; Zhang, Qun; Zhao, Qun

2016-10-01

Anxiety symptoms are the prevalent mental disorders for transgender women. However, only a few studies are available pertaining to this problem among Chinese Transgender women. Chinese Transgender women are a vulnerable population which is exposed to discrimination and loss of social support due to their gender identity and transition. This study was conducted to estimate the prevalence and factors associated with anxiety symptoms among Chinese transgender women. A cross-sectional study was performed by convenience sampling. This comprised of 209 Chinese transgender women in Shenyang, China. The Zung Self-Rating Anxiety Scale (SAS) was used to assess anxiety symptoms for these transgender women. Hierarchical multiple regression analysis was performed to explore the associated factors of SAS. The prevalence of anxiety symptoms in Chinese transgender women was found to be 34.5%. Regression analyses indicated that SAS was associated with casual partnership, friend discrimination and social support in the final model. Sexual partnership and discrimination contributed the most to the model, R-square, accounting for 19.2% and 15.5% of the total variance respectively. Chinese transgender women showed considerably high level of anxiety symptoms. It was also found that they were exposed to significant transition challenges, such as high risk sexual partnership, excessive discrimination and a reduction in social support. Furthermore, anxiety symptoms was best predicted by the absence or presence of a casual partner, friend discrimination and social support rather than the disclosure of their gender identity, knowledge of HIV prevention and health service. Improvement of social support, reduction of friend discrimination and determination of the characteristics of risky sexual partnerships especially for the casual partner can help to attenuate anxiety symptoms and increase mental well-being for transgender women. Copyright © 2016 Elsevier B.V. All rights reserved.

Discrimination, racial bias, and telomere length in African-American men.

PubMed

Chae, David H; Nuru-Jeter, Amani M; Adler, Nancy E; Brody, Gene H; Lin, Jue; Blackburn, Elizabeth H; Epel, Elissa S

2014-02-01

Leukocyte telomere length (LTL) is an indicator of general systemic aging, with shorter LTL being associated with several chronic diseases of aging and earlier mortality. Identifying factors related to LTL among African Americans may yield insights into mechanisms underlying racial disparities in health. To test whether the combination of more frequent reports of racial discrimination and holding a greater implicit anti-black racial bias is associated with shorter LTL among African-American men. Cross-sectional study of a community sample of 92 African-American men aged between 30 and 50 years. Participants were recruited from February to May 2010. Ordinary least squares regressions were used to examine LTL in kilobase pairs in relation to racial discrimination and implicit racial bias. Data analysis was completed in July 2013. After controlling for chronologic age and socioeconomic and health-related characteristics, the interaction between racial discrimination and implicit racial bias was significantly associated with LTL (b=-0.10, SE=0.04, p=0.02). Those demonstrating a stronger implicit anti-black bias and reporting higher levels of racial discrimination had the shortest LTL. Household income-to-poverty threshold ratio was also associated with LTL (b=0.05, SE=0.02, p<0.01). Results suggest that multiple levels of racism, including interpersonal experiences of racial discrimination and the internalization of negative racial bias, operate jointly to accelerate biological aging among African-American men. Societal efforts to address racial discrimination in concert with efforts to promote positive in-group racial attitudes may protect against premature biological aging in this population. © 2013 American Journal of Preventive Medicine Published by American Journal of Preventive Medicine All rights reserved.

Analysis of esterified and nonesterified fatty acids in serum from obese individuals after intake of breakfasts prepared with oils heated at frying temperature.

PubMed

Orozco-Solano, M I; Priego-Capote, F; Luque de Castro, M D

2013-07-01

In this study, levels of esterified and nonesterified fatty acids (EFAs and NEFAs, respectively) were compared in obese individuals (body mass index between 30 and 47 kg m(-2)) in basal state and after intake of four different breakfasts prepared with oils heated at frying temperature. The target oils were three sunflower oils--pure, enriched with dimethylsiloxane (400 μg mL(-1)) as lipophilic oxidation inhibitor, and enriched with phenolic compounds (400 μg mL(-1)) as hydrophilic oxidation inhibitors--and virgin olive oil with a natural content of phenolic compounds of 400 μg mL(-1). The intake of breakfasts was randomized to avoid trends associated to this variability source. EFAs and NEFAs were subjected to a sequential derivatization step for independent gas chromatography-mass spectrometry analysis of both fractions of metabolites in human serum. Derivatization was assisted by ultrasonic energy to accelerate the reaction kinetics, as required for high-throughput analysis. Statistical analysis supported on univariate (multifactor ANOVA) and multivariate approaches (principal component analysis and partial least squares-discriminant analysis) allowed identification of the main variability sources and also discriminating between individuals after intake of each breakfast. Individuals' samples after intake of breakfasts prepared with virgin olive oil were clearly separated from those who ingested the remaining breakfasts. The main compounds contributing to discrimination were omega-3 and omega-6 EFAs with special emphasis on arachidonic acid and eicosapentaenoic acid. These two polyunsaturated fatty acids are the precursors of eicosanoid metabolites, which are of vital importance as they play important roles in inflammation and in the pathogenesis of vascular and malignant diseases as cancer.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method].

PubMed

Wang, L; Qin, X C; Lin, H C; Deng, K F; Luo, Y W; Sun, Q R; Du, Q X; Wang, Z Y; Tuo, Y; Sun, J H

2018-02-01

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method. Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method. The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （ R ²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R ² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h. The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

PubMed

Schmitt, Laurent; Regnard, Jacques; Millet, Grégoire P

2015-01-01

Among the tools proposed to assess the athlete's "fatigue," the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global "fatigue" level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of "fatigue." Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes.

'Where' depends on 'what': a differential functional anatomy for position discrimination in one- versus two-dimensions.

PubMed

Fink, G R; Marshall, J C; Weiss, P H; Shah, N J; Toni, I; Halligan, P W; Zilles, K

2000-01-01

Line bisection is widely used as a clinical test of spatial cognition in patients with left visuospatial neglect after right hemisphere lesion. Surprisingly, many neglect patients who show severe impairment on marking the center of horizontal lines can accurately mark the center of squares. That these patients with left neglect are also typically poor at judging whether lines are correctly prebisected implies that the deficit can be perceptual rather than motoric. These findings suggest a differential neural basis for one- and two-dimensional visual position discrimination that we investigated with functional neuroimaging (fMRI). Normal subjects judged whether, in premarked lines or squares, the mark was placed centrally. Line center judgements differentially activated right parietal cortex, while square center judgements differentially activated the lingual gyrus bilaterally. These distinct neural bases for one- and two-dimensional visuospatial judgements help explain the observed clinical dissociations by showing that as a stimulus becomes a better, more 'object-like' gestalt, the ventral visuoperceptive route assumes more responsibility for assessing position within the object.

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

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.

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

Spectroscopic signature of mouse embryonic stem cell-derived hepatocytes using synchrotron Fourier transform infrared microspectroscopy

NASA Astrophysics Data System (ADS)

Thumanu, Kanjana; Tanthanuch, Waraporn; Ye, Danna; Sangmalee, Anawat; Lorthongpanich, Chanchao; Parnpai, Rangsun; Heraud, Philip

2011-05-01

Stem cell-based therapy for liver regeneration has been proposed to overcome the persistent shortage in the supply of suitable donor organs. A requirement for this to succeed is to find a rapid method to detect functional hepatocytes, differentiated from embryonic stem cells. We propose Fourier transform infrared (FTIR) microspectroscopy as a versatile method to identify the early and last stages of the differentiation process leading to the formation of hepatocytes. Using synchrotron-FTIR microspectroscopy, the means of identifying hepatocytes at the single-cell level is possible and explored. Principal component analysis and subsequent partial least-squares (PLS) discriminant analysis is applied to distinguish endoderm induction from hepatic progenitor cells and matured hepatocyte-like cells. The data are well modeled by PLS with endoderm induction, hepatic progenitor cells, and mature hepatocyte-like cells able to be discriminated with very high sensitivity and specificity. This method provides a practical tool to monitor endoderm induction and has the potential to be applied for quality control of cell differentiation leading to hepatocyte formation.

Discovery of human urinary biomarkers of aronia-citrus juice intake by HPLC-q-TOF-based metabolomic approach.

PubMed

Llorach, Rafael; Medina, Sonia; García-Viguera, Cristina; Zafrilla, Pilar; Abellán, José; Jauregui, Olga; Tomás-Barberán, Francisco A; Gil-Izquierdo, Angel; Andrés-Lacueva, Cristina

2014-06-01

Metabolomics has emerged in the field of food and nutrition sciences as a powerful tool for doing profiling approaches. In this context, HPLC-q-TOF-based metabolomics approach was applied to unveil changes in the urinary metabolome in human subjects (n = 51, 23 men and 28 women) after regular aronia-citrus juice (AC-juice) intake (250 mL/day) during 16 weeks compared to individuals given a placebo beverage. Samples were analyzed by HPLC-q-TOF followed by multivariate data analysis (orthogonal signal filtering-partial least square discriminant analysis) that discriminated relevant mass features related to AC-juice intake. The results showed that biomarkers of AC-juice intake including metabolites coming from metabolism of food components as proline betaine, ferulic acid, and two unknown mercapturate derivatives were identified. Discovery of new biomarkers of food intake will help in the building up of the food metabolome and facilitate future insights into the mechanisms of action of dietary components in population health. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

LED-based near infrared sensor for cancer diagnostics

NASA Astrophysics Data System (ADS)

Bogomolov, Andrey; Ageev, Vladimir; Zabarylo, Urszula; Usenov, Iskander; Schulte, Franziska; Kirsanov, Dmitry; Belikova, Valeria; Minet, Olaf; Feliksberger, E.; Meshkovsky, I.; Artyushenko, Viacheslav

2016-03-01

Optical spectroscopic technologies are increasingly used for cancer diagnostics. Feasibility of differentiation between malignant and healthy samples of human kidney using Fluorescence, Raman, MIR and NIR spectroscopy has been recently reported . In the present work, a simplification of NIR spectroscopy method has been studied. Traditional high-resolution NIR spectrometry was replaced by an optical sensor based on a set of light-emitting diodes at selected wavelengths as light sources and a photodiode. Two prototypes of the sensor have been developed and tested using 14 in-vitro samples of seven kidney tumor patients. Statistical evaluation of results using principal component analysis and partial least-squares discriminant analysis has been performed. Despite only partial discrimination between tumor and healthy tissue achieved by the presented new technique, the results evidence benefits of LED-based near-infrared sensing used for oncological diagnostics. Publisher's Note: This paper, originally published on 4 March, 2016, was replaced with a corrected/revised version on 7 April, 2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance.

Effects of grown origin, genotype, harvest year, and their interactions of wheat kernels on near infrared spectral fingerprints for geographical traceability.

PubMed

Zhao, Haiyan; Guo, Boli; Wei, Yimin; Zhang, Bo

2014-01-01

The effects of origin, genotype, harvest year, and their interactions on wheat near infrared (NIR) spectra were studied to find the reasons for differences in NIR fingerprints of wheat from different geographical origins and the stability of NIR fingerprints among different years. Ten varieties were grown in three regions of China for 2 years. 180 kernel samples were analysed by NIR. The spectra after pre-treatment were analysed by principal component analysis, multi-way analysis of variance, and discriminant partial least-squares. The results showed that origin, genotype, year, and their interactions all had significant effects on wheat NIR fingerprints. The second overtones of N-H and C-H stretching vibrations and a combination of stretch and deformation of C-H group in wheat were mainly influenced by the geographical origin. The wavelength ranges 975-990 nm, 1200 nm, and 1355-1380 nm contained plenty of origin information to build robust discriminant models of wheat geographical origin. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rapid differentiation among bacteria that cause gastroenteritis by use of low-resolution Raman spectroscopy and PLS discriminant analysis.

PubMed

Mello, Cesar; Ribeiro, Diórginis; Novaes, Fábio; Poppi, Ronei J

2005-10-01

Use of classical microbiological methods to differentiate bacteria that cause gastroenteritis is cumbersome but usually very efficient. The high cost of reagents and the time required for such identifications, approximately four days, could have serious consequences, however, mainly when the patients are children, the elderly, or adults with low resistance. The search for new methods enabling rapid and reagentless differentiation of these microorganisms is, therefore, extremely relevant. In this work the main microorganisms responsible for gastroenteritis, Escherichia coli, Salmonella choleraesuis, and Shigella flexneri, were studied. For each microorganism sixty different dispersions were prepared in physiological solution. The Raman spectra of these dispersions were recorded using a diode laser operating in the near infrared region. Partial least-squares (PLS) discriminant analysis was used to differentiate among the bacteria by use of their respective Raman spectra. This approach enabled correct classification of 100% of the bacteria evaluated and unknown samples from the clinical environment, in less time ( approximately 10 h), by use of a low-cost, portable Raman spectrometer, which can be easily used in intensive care units and clinical environments.

Multi-element, multi-compound isotope profiling as a means to distinguish the geographical and varietal origin of fermented cocoa (Theobroma cacao L.) beans.

PubMed

Diomande, Didier; Antheaume, Ingrid; Leroux, Maël; Lalande, Julie; Balayssac, Stéphane; Remaud, Gérald S; Tea, Illa

2015-12-01

Multi-element stable isotope ratios have been assessed as a means to distinguish between fermented cocoa beans from different geographical and varietal origins. Isotope ratios and percentage composition for C and N were measured in different tissues (cotyledons, shells) and extracts (pure theobromine, defatted cocoa solids, protein, lipids) obtained from fermented cocoa bean samples. Sixty-one samples from 24 different geographical origins covering all four continental areas producing cocoa were analyzed. Treatment of the data with unsupervised (Principal Component Analysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric statistical methods allowed the cocoa beans from different origins to be distinguished. The most discriminant variables identified as responsible for geographical and varietal differences were the δ(15)N and δ(13)C values of cocoa beans and some extracts and tissues. It can be shown that the isotope ratios are correlated with the altitude and precipitation conditions found in the different cocoa-growing regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Pain in fibromyalgia and discrimination power of the instruments: Visual Analog Scale, Dolorimetry and the McGill Pain Questionnaire.

PubMed

Marques, Amélia Pasqual; Assumpção, Ana; Matsutani, Luciana A; Pereira, Carlos A Bragança; Lage, Lais

2008-01-01

The aim of this study was to verify the discriminative power of the most widely used pain assessment instruments. The sample consisted of 279 subjects divided into Fibromyalgia Group FM- 205 patients with fibromyalgia and Control Group CG-74 healthy subjects), mean age 49.29 +/- 10.76 years. Only 9 subjects were male, 6 in FM and 3 in CG. FM were outpatients from the Rheumatology Clinic of the University of São Paulo--Hospital das Clínicas (HCFMUSP); the CG included people accompanying patients and hospital staff with similar socio-demographic characteristics. Three instruments were used to assess pain: the McGill Pain Questionnaire MPQ, the Visual Analog Scale (VAS), and the Dolorimetry, to measure pain threshold on tender points (generating the TP index). In order to assess the discriminative power of the instruments the measurements obtained were submitted to descriptive analysis and inferential analysis using ROC Curve-sensibility (S), specificity (S1) and area under the curve (AUC)--and Contingence tables with Chi-square Test and odds ratio. Significance level was 0.05. Higher sensibility specificity and area under the curve was obtained by VAS (80% 80% and 0.864, respectively), followed by Dolorimetry (S 77% S177% and AUC 0.851), McGill Sensory (S 72% S167% and AUC 0.765) and McGill Affective (S 69% S1 67% and AUC 0.753). VAS presented the higher sensibility, specificity and AUC, showing the greatest discriminative power among the instruments. However, these values are considerably similar to those of Dolorimetry.

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

PubMed

Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

2016-04-19

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Genetics Home Reference: Klinefelter syndrome

MedlinePlus

... AS, Gillam L. Thinking outside the square: considering gender in Klinefelter syndrome and 47, XXY. Int J ... Reference Celebrates Its 15th Anniversary Genetic Information Non-Discrimination Act (GINA) Turns 10 All Bulletins Features What ...

Metabolomic Profiling of Post-Mortem Brain Reveals Changes in Amino Acid and Glucose Metabolism in Mental Illness Compared with Controls.

PubMed

Zhang, Rong; Zhang, Tong; Ali, Ali Muhsen; Al Washih, Mohammed; Pickard, Benjamin; Watson, David G

2016-01-01

Metabolomic profiling was carried out on 53 post-mortem brain samples from subjects diagnosed with schizophrenia, depression, bipolar disorder (SDB), diabetes, and controls. Chromatography on a ZICpHILIC column was used with detection by Orbitrap mass spectrometry. Data extraction was carried out with m/z Mine 2.14 with metabolite searching against an in-house database. There was no clear discrimination between the controls and the SDB samples on the basis of a principal components analysis (PCA) model of 755 identified or putatively identified metabolites. Orthogonal partial least square discriminant analysis (OPLSDA) produced clear separation between 17 of the controls and 19 of the SDB samples (R2CUM 0.976, Q2 0.671, p-value of the cross-validated ANOVA score 0.0024). The most important metabolites producing discrimination were the lipophilic amino acids leucine/isoleucine, proline, methionine, phenylalanine, and tyrosine; the neurotransmitters GABA and NAAG and sugar metabolites sorbitol, gluconic acid, xylitol, ribitol, arabinotol, and erythritol. Eight samples from diabetic brains were analysed, six of which grouped with the SDB samples without compromising the model (R2 CUM 0.850, Q2 CUM 0.534, p-value for cross-validated ANOVA score 0.00087). There appears on the basis of this small sample set to be some commonality between metabolic perturbations resulting from diabetes and from SDB.

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.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

NASA Astrophysics Data System (ADS)

Daniel, Amuthachelvi; Prakasarao, Aruna; Ganesan, Singaravelu

2018-02-01

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

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.

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.

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

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.

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.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

PubMed Central

Garcia-Hernandez, Celia; Medina-Plaza, Cristina; Garcia-Cabezon, Cristina; Martin-Pedrosa, Fernando; del Valle, Isabel; de Saja, Jose Antonio; Rodríguez-Méndez, Maria Luz

2015-01-01

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately. PMID:26610494

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.

1H NMR Spectroscopy and MVA Analysis of Diplodus sargus Eating the Exotic Pest Caulerpa cylindracea.

PubMed

De Pascali, Sandra A; Del Coco, Laura; Felline, Serena; Mollo, Ernesto; Terlizzi, Antonio; Fanizzi, Francesco P

2015-06-05

The green alga Caulerpa cylindracea is a non-autochthonous and invasive species that is severely affecting the native communities in the Mediterranean Sea. Recent researches show that the native edible fish Diplodus sargus actively feeds on this alga and cellular and physiological alterations have been related to the novel alimentary habits. The complex effects of such a trophic exposure to the invasive pest are still poorly understood. Here we report on the metabolic profiles of plasma from D. sargus individuals exposed to C. cylindracea along the southern Italian coast, using 1H NMR spectroscopy and multivariate analysis (Principal Component Analysis, PCA, Orthogonal Partial Least Square, PLS, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA). Fish were sampled in two seasonal periods from three different locations, each characterized by a different degree of algal abundance. The levels of the algal bisindole alkaloid caulerpin, which is accumulated in the fish tissues, was used as an indicator of the trophic exposure to the seaweed and related to the plasma metabolic profiles. The profiles appeared clearly influenced by the sampling period beside the content of caulerpin, while the analyses also supported a moderate alteration of lipid and choline metabolism related to the Caulerpa-based diet.

Characteristic of entire corneal topography and tomography for the detection of sub-clinical keratoconus with Zernike polynomials using Pentacam.

PubMed

Xu, Zhe; Li, Weibo; Jiang, Jun; Zhuang, Xiran; Chen, Wei; Peng, Mei; Wang, Jianhua; Lu, Fan; Shen, Meixiao; Wang, Yuanyuan

2017-11-28

The study aimed to characterize the entire corneal topography and tomography for the detection of sub-clinical keratoconus (KC) with a Zernike application method. Normal subjects (n = 147; 147 eyes), sub-clinical KC patients (n = 77; 77 eyes), and KC patients (n = 139; 139 eyes) were imaged with the Pentacam HR system. The entire corneal data of pachymetry and elevation of both the anterior and posterior surfaces were exported from the Pentacam HR software. Zernike polynomials fitting was used to quantify the 3D distribution of the corneal thickness and surface elevation. The root mean square (RMS) values for each order and the total high-order irregularity were calculated. Multimeric discriminant functions combined with individual indices were built using linear step discriminant analysis. Receiver operating characteristic curves determined the diagnostic accuracy (area under the curve, AUC). The 3rd-order RMS of the posterior surface (AUC: 0.928) obtained the highest discriminating capability in sub-clinical KC eyes. The multimeric function, which consisted of the Zernike fitting indices of corneal posterior elevation, showed the highest discriminant ability (AUC: 0.951). Indices generated from the elevation of posterior surface and thickness measurements over the entire cornea using the Zernike method based on the Pentacam HR system were able to identify very early KC.

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Discriminating tests of information and topological indices. Animals and trees.

PubMed

Konstantinova, Elena V; Vidyuk, Maxim V

2003-01-01

In this paper we consider 13 information and topological indices based on the distance in a molecular graph with respect to their discrimination power. The numerical results of discriminating tests on 3490528 trees up to 21 vertices are given. The indices of the highest sensitivity are listed on the set of 1528775 alkane trees. The discrimination powers of indices are also examined on the classes of 849285 hexagonal, 298382 square, and 295365 triangular simply connected animals. The first class of animals corresponds to the structural formulas of planar benzenoid hydrocarbons. The values of all indices were calculated for all classes of animals as well as for the united set of 1443032 animals. The inspection of the data indicates the great sensitivity of four information indices and one topological index.

Association Between Socioeconomic Position Discrimination and Psychological Distress: Findings From a Community-Based Sample of Gay and Bisexual Men in New York City

PubMed Central

Gamarel, Kristi E.; Reisner, Sari L.; Parsons, Jeffrey T.

2012-01-01

Objectives. We examined the association between discrimination and mental health distress, focusing specifically on the relative importance of discrimination because of particular demographic domains (i.e., race/ethnicity, socioeconomic position [SEP]). Methods. The research team surveyed a sample of gay and bisexual men (n = 294) at a community event in New York City. Participants completed a survey on demographics, discrimination experiences in the past 12 months, attributed domains of discrimination, and mental health distress. Results. In adjusted models, discrimination was associated with higher depressive (B = 0.31; P < .01) and anxious (B = 0.29; P < .01) symptoms. A statistically significant quadratic term (discrimination-squared; P < .01) fit both models, such that moderate levels of discrimination were most robustly associated with poorer mental health. Discrimination because of SEP was associated with higher discrimination scores and was predictive of higher depressive (B = 0.22; P < .01) and anxious (B = 0.50; P < .01) symptoms. No other statistically significant relationship was found between discrimination domains and distress. Conclusions. In this sample, SEP emerged as the most important domain of discrimination in its association with mental health distress. Future research should consider intersecting domains of discrimination to better understand social disparities in mental health. PMID:22994188

Multivariate Analysis of Fruit Antioxidant Activities of Blackberry Treated with 1-Methylcyclopropene or Vacuum Precooling

PubMed Central

Li, Jian; Ma, Guowei; Ma, Lin; Bao, Xiaolin; Li, Liping; Zhao, Qian

2018-01-01

Effects of 1-methylcyclopropene (1-MCP) and vacuum precooling on quality and antioxidant properties of blackberries (Rubus spp.) were evaluated using one-way analysis of variance, principal component analysis (PCA), partial least squares (PLS), and path analysis. Results showed that the activities of antioxidant enzymes were enhanced by both 1-MCP treatment and vacuum precooling. PCA could discriminate 1-MCP treated fruit and the vacuum precooled fruit and showed that the radical-scavenging activities in vacuum precooled fruit were higher than those in 1-MCP treated fruit. The scores of PCA showed that H2O2 content was the most important variables of blackberry fruit. PLSR results showed that peroxidase (POD) activity negatively correlated with H2O2 content. The results of path coefficient analysis indicated that glutathione (GSH) also had an indirect effect on H2O2 content. PMID:29487622

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.

Characterization of Armillaria spp. from peach orchards in the southeastern United States using fatty acid methyl ester profiling.

PubMed

Cox, K D; Scherm, H; Riley, M B

2006-04-01

Limited information is available regarding the composition of cellular fatty acids in Armillaria and the extent to which fatty acid profiles can be used to characterize species in this genus. Fatty acid methyl ester (FAME) profiles generated from cultures of A. tabescens, A. mellea, and A. gallica consisted of 16-18 fatty acids ranging from 12-24 carbons in length, although some of these were present only in trace amounts. Across the three species, 9-cis,12-cis-octadecadienoic acid (9,12-C18:2), hexadecanoic acid (16:0), heneicosanoic acid (21:0), 9-cis-octadecenoic acid (9-C18:1), and 2-hydroxy-docosanoic acid (OH-22:0) were the most abundant fatty acids. FAME profiles from different thallus morphologies (mycelium, sclerotial crust, or rhizomorphs) displayed by cultures of A. gallica showed that thallus type had no significant effect on cellular fatty acid composition (P > 0.05), suggesting that FAME profiling is sufficiently robust for species differentiation despite potential differences in thallus morphology within and among species. The three Armillaria species included in this study could be distinguished from other lignicolous basidiomycete species commonly occurring on peach (Schizophyllum commune, Ganoderma lucidum, Stereum hirsutum, and Trametes versicolor) on the basis of FAME profiles using stepwise discriminant analysis (average squared canonical correlation = 0.953), whereby 9-C18:1, 9,12-C18:2, and 10-cis-hexadecenoic acid (10-C16:1) were the three strongest contributors. In a separate stepwise discriminant analysis, A. tabescens, A. mellea, and A. gallica were separated from one another based on their fatty acid profiles (average squared canonical correlation = 0.924), with 11-cis-octadecenoic acid (11-C18:1), 9-C18:1, and 2-hydroxy-hexadecanoic acid (OH-16:0) being most important for species separation. When fatty acids were extracted directly from mycelium dissected from naturally infected host tissue, the FAME-based discriminant functions developed in the preceding experiments classified all samples (n = 16) as A. tabescens; when applied to cultures derived from the same naturally infected samples, all unknowns were similarly classified as A. tabescens. Thus, FAME species classification of Armillaria unknowns directly from infected tissues may be feasible. Species designation of unknown Armillaria cultures by FAME analysis was identical to that indicated by IGS-RFLP classification with AluI.

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.

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.

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.

Depth perception from moving cast shadow in macaque monkey.

PubMed

Mizutani, Saneyuki; Usui, Nobuo; Yokota, Takanori; Mizusawa, Hidehiro; Taira, Masato; Katsuyama, Narumi

2015-07-15

In the present study, we investigate whether the macaque monkey can perceive motion in depth using a moving cast shadow. To accomplish this, we conducted two experiments. In the first experiment, an adult Japanese monkey was trained in a motion discrimination task in depth by binocular disparity. A square was presented on the display so that it appeared with a binocular disparity of 0.12 degrees (initial position), and moved toward (approaching) or away from (receding) the monkey for 1s. The monkey was trained to discriminate the approaching and receding motion of the square by GO/delayed GO-type responses. The monkey showed a significantly high accuracy rate in the task, and the performance was maintained when the position, color, and shape of the moving object were changed. In the next experiment, the change in the disparity was gradually decreased in the motion discrimination task. The results showed that the performance of the monkey declined as the distance of the approaching and receding motion of the square decreased from the initial position. However, when a moving cast shadow was added to the stimulus, the monkey responded to the motion in depth induced by the cast shadow in the same way as by binocular disparity; the reward was delivered randomly or given in all trials to prevent the learning of the 2D motion of the shadow in the frontal plane. These results suggest that the macaque monkey can perceive motion in depth using a moving cast shadow as well as using binocular disparity. Copyright © 2015 Elsevier B.V. All rights reserved.

Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

PubMed Central

Schmitt, Laurent; Regnard, Jacques; Millet, Grégoire P.

2015-01-01

Among the tools proposed to assess the athlete's “fatigue,” the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global “fatigue” level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of “fatigue.” Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes. PMID:26635629

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.

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.

Water Polo Game-Related Statistics in Women’s International Championships: Differences and Discriminatory Power

PubMed Central

Escalante, Yolanda; Saavedra, Jose M.; Tella, Victor; Mansilla, Mirella; García-Hermoso, Antonio; Dominguez, Ana M.

2012-01-01

The aims of this study were (i) to compare women’s water polo game-related statistics by match outcome (winning and losing teams) and phase (preliminary, classificatory, and semi-final/bronze medal/gold medal), and (ii) identify characteristics that discriminate performances for each phase. The game-related statistics of the 124 women’s matches played in five International Championships (World and European Championships) were analyzed. Differences between winning and losing teams in each phase were determined using the chi-squared. A discriminant analysis was then performed according to context in each of the three phases. It was found that the game-related statistics differentiate the winning from the losing teams in each phase of an international championship. The differentiating variables were both offensive (centre goals, power-play goals, counterattack goal, assists, offensive fouls, steals, blocked shots, and won sprints) and defensive (goalkeeper-blocked shots, goalkeeper-blocked inferiority shots, and goalkeeper-blocked 5-m shots). The discriminant analysis showed the game-related statistics to discriminate performance in all phases: preliminary, classificatory, and final phases (92%, 90%, and 83%, respectively). Two variables were discriminatory by match outcome (winning or losing teams) in all three phases: goals and goalkeeper-blocked shots. Key pointsThe preliminary phase that more than one variable was involved in this differentiation, including both offensive and defensive aspects of the game.The game-related statistics were found to have a high discriminatory power in predicting the result of matches with shots and goalkeeper-blocked shots being discriminatory variables in all three phases.Knowledge of the characteristics of women’s water polo game-related statistics of the winning teams and their power to predict match outcomes will allow coaches to take these characteristics into account when planning training and match preparation. PMID:24149356

PULSESMART: Pulse-based Arrhythmia Discrimination Using a Novel Smartphone Application

PubMed Central

McManus, David D.; Chong, Jo Woon; Soni, Apurv; Saczynski, Jane S.; Esa, Nada; Napolitano, Craig; Darling, Chad E.; Boyer, Edward; Rosen, Rochelle K.; Floyd, Kevin C.; Chon, Ki H.

2015-01-01

Background Atrial fibrillation (AF) is a common and dangerous paroxysmal rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. Objectives To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs) and premature ventricular contractions (PVCs). Methods We analyzed 219 2-minute pulse recordings from 121 participants with AF (n=98), PACs (n=15), or PVCs (n=15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from pre-existing AF. The PULSESMART app conducted pulse analysis using 3 methods [Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot]. We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n=65) of app users. Results The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was “useful” and “not complex” to use. Conclusions A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs. PMID:26391728

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.

Electronic tongue-based discrimination of Korean rice wines (makgeolli) including prediction of sensory evaluation and instrumental measurements.

PubMed

Kang, Bo-Sik; Lee, Jang-Eun; Park, Hyun-Jin

2014-05-15

A commercial electronic tongue was used to discriminate Korean rice wines (makgeolli) brewed from nine cultivars of rice with different amino acid and fatty acid compositions. The E-tongue was applied to establish prediction models with sensory evaluation or LC-MS/MS by partial least squares regression (PLSR). All makgeollis were classified into three groups by principal components analysis, and the separation pattern was affected by rice qualities and yeast fermentation. Makgeolli taste changed from the complicated comprising sweetness, saltiness, and umami to the uncomplicated, such as bitterness and then, sourness, with a decrease of amino acids and fatty acids in the rice. The quantitative correlation between E-tongue and sensory scores or LC-MS/MS by PLSR demonstrated that E-tongue could well predict most of the sensory attributes with relatively acceptable r(2), except for bitterness, but could not predict most of the chemical compounds responsible for taste attributes, except for ribose, lactate, succinate, and tryptophan. Copyright © 2013 Elsevier Ltd. All rights reserved.

How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations.

PubMed

Sargsyan, Karen; Grauffel, Cédric; Lim, Carmay

2017-04-11

The root-mean-square deviation (RMSD) is a similarity measure widely used in analysis of macromolecular structures and dynamics. As increasingly larger macromolecular systems are being studied, dimensionality effects such as the "curse of dimensionality" (a diminishing ability to discriminate pairwise differences between conformations with increasing system size) may exist and significantly impact RMSD-based analyses. For such large bimolecular systems, whether the RMSD or other alternative similarity measures might suffer from this "curse" and lose the ability to discriminate different macromolecular structures had not been explicitly addressed. Here, we show such dimensionality effects for both weighted and nonweighted RMSD schemes. We also provide a mechanism for the emergence of the "curse of dimensionality" for RMSD from the law of large numbers by showing that the conformational distributions from which RMSDs are calculated become increasingly similar as the system size increases. Our findings suggest the use of weighted RMSD schemes for small proteins (less than 200 residues) and nonweighted RMSD for larger proteins when analyzing molecular dynamics trajectories.

Metabolomic Change Precedes Apple Superficial Scald Symptoms

USDA-ARS?s Scientific Manuscript database

Metabolic profiling of 621 metabolites was employed to characterize metabolomic changes associated with ‘Granny Smith’ apple superficial scald development following 1-MCP or DPA treatment. Partial least squares-discriminant analyses were used to link metabolites with scald, postharvest treatments, ...

Descriptors of sensation confirm the multidimensional nature of desire to void.

PubMed

Das, Rebekah; Buckley, Jonathan D; Williams, Marie T

2015-02-01

To collect and categorize descriptors of "desire to void" sensation, determine the reliability of descriptor categories and assess whether descriptor categories discriminate between people with and without symptoms of overactive bladder. This observational, repeated measures study involved 64 Australian volunteers (47 female), aged 50 years or more, with and without symptoms of overactive bladder. Descriptors of desire to void sensation were derived from a structured interview (conducted on two occasions, 1 week apart). Descriptors were recorded verbatim and categorized in a three-stage process. Overactive bladder status was determined by the Overactive Bladder Awareness Tool and the Overactive Bladder Symptom Score. McNemar's test assessed the reliability of descriptors volunteered between two occasions and Partial Least Squares Regression determined whether language categories discriminated according to overactive bladder status. Post hoc Chi squared analysis and relative risk calculation determined the size and direction of overactive bladder prediction. Thirteen language categories (Urgency, Fullness, Pressure, Tickle/tingle, Pain/ache, Heavy, Normal, Intense, Sudden, Annoying, Uncomfortable, Anxiety, and Unique somatic) encapsulated 344 descriptors of sensation. Descriptor categories were stable between two interviews. The categories "Urgency" and "Fullness" predicted overactive bladder status. Participants who volunteered "Urgency" descriptors were twice as likely to have overactive bladder and participants who volunteered "Fullness" descriptors were almost three times as likely not to have overactive bladder. The sensation of desire to void is reliably described over sessions separated by a week, the language used reflects multiple dimensions of sensation, and can predict overactive bladder status. © 2013 Wiley Periodicals, Inc.

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.

Chromatographic profiles of Phyllanthus aqueous extracts samples: a proposition of classification using chemometric models.

PubMed

Martins, Lucia Regina Rocha; Pereira-Filho, Edenir Rodrigues; Cass, Quezia Bezerra

2011-04-01

Taking in consideration the global analysis of complex samples, proposed by the metabolomic approach, the chromatographic fingerprint encompasses an attractive chemical characterization of herbal medicines. Thus, it can be used as a tool in quality control analysis of phytomedicines. The generated multivariate data are better evaluated by chemometric analyses, and they can be modeled by classification methods. "Stone breaker" is a popular Brazilian plant of Phyllanthus genus, used worldwide to treat renal calculus, hepatitis, and many other diseases. In this study, gradient elution at reversed-phase conditions with detection at ultraviolet region were used to obtain chemical profiles (fingerprints) of botanically identified samples of six Phyllanthus species. The obtained chromatograms, at 275 nm, were organized in data matrices, and the time shifts of peaks were adjusted using the Correlation Optimized Warping algorithm. Principal Component Analyses were performed to evaluate similarities among cultivated and uncultivated samples and the discrimination among the species and, after that, the samples were used to compose three classification models using Soft Independent Modeling of Class analogy, K-Nearest Neighbor, and Partial Least Squares for Discriminant Analysis. The ability of classification models were discussed after their successful application for authenticity evaluation of 25 commercial samples of "stone breaker."

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.

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.

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

PubMed Central

Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

2012-01-01

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis.

PubMed

Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede

2015-12-01

This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Differentiation of four Aspergillus species and one Zygosaccharomyces with two electronic tongues based on different measurement techniques.

PubMed

Söderström, C; Rudnitskaya, A; Legin, A; Krantz-Rülcker, C

2005-09-29

Two electronic tongues based on different measurement techniques were applied to the discrimination of four molds and one yeast. Chosen microorganisms were different species of Aspergillus and yeast specie Zygosaccharomyces bailii, which are known as food contaminants. The electronic tongue developed in Linköping University was based on voltammetry. Four working electrodes made of noble metals were used in a standard three-electrode configuration in this case. The St. Petersburg electronic tongue consisted of 27 potentiometric chemical sensors with enhanced cross-sensitivity. Sensors with chalcogenide glass and plasticized PVC membranes were used. Two sets of samples were measured using both electronic tongues. Firstly, broths were measured in which either one of the molds or the yeast grew until late logarithmic phase or border of the stationary phase. Broths inoculated by either one of molds or the yeast was measured at five different times during microorganism growth. Data were evaluated using principal component analysis (PCA), partial least square regression (PLS) and linear discriminant analysis (LDA). It was found that both measurement techniques could differentiate between fungi species. Merged data from both electronic tongues improved differentiation of the samples in selected cases.

Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling.

PubMed

Senden, R; Savelberg, H H C M; Grimm, B; Heyligers, I C; Meijer, K

2012-06-01

This study investigated whether the Tinetti scale, as a subjective measure for fall risk, is associated with objectively measured gait characteristics. It is studied whether gait parameters are different for groups that are stratified for fall risk using the Tinetti scale. Moreover, the discriminative power of gait parameters to classify elderly according to the Tinetti scale is investigated. Gait of 50 elderly with a Tinneti>24 and 50 elderly with a Tinetti≤24 was analyzed using acceleration-based gait analysis. Validated algorithms were used to derive spatio-temporal gait parameters, harmonic ratio, inter-stride amplitude variability and root mean square (RMS) from the accelerometer data. Clear differences in gait were found between the groups. All gait parameters correlated with the Tinetti scale (r-range: 0.20-0.73). Only walking speed, step length and RMS showed moderate to strong correlations and high discriminative power to classify elderly according to the Tinetti scale. It is concluded that subtle gait changes that have previously been related to fall risk are not captured by the subjective assessment. It is therefore worthwhile to include objective gait assessment in fall risk screening. Copyright © 2012 Elsevier B.V. All rights reserved.

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

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance–Infrared Spectral Data

PubMed Central

Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-01-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance–infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties. PMID:28585466

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance-Infrared Spectral Data.

PubMed

Zargaran, Arman; Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-10-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance-infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

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.

Longitudinal Discriminant Analysis of Hemoglobin Level for Predicting Preeclampsia

PubMed Central

Nasiri, Malihe; Faghihzadeh, Soghrat; Alavi Majd, Hamid; Zayeri, Farid; Kariman, Noorosadat; Safavi Ardebili, Nastaran

2015-01-01

Background: Preeclampsia is one of the most serious complications during pregnancy with important effects on health of mother and fetus that causes maternal and fetal morbidity and mortality. This study was performed to evaluate whether high levels of hemoglobin may increase the risk of preeclampsia. Objectives: The present study aimed to predict preeclampsia by the hemoglobin profiles through longitudinal discriminant analysis and comparing the error rate of discrimination in longitudinal and cross sectional data. Patients and Methods: In a prospective cohort study from October 2010 to July 2011, 650 pregnant women referred to the prenatal clinic of Milad Hospital in Tehran were evaluated in 3 stages. The hemoglobin level of each woman was measured in the first, second, and third trimester of pregnancy by an expert technician. The subjects were followed up to delivery and preeclampsia was the main outcome under study. The covariance pattern and linear-mixed effects models are common methods that were applied for discriminant analysis of longitudinal data. Also Student t, Mann-Whitney U, and chi-square tests were used for comparing the demographic and clinical characteristics between two groups. Statistical analyses were performed using the SAS software version 9.1. Results: The prevalence rate of preeclampsia was 7.2% (47 women). The women with preeclampsia had a higher mean of hemoglobin values and the difference was 0.46 g/dL (P = 0.003). Also the mean of hemoglobin in the first trimester was higher than that of the second trimester, and was lower than that of the third trimester and the differences were significant (P = 0.015 and P < 0.001, respectively). The sensitivity for longitudinal data and cross-sectional data in three trimesters was 90%, 67%, 72%, and 54% and the specificity was 88%, 55%, 63%, and 50%, respectively. Conclusions: The levels of hemoglobin can be used to predict preeclampsia and monitoring the pregnant women and its regular measure in 3 trimesters help us to identify women at risk for preeclampsia. PMID:26019901

Rapid profiling of Swiss cheese by attenuated total reflectance (ATR) infrared spectroscopy and descriptive sensory analysis.

PubMed

Kocaoglu-Vurma, N A; Eliardi, A; Drake, M A; Rodriguez-Saona, L E; Harper, W J

2009-08-01

The acceptability of cheese depends largely on the flavor formed during ripening. The flavor profiles of cheeses are complex and region- or manufacturer-specific which have made it challenging to understand the chemistry of flavor development and its correlation with sensory properties. Infrared spectroscopy is an attractive technology for the rapid, sensitive, and high-throughput analysis of foods, providing information related to its composition and conformation of food components from the spectra. Our objectives were to establish infrared spectral profiles to discriminate Swiss cheeses produced by different manufacturers in the United States and to develop predictive models for determination of sensory attributes based on infrared spectra. Fifteen samples from 3 Swiss cheese manufacturers were received and analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). The spectra were analyzed using soft independent modeling of class analogy (SIMCA) to build a classification model. The cheeses were profiled by a trained sensory panel using descriptive sensory analysis. The relationship between the descriptive sensory scores and ATR-IR spectra was assessed using partial least square regression (PLSR) analysis. SIMCA discriminated the Swiss cheeses based on manufacturer and production region. PLSR analysis generated prediction models with correlation coefficients of validation (rVal) between 0.69 and 0.96 with standard error of cross-validation (SECV) ranging from 0.04 to 0.29. Implementation of rapid infrared analysis by the Swiss cheese industry would help to streamline quality assurance.

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.

Chemosensory characteristics of regional Vidal icewines from China and Canada.

PubMed

Huang, Ling; Ma, Yue; Tian, Xin; Li, Ji-Ming; Li, Lan-Xiao; Tang, Ke; Xu, Yan

2018-09-30

This work aimed to compare the flavor characteristics of Vidal icewines from China and Canada and to establish relationships between sensory descriptors and chemical composition. Descriptive analysis was performed with a trained panel to obtain the sensory profiles. Thirty important aroma-active compounds were quantified by four different methodologies. Partial least squares discriminant analysis was used to identify candidate compounds, which were unique to certain sensory descriptors. The sensory profiles of icewines from China were characterized by nut and honey aromas, while icewines from Canada expressed caramel and rose aromas. Nut and honey aromas had a close correlation with 1-hexanol, isoamyl acetate, phenethyl acetate and phenylethyl alcohol. Caramel aroma was correlated with ethyl esters and lactones and rose aroma was correlated with terpenes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Texture discrimination and multi-unit recording in the rat vibrissal nerve

PubMed Central

Albarracín, Ana L; Farfán, Fernando D; Felice, Carmelo J; Décima, Emilio E

2006-01-01

Background Rats distinguish objects differing in surface texture by actively moving their vibrissae. In this paper we characterized some aspects of texture sensing in anesthetized rats during active touch. We analyzed the multifiber discharge from a deep vibrissal nerve when the vibrissa sweeps materials (wood, metal, acrylic, sandpaper) having different textures. We polished these surfaces with sandpaper (P1000) to obtain close degrees of roughness and we induced vibrissal movement with two-branch facial nerve stimulation. We also consider the change in pressure against the vibrissa as a way to improve the tactile information acquisition. The signals were compared with a reference signal (control) – vibrissa sweeping the air – and were analyzed with the Root Mean Square (RMS) and the Power Spectrum Density (PSD). Results We extracted the information about texture discrimination hidden in the population activity of one vibrissa innervation, using the RMS values and the PSD. The pressure level 3 produced the best differentiation for RMS values and it could represent the "optimum" vibrissal pressure for texture discrimination. The frequency analysis (PSD) provided information only at low-pressure levels and showed that the differences are not related to the roughness of the materials but could be related to other texture parameters. Conclusion Our results suggest that the physical properties of different materials could be transduced by the trigeminal sensory system of rats, as are shown by amplitude and frequency changes. Likewise, varying the pressure could represent a behavioral strategy that improves the information acquisition for texture discrimination. PMID:16719904

Texture discrimination and multi-unit recording in the rat vibrissal nerve.

PubMed

Albarracín, Ana L; Farfán, Fernando D; Felice, Carmelo J; Décima, Emilio E

2006-05-23

Rats distinguish objects differing in surface texture by actively moving their vibrissae. In this paper we characterized some aspects of texture sensing in anesthetized rats during active touch. We analyzed the multifiber discharge from a deep vibrissal nerve when the vibrissa sweeps materials (wood, metal, acrylic, sandpaper) having different textures. We polished these surfaces with sandpaper (P1000) to obtain close degrees of roughness and we induced vibrissal movement with two-branch facial nerve stimulation. We also consider the change in pressure against the vibrissa as a way to improve the tactile information acquisition. The signals were compared with a reference signal (control)--vibrissa sweeping the air--and were analyzed with the Root Mean Square (RMS) and the Power Spectrum Density (PSD). We extracted the information about texture discrimination hidden in the population activity of one vibrissa innervation, using the RMS values and the PSD. The pressure level 3 produced the best differentiation for RMS values and it could represent the "optimum" vibrissal pressure for texture discrimination. The frequency analysis (PSD) provided information only at low-pressure levels and showed that the differences are not related to the roughness of the materials but could be related to other texture parameters. Our results suggest that the physical properties of different materials could be transduced by the trigeminal sensory system of rats, as are shown by amplitude and frequency changes. Likewise, varying the pressure could represent a behavioral strategy that improves the information acquisition for texture discrimination.

Preliminary Study of Information Extraction of LANDSAT TM Data for a Suburban/regional Test Site

NASA Technical Reports Server (NTRS)

Toll, D. L.

1985-01-01

A substantial amount of spectral information is available from TM (as compared to MSS) data for a 14.25 square km area between Beltsville and Laurel, Maryland. Large buildings and street patterns were resolved in the TM imagery. While there was added information content in TM data for discriminating surburban/regional land cover, characteristics of MSS can improve land cover discrimination over TM when conventional classification procedures are used on digital data. The improved qualitization of TM is likely valuable in situations where there are spectral similarities between classes. The spatial resolution in TM decreased land cover discrimination as a result of increased within class variability. For many general digital evaluations, inclusion of four bands representing the four spectral regions can provide much useful land cover discrimination. Inclusion of TM 6 indicates an improvement in spectral class discrimination. Of primary spectral importance is the discrimination between water, vegetative surfaces, and impervious surfaces due to differences in thermal properties. Results from the principle component transformed data clearly indicates additional information content in TM over MSS.

Discrimination of Human Forearm Motions on the Basis of Myoelectric Signals by Using Adaptive Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Kiso, Atsushi; Seki, Hirokazu

This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

Olfactory bulb size, odor discrimination and magnetic insensitivity in hummingbirds.

PubMed

Ioalé, P; Papi, F

1989-05-01

Relative olfactory bulb size with respect to telencephalic hemispheres (olfactory ratio) was measured in five species of hummingbirds. Trochiliformes were found to be next to last among 25 avian orders with respect to olfactory bulb development. One hummingbird species, the White-vented Violetear (Colibri serrirostris), was trained in a successive go/no-go discrimination task, and learned to feed or not to feed from a container dependent on the olfactory stimuli associated with it. Test birds learned to discriminate amyl acetate vs. turpentine essence, jasmine essence vs. lavender essence, eucalyptus essence vs. no odor, beta-ionone vs. no odor, carvone vs. eucalyptol. In contrast, 1-phenylethanol vs. beta-ionone discrimination, two odorants which appear similar to humans, was unsuccessful. Using a similar procedure, attempts were made to condition a White-vented Violetear and a Versicolored Emerald (Amazilia versicolor) to magnetic stimuli. The birds were unable to discriminate between a normal field and an oscillating field (square wave, 1 Hz, amplitude +/- 0.40 G).

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.

Minimum Bayes risk image correlation

NASA Technical Reports Server (NTRS)

Minter, T. C., Jr.

1980-01-01

In this paper, the problem of designing a matched filter for image correlation will be treated as a statistical pattern recognition problem. It is shown that, by minimizing a suitable criterion, a matched filter can be estimated which approximates the optimum Bayes discriminant function in a least-squares sense. It is well known that the use of the Bayes discriminant function in target classification minimizes the Bayes risk, which in turn directly minimizes the probability of a false fix. A fast Fourier implementation of the minimum Bayes risk correlation procedure is described.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

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.

6,7-dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid attenuates heptatocellular carcinoma in rats with NMR-based metabolic perturbations.

PubMed

Kumar, Pranesh; Singh, Ashok K; Raj, Vinit; Rai, Amit; Maity, Siddhartha; Rawat, Atul; Kumar, Umesh; Kumar, Dinesh; Prakash, Anand; Guleria, Anupam; Saha, Sudipta

2017-08-01

6,7-dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid (M1) was synthesized and evaluated for in-vivo antiproliferative action in diethylnitrosamine-induced hepatocarcinogenic rats. The antiproliferative effect of M1 was assessed by various biochemical parameters, histopathology of liver and HPLC analysis. Proton nuclear magnetic resonance-based serum metabolic study was implemented on rat sera to explore the effects of M1 on hepatocellular carcinoma-induced metabolic alterations. M1 showed protective action on liver and restored the arrangement of liver tissues in normal proportion. HPLC analysis displayed a good plasma drug concentration after its oral administration. Score plots of partial least squares discriminate analysis models exhibited that M1 therapy ameliorated hepatocellular carcinoma-induced metabolic alterations which signified its antiproliferative potential. M1 manifested notable antiproliferative profile, and warrants further investigation for future anticancer therapy.

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.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

Metabolomic Investigation of Rat Serum Following Oral Administration of the Willow Bracket Medicinal Mushroom, Phellinus igniarius (Agaricomycetes), by UPLC-HDMS.

PubMed

Dong, Yu; He, Ying; Yu, Zhongming; Zhang, Yang; Wang, Nani; Shou, Dan; Li, Changyu

2016-01-01

The medicinal willow bracket mushroom, Phellinus igniarius, is a species that has been reported to possess antibacterial, antioxidative, antitumor, antidiabetic, and antihyperlipidemia activities. The aim of this study was to elucidate the changes in endogenous metabolites after oral administration of a decoction of Ph. Igniarius. Ultraperformance liquid chromatography (UPLC)/electrospray ionization synapt high-definition mass spectrometry (ESI-HDMS) combined with pattern recognition approaches, including principal component analysis and orthogonal partial least squares discriminant analysis, were integrated to discover differentiating metabolites. The current metabolomics approach identified 16 ions (5 in the negative mode, 11 in the positive mode) as "differentiating metabolites". The results illustrated that Ph. Igniarius is likely to increase the biosynthesis and secretion of bile acids that provide hypolipidemic activity and showed that robust UPLC/ESI-HDMS techniques are promising for profiling analysis of medicinal mushroom metabolites.

Taste characteristics of Chinese bayberry juice characterized by sensory evaluation, chromatography analysis, and an electronic tongue.

PubMed

Yu, Haiyan; Zhang, Yan; Zhao, Jie; Tian, Huaixiang

2018-05-01

To evaluate the taste characteristics of Chinese bayberry juice, four types of bayberry juice sourced from different origins and varieties were analysed using sensory evaluation, chromatography, spectroscopy analysis and an electronic tongue (E-tongue). Nine organic acids and three sugars were assessed using high performance liquid chromatography. Total polyphenols were measured by spectrophotometry. The overall taste profile was collected using the E-tongue. The four types of bayberry juice differed in the sensory attributes of sour, sweet, bitter, and astringent. The E-tongue responses combined with discriminant analysis were able to characterise the taste profiles of the juices. The relationships between the taste compounds and the sensory panel scores established by partial least squares showed that total polyphenols, quininic acid, maleic acid, fructose, citric acid, lactic acid, succinic acid and sucrose made significant contributions to the taste characteristics of the Chinese bayberry juice.

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.

On the Identification of Rayon/Viscose as a Major Fraction of Microplastics in the Marine Environment: Discrimination between Natural and Manmade Cellulosic Fibers Using Fourier Transform Infrared Spectroscopy

PubMed Central

Comnea-Stancu, Ionela Raluca; Wieland, Karin; Ramer, Georg; Schwaighofer, Andreas

2016-01-01

This work was sparked by the reported identification of man-made cellulosic fibers (rayon/viscose) in the marine environment as a major fraction of plastic litter by Fourier transform infrared (FT-IR) transmission spectroscopy and library search. To assess the plausibility of such findings, both natural and man-made fibers were examined using FT-IR spectroscopy. Spectra acquired by transmission microscopy, attenuated total reflection (ATR) microscopy, and ATR spectroscopy were compared. Library search was employed and results show significant differences in the identification rate depending on the acquisition method of the spectra. Careful selection of search parameters and the choice of spectra acquisition method were found to be essential for optimization of the library search results. When using transmission spectra of fibers and ATR libraries it was not possible to differentiate between man-made and natural fibers. Successful differentiation of natural and man-made cellulosic fibers has been achieved for FT-IR spectra acquired by ATR microscopy and ATR spectroscopy, and application of ATR libraries. As an alternative, chemometric methods such as unsupervised hierarchical cluster analysis, principal component analysis, and partial least squares-discriminant analysis were employed to facilitate identification based on intrinsic relationships of sample spectra and successful discrimination of the fiber type could be achieved. Differences in the ATR spectra depending on the internal reflection element (Ge versus diamond) were observed as expected; however, these did not impair correct classification by chemometric analysis. Moreover, the effects of different levels of humidity on the IR spectra of natural and man-made fibers were investigated, too. It has been found that drying and re-humidification leads to intensity changes of absorption bands of the carbohydrate backbone, but does not impair the identification of the fiber type by library search or cluster analysis. PMID:27650982

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.

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.

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

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

Preliminary identification of unicellular algal genus by using combined confocal resonance Raman spectroscopy with PCA and DPLS analysis

NASA Astrophysics Data System (ADS)

He, Shixuan; Xie, Wanyi; Zhang, Ping; Fang, Shaoxi; Li, Zhe; Tang, Peng; Gao, Xia; Guo, Jinsong; Tlili, Chaker; Wang, Deqiang

2018-02-01

The analysis of algae and dominant alga plays important roles in ecological and environmental fields since it can be used to forecast water bloom and control its potential deleterious effects. Herein, we combine in vivo confocal resonance Raman spectroscopy with multivariate analysis methods to preliminary identify the three algal genera in water blooms at unicellular scale. Statistical analysis of characteristic Raman peaks demonstrates that certain shifts and different normalized intensities, resulting from composition of different carotenoids, exist in Raman spectra of three algal cells. Principal component analysis (PCA) scores and corresponding loading weights show some differences from Raman spectral characteristics which are caused by vibrations of carotenoids in unicellular algae. Then, discriminant partial least squares (DPLS) classification method is used to verify the effectiveness of algal identification with confocal resonance Raman spectroscopy. Our results show that confocal resonance Raman spectroscopy combined with PCA and DPLS could handle the preliminary identification of dominant alga for forecasting and controlling of water blooms.

Sex differences in gut microbiota in patients with major depressive disorder.

PubMed

Chen, Jian-Jun; Zheng, Peng; Liu, Yi-Yun; Zhong, Xiao-Gang; Wang, Hai-Yang; Guo, Yu-Jie; Xie, Peng

2018-01-01

Our previous studies found that disturbances in gut microbiota might have a causative role in the onset of major depressive disorder (MDD). The aim of this study was to investigate whether there were sex differences in gut microbiota in patients with MDD. First-episode drug-naïve MDD patients and healthy controls were included. 16S rRNA gene sequences extracted from the fecal samples of the included subjects were analyzed. Principal-coordinate analysis and partial least squares-discriminant analysis were used to assess whether there were sex-specific gut microbiota. A random forest algorithm was used to identify the differential operational taxonomic units. Linear discriminant-analysis effect size was further used to identify the dominant sex-specific phylotypes responsible for the differences between MDD patients and healthy controls. In total, 57 and 74 differential operational taxonomic units responsible for separating female and male MDD patients from their healthy counterparts were identified. Compared with their healthy counterparts, increased Actinobacteria and decreased Bacteroidetes levels were found in female and male MDD patients, respectively. The most differentially abundant bacterial taxa in female and male MDD patients belonged to phyla Actinobacteria and Bacteroidia, respectively. Meanwhile, female and male MDD patients had different dominant phylotypes. These results demonstrated that there were sex differences in gut microbiota in patients with MDD. The suitability of Actinobacteria and Bacteroidia as the sex-specific biomarkers for diagnosing MDD should be further explored.

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.

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.

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

Characterization and discrimination of Taihe black-boned silky fowl (Gallus gallus domesticus Brisson) muscles using LC/MS-based lipidomics.

PubMed

Mi, Si; Shang, Ke; Jia, Wei; Zhang, Chun-Hui; Li, Xia; Fan, Yu-Qing; Wang, Hang

2018-07-01

Taihe black-boned silky fowl (Gallus gallus domesticus Brisson) has a history of over 2200 years of being consumed as a curative food in China. In this work, an LC/MS-based lipidomics approach was employed to investigate the characteristic lipid composition of Taihe black-boned silky fowls from different ages and genders as well as from different carcass parts. Data were processed using an orthogonal partial least squares discriminant analysis and one-way analysis of variance. A total of 1127 lipids were detected in Taihe black-boned silky fowl muscles. Among them, 88, 11 and 1 lipid species were found to have both a variable influence on a projection value >1 and a p-value smaller than 0.05 between different age, gender and part groups. These results illustrate that the influence of the 3 investigated factors on the lipid profiles of Taihe black-boned silky fowl decreased in the order of age > gender > part. Lipid profile differences will facilitate a better understanding of the curative properties of Taihe black-boned silky fowl. Taihe and crossbred black-boned silky fowls were compared in terms of their lipid compositions based on the same strategy. The results showed that the two groups were able to discriminate from each other effectively. 47 lipid compounds were determined to be potential markers for the authentication of Taihe black-boned silky fowl. This work demonstrates the successful application of lipidomics for lipid profiling in food raw materials. Copyright © 2018 Elsevier Ltd. All rights reserved.

Increased mean aliphatic lipid chain length in left ventricular hypertrophy secondary to arterial hypertension: A cross-sectional study.

PubMed

Evaristi, Maria Francesca; Caubère, Céline; Harmancey, Romain; Desmoulin, Franck; Peacock, William Frank; Berry, Matthieu; Turkieh, Annie; Barutaut, Manon; Galinier, Michel; Dambrin, Camille; Polidori, Carlo; Miceli, Cristina; Chamontin, Bernard; Koukoui, François; Roncalli, Jerôme; Massabuau, Pierre; Smih, Fatima; Rouet, Philippe

2016-11-01

About 77.9 million (1 in 4) American adults have high blood pressure. High blood pressure is the primary cause of left ventricular hypertrophy (LVH), which represents a strong predictor of future heart failure and cardiovascular mortality. Previous studies have shown an altered metabolic profile in hypertensive patients with LVH. The goal of this study was to identify blood metabolomic LVH biomarkers by H NMR to provide novel diagnostic tools for rapid LVH detection in populations of hypertensive individuals. This cross-sectional study included 48 hypertensive patients with LVH matched with 48 hypertensive patients with normal LV size, and 24 healthy controls. Two-dimensional targeted M-mode echocardiography was performed to measure left ventricular mass index. Partial least squares discriminant analysis was used for the multivariate analysis of the H NMR spectral data. From the H NMR-based metabolomic profiling, signals coming from methylene (-CH2-) and methyl (-CH3) moieties of aliphatic chains from plasma lipids were identified as discriminant variables. The -CH2-/-CH3 ratio, an indicator of the mean length of the aliphatic lipid chains, was significantly higher (P < 0.001) in the LVH group than in the hypertensive group without LVH and controls. Receiver operating characteristic curve showed that a cutoff of 2.34 provided a 52.08% sensitivity and 85.42% specificity for discriminating LVH (AUC = 0.703, P-value < 0.001). We propose the -CH2-/-CH3 ratio from plasma aliphatic lipid chains as a biomarker for the diagnosis of left ventricular remodeling in hypertension.

The effect of combining two echo times in automatic brain tumor classification by MRS.

PubMed

García-Gómez, Juan M; Tortajada, Salvador; Vidal, César; Julià-Sapé, Margarida; Luts, Jan; Moreno-Torres, Angel; Van Huffel, Sabine; Arús, Carles; Robles, Montserrat

2008-11-01

(1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd.

Phylogenetic comparative methods complement discriminant function analysis in ecomorphology.

PubMed

Barr, W Andrew; Scott, Robert S

2014-04-01

In ecomorphology, Discriminant Function Analysis (DFA) has been used as evidence for the presence of functional links between morphometric variables and ecological categories. Here we conduct simulations of characters containing phylogenetic signal to explore the performance of DFA under a variety of conditions. Characters were simulated using a phylogeny of extant antelope species from known habitats. Characters were modeled with no biomechanical relationship to the habitat category; the only sources of variation were body mass, phylogenetic signal, or random "noise." DFA on the discriminability of habitat categories was performed using subsets of the simulated characters, and Phylogenetic Generalized Least Squares (PGLS) was performed for each character. Analyses were repeated with randomized habitat assignments. When simulated characters lacked phylogenetic signal and/or habitat assignments were random, <5.6% of DFAs and <8.26% of PGLS analyses were significant. When characters contained phylogenetic signal and actual habitats were used, 33.27 to 45.07% of DFAs and <13.09% of PGLS analyses were significant. False Discovery Rate (FDR) corrections for multiple PGLS analyses reduced the rate of significance to <4.64%. In all cases using actual habitats and characters with phylogenetic signal, correct classification rates of DFAs exceeded random chance. In simulations involving phylogenetic signal in both predictor variables and predicted categories, PGLS with FDR was rarely significant, while DFA often was. In short, DFA offered no indication that differences between categories might be explained by phylogenetic signal, while PGLS did. As such, PGLS provides a valuable tool for testing the functional hypotheses at the heart of ecomorphology. Copyright © 2013 Wiley Periodicals, Inc.

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.

Microclimate influence on mineral and metabolic profiles of grape berries.

PubMed

Pereira, G E; Gaudillere, J-P; Pieri, P; Hilbert, G; Maucourt, M; Deborde, C; Moing, A; Rolin, D

2006-09-06

The grape berry microclimate is known to influence berry quality. The effects of the light exposure of grape berry clusters on the composition of berry tissues were studied on the "Merlot" variety grown in a vineyard in Bordeaux, France. The light exposure of the fruiting zone was modified using different intensities of leaf removal, cluster position relative to azimuth, and berry position in the cluster. Light exposures were identified and classified by in situ measurements of berry temperatures. Berries were sampled at maturity (>19 Brix) for determination of skin and/or pulp chemical and metabolic profiles based on (1) chemical and physicochemical measurement of minerals (N, P, K, Ca, Mg), (2) untargeted 1H NMR metabolic fingerprints, and HPLC targeted analyses of (3) amino acids and (4) phenolics. Each profile defined by partial least-square discriminant analysis allowed us to discriminate berries from different light exposure. Discriminant compounds between shaded and light-exposed berries were quercetin-3-glucoside, kaempferol-3-glucoside, myricetin-3-glucoside, and isorhamnetin-3-glucoside for the phenolics, histidine, valine, GABA, alanine, and arginine for the amino acids, and malate for the organic acids. Capacities of the different profiling techniques to discriminate berries were compared. Although the proportion of explained variance from the 1H NMR fingerprint was lower compared to that of chemical measurements, NMR spectroscopy allowed us to identify lit and shaded berries. Light exposure of berries increased the skin and pulp flavonols, histidine and valine contents, and reduced the organic acids, GABA, and alanine contents. All the targeted and nontargeted analytical data sets used made it possible to discriminate sun-exposed and shaded berries. The skin phenolics pattern was the most discriminating and allowed us to sort sun from shade berries. These metabolite classes can be used to qualify berries collected in an undetermined environment. The physiological significance of light and temperature effects on berry composition is discussed.

Profiles in coping: responses to sexual harassment across persons, organizations, and cultures.

PubMed

Cortina, Lilia M; Wasti, S Arzu

2005-01-01

This study explicates the complexity of sexual harassment coping behavior among 4 diverse samples of working women: (a) working-class Hispanic Americans, (b) working-class Anglo Americans, (c) professional Turks, and (d) professional Anglo Americans. K-means cluster analysis revealed 3 common harassment coping profiles: (a) detached, (b) avoidant negotiating, and (c) support seeking. The authors then tested an integrated framework of coping profile determinants, involving social power, stressor severity, social support, and culture. Analysis of variance, chi-square, and discriminant function results identified significant determinants at each of the 4 levels of this ecological model. These findings underscore the importance of focusing on whole patterns of experience--and considering influences at the level of the individual employee and multiple levels of the surrounding context--when studying how women cope with workplace sexual harassment.

Fatty acids and fat-soluble vitamins in ewe's milk predicted by near infrared reflectance spectroscopy. Determination of seasonality.

PubMed

Revilla, I; Escuredo, O; González-Martín, M I; Palacios, C

2017-01-01

The aim of the present work was to determine the fatty acid and fat-soluble vitamin composition and the season of ewe's milk production using NIR spectroscopy. 219 ewe's milk samples from different breeds and feeding regimes were taken each month over one year. Fatty acids were analyzed by gas chromatography, and retinol and α-, and γ-tocopherol by liquid chromatography. The results showed that the quantification was more accurate for the milk dried on paper, except for vitamins. Calibration statistical descriptors on milk dried on paper were good for capric, lauric, myristic, palmitoleic, stearic and oleic acids, and acceptable for caprilic, undecanoic, 9c, 11tCLA, ΣCLA, PUFA, ω3, ω6, retinol and α-tocopherol. The equations for the discrimination of seasonality was obtained using the partial least squares discriminant analysis (PLSDA) algorithm. 93% of winter samples and 89% of summer samples were correctly classified using the NIR spectra of milk dried on paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Secondary Psychometric Examination of the Dimensional Obsessive-Compulsive Scale: Classical Testing, Item Response Theory, and Differential Item Functioning.

PubMed

Thibodeau, Michel A; Leonard, Rachel C; Abramowitz, Jonathan S; Riemann, Bradley C

2015-12-01

The Dimensional Obsessive-Compulsive Scale (DOCS) is a promising measure of obsessive-compulsive disorder (OCD) symptoms but has received minimal psychometric attention. We evaluated the utility and reliability of DOCS scores. The study included 832 students and 300 patients with OCD. Confirmatory factor analysis supported the originally proposed four-factor structure. DOCS total and subscale scores exhibited good to excellent internal consistency in both samples (α = .82 to α = .96). Patient DOCS total scores reduced substantially during treatment (t = 16.01, d = 1.02). DOCS total scores discriminated between students and patients (sensitivity = 0.76, 1 - specificity = 0.23). The measure did not exhibit gender-based differential item functioning as tested by Mantel-Haenszel chi-square tests. Expected response options for each item were plotted as a function of item response theory and demonstrated that DOCS scores incrementally discriminate OCD symptoms ranging from low to extremely high severity. Incremental differences in DOCS scores appear to represent unbiased and reliable differences in true OCD symptom severity. © The Author(s) 2014.

GC-MS-based metabolite profiling of Cosmos caudatus leaves possessing alpha-glucosidase inhibitory activity.

PubMed

Javadi, Neda; Abas, Faridah; Abd Hamid, Azizah; Simoh, Sanimah; Shaari, Khozirah; Ismail, Intan Safinar; Mediani, Ahmed; Khatib, Alfi

2014-06-01

Cosmos caudatus, which is known as "Ulam Raja," is an herbal plant used in Malaysia to enhance vitality. This study focused on the evaluation of the α-glucosidase inhibitory activity of different ethanolic extracts of C. caudatus. Six series of samples extracted with water, 20%, 40%, 60%, 80%, and 100% ethanol (EtOH) were employed. Gas chromatography-mass spectrometry (GC-MS) and orthogonal partial least-squares (OPLS) analysis was used to correlate bioactivity of different extracts to different metabolite profiles of C. caudatus. The obtained OPLS scores indicated a distinct and remarkable separation into 6 clusters, which were indicative of the 6 different ethanol concentrations. GC-MS can be integrated with multivariate data analysis to identify compounds that inhibit α-glucosidase activity. In addition, catechin, α-linolenic acid, α-D-glucopyranoside, and vitamin E compounds were identified and indicate the potential α-glucosidase inhibitory activity of this herb. GC-MS and multivariate data analysis was applied to discriminate Cosmos caudatus samples extracted with water and different ratio of ethanol. Orthogonal partial least-squares (OPLS) model developed was used to determine the major metabolites contributed to α-glucosidase inhibitory activity. This approach also has the ability to predict the bioactivity of a new set of extracts based on a developed validated regression model that is important for quality control of the herb preparation. © 2014 Institute of Food Technologists®

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.

An Innovative Approach for The Integration of Proteomics and Metabolomics Data In Severe Septic Shock Patients Stratified for Mortality.

PubMed

Cambiaghi, Alice; Díaz, Ramón; Martinez, Julia Bauzá; Odena, Antonia; Brunelli, Laura; Caironi, Pietro; Masson, Serge; Baselli, Giuseppe; Ristagno, Giuseppe; Gattinoni, Luciano; de Oliveira, Eliandre; Pastorelli, Roberta; Ferrario, Manuela

2018-04-27

In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicenter ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolites concentration and relative proteins abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three different elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and Partial least squares Discriminant Analysis were also implemented. All the obtained models correctly classified the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information.

The longitudinal cerebrospinal fluid metabolomic profile of amyotrophic lateral sclerosis

PubMed Central

Gray, Elizabeth; Larkin, James R.; Claridge, Tim D. W.; Talbot, Kevin; Sibson, Nicola R.; Turner, Martin R.

2015-01-01

Neurochemical biomarkers are urgently sought in ALS. Metabolomic analysis of cerebrospinal fluid (CSF) using proton nuclear magnetic resonance (1H-NMR) spectroscopy is a highly sensitive method capable of revealing nervous system cellular pathology. The 1H-NMR CSF metabolomic signature of ALS was sought in a longitudinal cohort. Six-monthly serial collection was performed in ALS patients across a range of clinical sub-types (n = 41) for up to two years, and in healthy controls at a single time-point (n = 14). A multivariate statistical approach, partial least squares discriminant analysis, was used to determine differences between the NMR spectra from patients and controls. Significantly predictive models were found using those patients with at least one year's interval between recruitment and the second sample. Glucose, lactate, citric acid and, unexpectedly, ethanol were the discriminating metabolites elevated in ALS. It is concluded that 1H-NMR captured the CSF metabolomic signature associated with derangements in cellular energy utilization connected with ALS, and was most prominent in comparisons using patients with longer disease duration. The specific metabolites identified support the concept of a hypercatabolic state, possibly involving mitochondrial dysfunction specifically. Endogenous ethanol in the CSF may be an unrecognized novel marker of neuronal tissue injury in ALS. PMID:26121274

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.

Multivariate Modeling of Proteins Related to Trapezius Myalgia, a Comparative Study of Female Cleaners with or without Pain

PubMed Central

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia. PMID:24023854

Multivariate modeling of proteins related to trapezius myalgia, a comparative study of female cleaners with or without pain.

PubMed

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia.

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.

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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.

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.

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.

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

Mirjankar, Nikhil S.; Fraga, Carlos G.; Carman, April J.

Chemical attribution signatures (CAS) for chemical threat agents (CTAs) are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. In a previous study, anionic impurity profiles developed using high performance ion chromatography (HPIC) were demonstrated as CAS for matching samples from eight potassium cyanide (KCN) stocks to their reported countries of origin. Herein, a larger number of solid KCN stocks (n = 13) and, for the first time, solid sodium cyanide (NaCN) stocks (n = 15) were examined to determine what additional sourcing information can be obtained through anion, carbon stablemore » isotope, and elemental analyses of cyanide stocks by HPIC, isotope ratio mass spectrometry (IRMS), and inductively coupled plasma optical emission spectroscopy (ICP-OES), respectively. The HPIC anion data was evaluated using the variable selection methods of Fisher-ratio (F-ratio), interval partial least squares (iPLS), and genetic algorithm-based partial least squares (GAPLS) and the classification methods of partial least squares discriminate analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminate analysis (SVMDA). In summary, hierarchical cluster analysis (HCA) of anion impurity profiles from multiple cyanide stocks from six reported country of origins resulted in cyanide samples clustering into three groups: Czech Republic, Germany, and United States, independent of the associated alkali metal (K or Na). The three country groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries with known solid cyanide factories. Both the anion and elemental CAS are believed to originate from the aqueous alkali hydroxides used in cyanide manufacture. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). The carbon isotope CAS is believed to originate from the carbon source and process used to make the HCN utilized in cyanide synthesis. Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability (so far) of using anion impurities for matching a cyanide sample to its country of manufacture (i.e., factory). Variable selection reduced errors for those classification methods having errors greater than zero with iPLS-forward selection, and F-ratio typically providing the lowest errors. Finally, using anion profiles to match cyanides to a specific stock or stock group resulted in cross-validation errors ranging from zero to 5.3%.« less

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.

A rapid method for detection of fumonisins B1 and B2 in corn meal using Fourier transform near infrared (FT-NIR) spectroscopy implemented with integrating sphere.

PubMed

Gaspardo, B; Del Zotto, S; Torelli, E; Cividino, S R; Firrao, G; Della Riccia, G; Stefanon, B

2012-12-01

Fourier transform near infrared (FT-NIR) spectroscopy is an analytical procedure generally used to detect organic compounds in food. In this work the ability to predict fumonisin B(1)+B(2) contents in corn meal using an FT-NIR spectrophotometer, equipped with an integration sphere, was assessed. A total of 143 corn meal samples were collected in Friuli Venezia Giulia Region (Italy) and used to define a 15 principal components regression model, applying partial least square regression algorithm with full cross validation as internal validation. External validation was performed to 25 unknown samples. Coefficients of correlation, root mean square error and standard error of calibration were 0.964, 0.630 and 0.632, respectively and the external validation confirmed a fair potential of the model in predicting FB(1)+FB(2) concentration. Results suggest that FT-NIR analysis is a suitable method to detect FB(1)+FB(2) in corn meal and to discriminate safe meals from those contaminated. Copyright © 2012 Elsevier Ltd. All rights reserved.

Stereochemistry of complexes with double and triple metal-ligand bonds: a continuous shape measures analysis.

PubMed

Alvarez, Santiago; Menjón, Babil; Falceto, Andrés; Casanova, David; Alemany, Pere

2014-11-17

To each coordination polyhedron we can associate a normalized coordination polyhedron that retains the angular orientation of the central atom-ligand bonds but has all the vertices at the same distance from the center. The use of shape measures of these normalized coordination polyhedra provides a simple and efficient way of discriminating angular and bond distance distortions from an ideal polyhedron. In this paper we explore the applications of such an approach to analyses of several stereochemical problems. Among others, we discuss how to discern the off-center displacement of the metal from metal-ligand bond shortening distortions in families of square planar biscarbene and octahedral dioxo complexes. The normalized polyhedron approach is also shown to be very useful to understand stereochemical trends with the help of shape maps, minimal distortion pathways, and ligand association/dissociation pathways, illustrated by the Berry and anti Berry distortions of triple-bonded [X≡ML4] complexes, the square pyramidal geometries of Mo coordination polyhedra in oxido-reductases, the coordination geometries of actinyl complexes, and the tetrahedricity of heavy atom-substituted carbon centers.

Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy.

PubMed

Li, Shuifang; Zhang, Xin; Shan, Yang; Su, Donglin; Ma, Qiang; Wen, Ruizhi; Li, Jiaojuan

2017-03-01

Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (R p 2 ) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively. Copyright © 2016. Published by Elsevier Ltd.

New non-invasive automatic cough counting program based on 6 types of classified cough sounds.

PubMed

Murata, Akira; Ohota, Nao; Shibuya, Atsuo; Ono, Hiroshi; Kudoh, Shoji

2006-01-01

Cough consisting of an initial deep inspiration, glottal closure, and an explosive expiration accompanied by a sound is one of the most common symptoms of respiratory disease. Despite its clinical importance, standard methods for objective cough analysis have yet to be established. We investigated the characteristics of cough sounds acoustically, designed a program to discriminate cough sounds from other sounds, and finally developed a new objective method of non-invasive cough counting. In addition, we evaluated the clinical efficacy of that program. We recorded cough sounds using a memory stick IC recorder in free-field from 2 patients and analyzed the intensity of 534 recorded coughs acoustically according to time domain. First we squared the sound waveform of recorded cough sounds, which was then smoothed out over a 20 ms window. The 5 parameters and some definitions to discriminate the cough sounds from other noise were identified and the cough sounds were classified into 6 groups. Next, we applied this method to develop a new automatic cough count program. Finally, to evaluate the accuracy and clinical usefulness of this program, we counted cough sounds collected from another 10 patients using our program and conventional manual counting. And the sensitivity, specificity and discriminative rate of the program were analyzed. This program successfully discriminated recorded cough sounds out of 1902 sound events collected from 10 patients at a rate of 93.1%. The sensitivity was 90.2% and the specificity was 96.5%. Our new cough counting program can be sufficiently useful for clinical studies.

Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

PubMed

Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V

2015-05-20

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

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

Coble, Jamie; Orton, Christopher; Schwantes, Jon

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test threemore » fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.« less

Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis.

PubMed

Gilany, Kambiz; Mani-Varnosfaderani, Ahmad; Minai-Tehrani, Arash; Mirzajani, Fateme; Ghassempour, Alireza; Sadeghi, Mohammed Reza; Amini, Mehdi; Rezadoost, Hassan

2017-08-01

Male factor infertility is involved in almost half of all infertile couples. Lack of the ejaculated sperm owing to testicular malfunction has been reported in 6-10% of infertile men, a condition named nonobstructive azoospermia (NOA). In this study, we investigated untargeted metabolomic profiling of the seminal plasma in NOA men using gas chromatography-mass spectrometry and advance chemometrics. In this regard, the seminal plasma fluids of 11 NOA men with TESE-negative, nine NOA men with TESE-positive and 10 fertile healthy men (as a control group) were collected. Quadratic discriminate analysis (QDA) technique was implemented on total ion chromatograms (TICs) for identification of discriminatory retention times. We developed multivariate classification models using the QDA technique. Our results revealed that the developed QDA models could predict the classes of samples using their TIC data. The receiver operating characteristic curves for these models were >0.88. After recognition of discriminatory retention time's asymmetric penalized least square, evolving factor analysis, correlation optimized warping and alternating least squares strategies were applied for preprocessing and deconvolution of the overlapped chromatographic peaks. We could identify 36 discriminatory metabolites. These metabolites may be considered discriminatory biomarkers for different groups in NOA. Copyright © 2017 John Wiley & Sons, Ltd.

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

The Link Between Everyday Discrimination, Healthcare Utilization, and Health Status Among a National Sample of Women.

PubMed

Fazeli Dehkordy, Soudabeh; Hall, Kelli S; Dalton, Vanessa K; Carlos, Ruth C

2016-10-01

Research has not adequately examined the potential negative effects of perceiving routine discrimination on general healthcare utilization or health status, especially among reproductive-aged women. We sought to evaluate the association between everyday discrimination, health service use, and perceived health among a national sample of women in the United States. Data were drawn from the Women's Healthcare Experiences and Preferences survey, a randomly selected, national probability sample of 1078 U.S. women aged 18-55 years. We examined associations between everyday discrimination (via a standardized scale) on frequency of health service utilization and perceived general health status using chi-square and multivariable logistic regression modeling. Compared with women who reported healthcare visits every 3 years or less (reference group), each one-point increase in discrimination score was associated with higher odds of having healthcare visits annually or more often (odds ratio [OR] = 1.36, confidence interval [95% CI] = 1.01-1.83). Additionally, each one-point increase in discrimination score was significantly associated with lower odds of having excellent/very good perceived health (OR = 0.65; 95% CI = 0.54-0.80). Perceived discrimination was associated with increased exposure to the healthcare setting among this national sample of women. Perceived discrimination was also inversely associated with excellent/very good perceived health status.

Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System

PubMed Central

Khalaf, Walaa; Pace, Calogero; Gaudioso, Manlio

2009-01-01

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte. PMID:22573980

Comparison of support vector machine classification to partial least squares dimension reduction with logistic descrimination of hyperspectral data

NASA Astrophysics Data System (ADS)

Wilson, Machelle; Ustin, Susan L.; Rocke, David

2003-03-01

Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.

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.

Correlation between species-specific metabolite profiles and bioactivities of blueberries (Vaccinium spp.).

PubMed

Lee, Sarah; Jung, Eun Sung; Do, Seon-Gil; Jung, Ga-Young; Song, Gwanpil; Song, Jung-Min; Lee, Choong Hwan

2014-03-05

Metabolite profiling of three blueberry species (Vaccinium bracteatum Thunb., V. oldhamii Miquel., and V. corymbosum L.) was performed using gas chromatography-time-of-flight-mass spectrometry (GC-TOF-MS) and ultraperformance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS) combined multivariate analysis. Partial least-squares discriminant analysis clearly showed metabolic differences among species. GC-TOF-MS analysis revealed significant differences in amino acids, organic acids, fatty acids, sugars, and phenolic acids among the three blueberry species. UPLC-Q-TOF-MS analysis indicated that anthocyanins were the major metabolites distinguishing V. bracteatum from V. oldhamii. The contents of anthocyanins such as glycosides of cyanidin were high in V. bracteatum, while glycosides of delphinidin, petunidin, and malvidin were high in V. oldhamii. Antioxidant activities assessed using ABTS and DPPH assays showed the greatest activity in V. oldhamii and revealed the highest correlation with total phenolic, total flavonoid, and total anthocyanin contents and their metabolites.

Analysis of antique bronze coins by Laser Induced Breakdown Spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Bachler, M. Orlić; Bišćan, M.; Kregar, Z.; Jelovica Badovinac, I.; Dobrinić, J.; Milošević, S.

2016-09-01

This work presents a feasibility study of applying the Principal Component Analysis (PCA) to data obtained by Laser-Induced Breakdown Spectroscopy (LIBS) with the aim of determining correlation between different samples. The samples were antique bronze coins coated in silver (follis) dated in the Roman Empire period and were made during different rulers in different mints. While raw LIBS data revealed that in the period from the year 286 to 383 CE content of silver was constantly decreasing, the PCA showed that the samples can be somewhat grouped together based on their place of origin, which could be a useful hint when analysing unknown samples. It was also found that PCA can help in discriminating spectra corresponding to ablation from the surface and from the bulk. Furthermore, Partial Least Squares method (PLS) was used to obtain, based on a set of samples with known composition, an estimation of relative copper concentration in studied ancient coins. This analysis showed that copper concentration in surface layers ranged from 83% to 90%.

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

Multiplexed analysis combining distinctly-sized CdTe-MPA quantum dots and chemometrics for multiple mutually interfering analyte determination.

PubMed

Bittar, Dayana B; Ribeiro, David S M; Páscoa, Ricardo N M J; Soares, José X; Rodrigues, S Sofia M; Castro, Rafael C; Pezza, Leonardo; Pezza, Helena R; Santos, João L M

2017-11-01

Semiconductor quantum dots (QDs) have demonstrated a great potential as fluorescent probes for heavy metals monitoring. However, their great reactivity, whose tunability could be difficult to attain, could impair selectivity yielding analytical results with poor accuracy. In this work, the combination in the same analysis of multiple QDs, each with a particular ability to interact with the analyte, assured a multi-point detection that was not only exploited for a more precise analyte discrimination but also for the simultaneous discrimination of multiple mutually interfering species, in the same sample. Three different MPA-CdTe QDs (2.5, 3.0 and 3.8nm) with a good size distribution, confirmed by the FWHM values of 48.6, 55.4 and 80.8nm, respectively, were used. Principal component analysis (PCA) and partial least squares regression (PLS) were used for fluorescence data analysis. Mixtures of two MPA-CdTe QDs, emitting at different wavelength namely 549/566, 549/634 and 566/634nm were assayed. The 549/634nm emitting QDs mixture provided the best results for the discrimination of distinct ions on binary and ternary mixtures. The obtained RMSECV and R 2 CV values for the binary mixture were good, namely, from 0.01 to 0.08mgL -1 and from 0.74 to 0.89, respectively. Regarding the ternary mixture the RMSECV and R 2 CV values were good for Hg(II) (0.06 and 0.73mgL -1 , respectively) and Pb(II) (0.08 and 0.87mg L -1 , respectively) and acceptable for Cu(II) (0.02 and 0.51mgL -1 , respectively). In conclusion, the obtained results showed that the developed approach is capable of resolve binary and ternary mixtures of Pb (II), Hg (II) and Cu (II), providing accurate information about lead (II) and mercury (II) concentration and signaling the occurrence of Cu (II). Copyright © 2017 Elsevier B.V. All rights reserved.

New Biomarkers of Coffee Consumption Identified by the Non-Targeted Metabolomic Profiling of Cohort Study Subjects

PubMed Central

Martin, Jean-François; Lyan, Bernard; Pujos-Guillot, Estelle; Fezeu, Leopold; Hercberg, Serge; Comte, Blandine; Galan, Pilar; Touvier, Mathilde; Manach, Claudine

2014-01-01

Coffee contains various bioactives implicated with human health and disease risk. To accurately assess the effects of overall consumption upon health and disease, individual intake must be measured in large epidemiological studies. Metabolomics has emerged as a powerful approach to discover biomarkers of intake for a large range of foods. Here we report the profiling of the urinary metabolome of cohort study subjects to search for new biomarkers of coffee intake. Using repeated 24-hour dietary records and a food frequency questionnaire, 20 high coffee consumers (183–540 mL/d) and 19 low consumers were selected from the French SU.VI.MAX2 cohort. Morning spot urine samples from each subject were profiled by high-resolution mass spectrometry. Partial least-square discriminant analysis of multidimensional liquid chromatography-mass spectrometry data clearly distinguished high consumers from low via 132 significant (p-value<0.05) discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and in-house databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl), and the alkaloid trigonelline. Some caffeine metabolites, such as 1-methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity. Once validated in other cohorts or intervention studies, these specific single or combined biomarkers will become a valuable alternative to assessment of coffee intake by dietary survey and finally lead to a better understanding of the health implications of coffee consumption. PMID:24713823

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.

Stigmatization and Discrimination toward People Living with HIV/AIDS in a Coastal City of South India.

PubMed

Kumar, Nithin; Unnikrishnan, Bhaskaran; Thapar, Rekha; Mithra, Prasanna; Kulkarni, Vaman; Holla, Ramesh; Bhagawan, Darshan; Kumar, Avinash

The HIV/AIDS scenario all over the world is complicated by the stigmatic and discriminative attitudes toward the HIV-infected individuals. In this facility-based, cross-sectional study, 104 HIV-positive patients were assessed regarding their personal experience with HIV-related stigma and discrimination using a Revised HIV Stigma Scale. The association between stigma and factors such as socioeconomic status and gender was tested using chi-square test, and P < .05 was considered statistically significant. A large proportion (41.3%) of the participants were in the age-group of 26 to 35 years. Confidentiality of the HIV positivity status was maintained only in 14.4% of the participants. Compared to females (48.2%), more than half (51.5%) of the male participants had experienced HIV/AIDS-related personalized stigma ( P > .05). HIV-related stigma and discrimination are the major social determinants driving the epidemic, despite the advances in medical treatment and increases in the awareness about the disease.

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.

Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

NASA Astrophysics Data System (ADS)

Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

2015-03-01

Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

Perceived racial, sexual identity, and homeless status-related discrimination among Black adolescents and young adults experiencing homelessness: Relations with depressive symptoms and suicidality.

PubMed

Gattis, Maurice N; Larson, Andrea

2016-01-01

There is a dearth of empirical evidence that addresses how racial minority, sexual minority, and homeless statuses, with their accompanying experiences of stigma and discrimination, are related to mental health in adolescent and young adult populations. The current study addresses this gap by examining the associations between multiple forms of discrimination, depressive symptoms, and suicidality in a sample of 89 Black adolescents and young adults (52% female; 47% nonheterosexual, ages 16-24) experiencing homelessness. Results from a series of ordinary least squares and logistic regressions suggested that perceived homelessness stigma and racial discrimination were associated with higher levels of depressive symptoms, controlling for gender, age, and other types of discrimination, while perceived sexual identity discrimination showed no association. Having ever spent a homeless night on the street, an indicator of homelessness severity, accounted for a substantial amount of the association between homelessness stigma and depressive symptoms. In contrast, suicidality was not significantly associated with any measure of discrimination, homelessness severity, or personal characteristics. We also found no indication that the associations between perceived discrimination targeted at racial and homelessness statuses and mental health differed by sexual minority status. Our results suggest that depressive symptoms and suicidality are prevalent among Black homeless youth, and that depressive symptoms are particularly associated with racial discrimination and indicators of homelessness. The roles of discrimination and a lack of safe housing may be taken into account when designing programs and policies that address the mental health of Black adolescents and young adults experiencing homelessness. (c) 2016 APA, all rights reserved).

A discriminative structural similarity measure and its application to video-volume registration for endoscope three-dimensional motion tracking.

PubMed

Luo, Xiongbiao; Mori, Kensaku

2014-06-01

Endoscope 3-D motion tracking, which seeks to synchronize pre- and intra-operative images in endoscopic interventions, is usually performed as video-volume registration that optimizes the similarity between endoscopic video and pre-operative images. The tracking performance, in turn, depends significantly on whether a similarity measure can successfully characterize the difference between video sequences and volume rendering images driven by pre-operative images. The paper proposes a discriminative structural similarity measure, which uses the degradation of structural information and takes image correlation or structure, luminance, and contrast into consideration, to boost video-volume registration. By applying the proposed similarity measure to endoscope tracking, it was demonstrated to be more accurate and robust than several available similarity measures, e.g., local normalized cross correlation, normalized mutual information, modified mean square error, or normalized sum squared difference. Based on clinical data evaluation, the tracking error was reduced significantly from at least 14.6 mm to 4.5 mm. The processing time was accelerated more than 30 frames per second using graphics processing unit.

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J. (Principal Investigator)

1982-01-01

Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7.

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.

Taxonomic and numerical sufficiency in a Lower and Middle Miocene molluscan metacommunity of the Central Paratethys

NASA Astrophysics Data System (ADS)

Zuschin, Martin; Nawrot, Rafal; Harzhauser, Mathias; Mandic, Oleg

2015-04-01

Among the most important questions in quantitative palaeoecology is how taxonomic and numerical resolution affect the analysis of community and metacommunity patterns. A species-abundance data set (10 localities, 213 bulk samples, 478 species, > 49,000 shells) from Burdigalian, Langhian and Serravallian benthic marine molluscan assemblages of the Central Paratethys was studied for this purpose. Assemblages are from two nearshore habitats (estuarine and marine intertidal) and three subtidal habitats (estuarine, fully marine sandy, and fully marine pelitic), which represent four biozones and four 3rd order depositional sequences over more than three million years, and are developed along the same depth-related environmental gradient. Double-standardized data subsampled to 19 samples per habitat, each with a minimum of 50 specimens, were used to calculate R²-values from PERMANOVA as a measure of differences between habitats at three taxonomic levels (species, genera and families) and at five levels of data transformation (raw abundances, percentages, square-root transformed percentages, fourth-root transformed percentages, presence-absence data). Species discriminate better between habitats than genera and families, but the differences between taxonomic levels are much stronger in the subtidal, where genera and families have more species than than in the intertidal. When all habitats are compared percentages and square-root transformed percentages discriminate equally well and perform better than higher levels of data transformation. Among nearshore and among subtidal habitats, however, the ability to discriminate between habitats increases with the level of data transformation (i.e., it is best for fourth-root transformed percentages and presence-absence data). The impact of decreasing taxonomic resolution is of minor importance in nearshore habitats, which are characterized by similar assemblages showing strong dominance of few widely distributed species, and many families represented by only one species (77.9%). Consequently, the differentiation between nearshore habitats is much weaker compared to subtidal assemblages. The latter are characterized by more distinct, relatively even assemblages with comparatively few families represented by only one species (64.2%) and many rare taxa, whose importance is emphasized by higher levels of data transformation.

Qualitative and quantitative prediction of volatile compounds from initial amino acid profiles in Korean rice wine (makgeolli) model.

PubMed

Kang, Bo-Sik; Lee, Jang-Eun; Park, Hyun-Jin

2014-06-01

In Korean rice wine (makgeolli) model, we tried to develop a prediction model capable of eliciting a quantitative relationship between initial amino acids in makgeolli mash and major aromatic compounds, such as fusel alcohols, their acetate esters, and ethyl esters of fatty acids, in makgeolli brewed. Mass-spectrometry-based electronic nose (MS-EN) was used to qualitatively discriminate between makgeollis made from makgeolli mashes with different amino acid compositions. Following this measurement, headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (GC-MS) combined with partial least-squares regression (PLSR) method was employed to quantitatively correlate amino acid composition of makgeolli mash with major aromatic compounds evolved during makgeolli fermentation. In qualitative prediction with MS-EN analysis, the makgeollis were well discriminated according to the volatile compounds derived from amino acids of makgeolli mash. Twenty-seven ion fragments with mass-to-charge ratio (m/z) of 55 to 98 amu were responsible for the discrimination. In GC-MS combined with PLSR method, a quantitative approach between the initial amino acids of makgeolli mash and the fusel compounds of makgeolli demonstrated that coefficient of determination (R(2)) of most of the fusel compounds ranged from 0.77 to 0.94 in good correlation, except for 2-phenylethanol (R(2) = 0.21), whereas R(2) for ethyl esters of MCFAs including ethyl caproate, ethyl caprylate, and ethyl caprate was 0.17 to 0.40 in poor correlation. The amino acids have been known to affect the aroma in alcoholic beverages. In this study, we demonstrated that an electronic nose qualitatively differentiated Korean rice wines (makgeollis) by their volatile compounds evolved from amino acids with rapidity and reproducibility and successively, a quantitative correlation with acceptable R2 between amino acids and fusel compounds could be established via HS-SPME GC-MS combined with partial least-squares regression. Our approach for predicting the quantities of volatile compounds in the finished product from initial condition of fermentation will give an insight to food researchers to modify and optimize the qualities of the corresponding products. © 2014 Institute of Food Technologists®

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

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.

Cross-validating a bidimensional mathematics anxiety scale.

PubMed

Haiyan Bai

2011-03-01

The psychometric properties of a 14-item bidimensional Mathematics Anxiety Scale-Revised (MAS-R) were empirically cross-validated with two independent samples consisting of 647 secondary school students. An exploratory factor analysis on the scale yielded strong construct validity with a clear two-factor structure. The results from a confirmatory factor analysis indicated an excellent model-fit (χ(2) = 98.32, df = 62; normed fit index = .92, comparative fit index = .97; root mean square error of approximation = .04). The internal consistency (.85), test-retest reliability (.71), interfactor correlation (.26, p < .001), and positive discrimination power indicated that MAS-R is a psychometrically reliable and valid instrument for measuring mathematics anxiety. Math anxiety, as measured by MAS-R, correlated negatively with student achievement scores (r = -.38), suggesting that MAS-R may be a useful tool for classroom teachers and other educational personnel tasked with identifying students at risk of reduced math achievement because of anxiety.

Distinction Between Recurrent Glioma and Radiation Injury Using Magnetic Resonance Spectroscopy in Combination With Diffusion-Weighted Imaging

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

Zeng, Q.-S.; Li, C.-F.; Liu Hong

2007-05-01

Purpose: The aim of this study was to explore the diagnostic effectiveness of magnetic resonance (MR) spectroscopy with diffusion-weighted imaging on the evaluation of the recurrent contrast-enhancing areas at the site of treated gliomas. Methods and Materials: In 55 patients who had new contrast-enhancing lesions in the vicinity of the previously resected and irradiated high-grade gliomas, two-dimensional MR spectroscopy and diffusion-weighted imaging were performed. Spectral data for N-acetylaspartate (NAA), choline (Cho), creatine (Cr), lipid (Lip), and lactate (Lac) were analyzed in conjunction with the apparent diffusion coefficient (ADC) in all patients. Diagnosis of these lesions was assigned by means ofmore » follow-up or histopathology. Results: The Cho/NAA and Cho/Cr ratios were significantly higher in recurrent tumor than in regions of radiation injury (p < 0.01). The ADC value and ADC ratios (ADC of contrast-enhancing lesion to matching structure in the contralateral hemisphere) were significantly higher in radiation injury regions than in recurrent tumor (p < 0.01). With MR spectroscopic data, two variables (Cho/NAA and Cho/Cr ratios) were shown to differentiate recurrent glioma from radiation injury, and 85.5% of total subjects were correctly classified into groups. However, with discriminant analysis of MR spectroscopy imaging plus diffusion-weighted imaging, three variables (Cho/NAA, Cho/Cr, and ADC ratio) were identified and 96.4% of total subjects were correctly classified. There was a significant difference between the diagnostic accuracy of the two discriminant analyses (Chi-square = 3.96, p = 0.046). Conclusion: Using discriminant analysis, this study found that MR spectroscopy in combination with ADC ratio, rather than ADC value, can improve the ability to differentiate recurrent glioma and radiation injury.« less

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.

Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface.

PubMed

Chang, G C; Kang, W J; Luh, J J; Cheng, C K; Lai, J S; Chen, J J; Kuo, T S

1996-10-01

The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications.

High Resolution Mass Spectrometric Analysis of Secoiridoids and Metabolites as Biomarkers of Acute Olive Oil Intake-An Approach to Study Interindividual Variability in Humans.

PubMed

Silva, Sandra; Garcia-Aloy, Mar; Figueira, Maria Eduardo; Combet, Emilie; Mullen, William; Bronze, Maria Rosário

2018-01-01

Phenolic compounds are minor components of extra virgin olive oil (EVOO). Secoiridoids are the major components contributing to the phenolic content of EVOO. Information is lacking regarding their potential as biomarkers for EVOO intake. Healthy volunteers (n = 9) ingested 50 mL of EVOO in a single dose containing 322 mg kg -1 total phenolic content (caffeic acid equivalents) and 6 mg 20 g -1 hydroxytyrosol and its derivatives. Plasma is collected before (0 h) and at 0.5, 1, 2, 4, and 6 h after ingestion. Urine samples are collected prior to ingestion (0 h) and at 0-4, 4-8, 8-15, and 15-24 h. Samples are analyzed by UPLC coupled with an Exactive Orbitrap MS. Partial least squares discriminant analysis with orthogonal signal correction is applied to screen for metabolites that allow sample discrimination. Plasma biomarkers and urine biomarkers are selected although individual variability is observed among volunteers. Results are in accordance with in vitro experiments performed (in vitro digestion and hepatic microsomal activity assays). Plasma (elenolic acid + H 2 ; p-HPEA-EA + H 2 + glucuronide) and urinary (3,4-DHPEA-EA, 3,4-DHPEA-EA + H 2 +glucuronide, methyl 3,4-DHPEA-EA + H 2 +glucuronide) secoiridoid compounds are selected as biomarkers to monitor EVOO intake showing good predictive ability according to multivariate analysis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Metabolic Signature of Remote Ischemic Preconditioning Involving a Cocktail of Amino Acids and Biogenic Amines.

PubMed

Chao de la Barca, Juan Manuel; Bakhta, Oussama; Kalakech, Hussein; Simard, Gilles; Tamareille, Sophie; Catros, Véronique; Callebert, Jacques; Gadras, Cédric; Tessier, Lydie; Reynier, Pascal; Prunier, Fabrice; Mirebeau-Prunier, Delphine

2016-09-24

Remote ischemic preconditioning (RIPC) is an attractive therapeutic procedure for protecting the heart against ischemia/reperfusion injury. Despite evidence of humoral mediators transported through the circulation playing a critical role, their actual identities so far remain unknown. We sought to identify plasmatic RIPC-induced metabolites that may play a role. Rat plasma samples from RIPC and control groups were analyzed using a targeted metabolomic approach aimed at measuring 188 metabolites. Principal component analysis and orthogonal partial least-squares discriminant analysis were used to identify the metabolites that discriminated between groups. Plasma samples from 50 patients subjected to RIPC were secondarily explored to confirm the results obtained in rats. Finally, a combination of the metabolites that were significantly increased in both rat and human plasma was injected prior to myocardial ischemia/reperfusion in rats. In the rat samples, 124 molecules were accurately quantified. Six metabolites (ornithine, glycine, kynurenine, spermine, carnosine, and serotonin) were the most significant variables for marked differentiation between the RIPC and control groups. In human plasma, analysis confirmed ornithine decrease and kynurenine and glycine increase following RIPC. Injection of the glycine and kynurenine alone or in combination replicated the protective effects of RIPC seen in rats. We have hereby reported significant variations in a cocktail of amino acids and biogenic amines after remote ischemic preconditioning in both rat and human plasma. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01390129. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Investigation of quartz grain surface textures by atomic force microscopy for forensic analysis.

PubMed

Konopinski, D I; Hudziak, S; Morgan, R M; Bull, P A; Kenyon, A J

2012-11-30

This paper presents a study of quartz sand grain surface textures using atomic force microscopy (AFM) to image the surface. Until now scanning electron microscopy (SEM) has provided the primary technique used in the forensic surface texture analysis of quartz sand grains as a means of establishing the provenance of the grains for forensic reconstructions. The ability to independently corroborate the grain type classifications is desirable and provides additional weight to the findings of SEM analysis of the textures of quartz grains identified in forensic soil/sediment samples. AFM offers a quantitative means of analysis that complements SEM examination, and is a non-destructive technique that requires no sample preparation prior to scanning. It therefore has great potential to be used for forensic analysis where sample preservation is highly valuable. By taking quantitative topography scans, it is possible to produce 3D representations of microscopic surface textures and diagnostic features for examination. Furthermore, various empirical measures can be obtained from analysing the topography scans, including arithmetic average roughness, root-mean-square surface roughness, skewness, kurtosis, and multiple gaussian fits to height distributions. These empirical measures, combined with qualitative examination of the surfaces can help to discriminate between grain types and provide independent analysis that can corroborate the morphological grain typing based on the surface textures assigned using SEM. Furthermore, the findings from this study also demonstrate that quartz sand grain surfaces exhibit a statistically self-similar fractal nature that remains unchanged across scales. This indicates the potential for a further quantitative measure that could be utilised in the discrimination of quartz grains based on their provenance for forensic investigations. Copyright © 2012 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…

6,7-dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid attenuates heptatocellular carcinoma in rats with NMR-based metabolic perturbations

PubMed Central

Kumar, Pranesh; Singh, Ashok K; Raj, Vinit; Rai, Amit; Maity, Siddhartha; Rawat, Atul; Kumar, Umesh; Kumar, Dinesh; Prakash, Anand; Guleria, Anupam; Saha, Sudipta

2017-01-01

Aim: 6,7-dimethoxy-1,2,3,4-tetrahydro-isoquinoline-3-carboxylic acid (M1) was synthesized and evaluated for in-vivo antiproliferative action in diethylnitrosamine-induced hepatocarcinogenic rats. Materials & methods: The antiproliferative effect of M1 was assessed by various biochemical parameters, histopathology of liver and HPLC analysis. Proton nuclear magnetic resonance-based serum metabolic study was implemented on rat sera to explore the effects of M1 on hepatocellular carcinoma-induced metabolic alterations. Results: M1 showed protective action on liver and restored the arrangement of liver tissues in normal proportion. HPLC analysis displayed a good plasma drug concentration after its oral administration. Score plots of partial least squares discriminate analysis models exhibited that M1 therapy ameliorated hepatocellular carcinoma-induced metabolic alterations which signified its antiproliferative potential. Conclusion: M1 manifested notable antiproliferative profile, and warrants further investigation for future anticancer therapy. PMID:28884001

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.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

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.

Use of valence band Auger electron spectroscopy to study thin film growth: oxide and diamond-like carbon films

NASA Astrophysics Data System (ADS)

Steffen, H. J.

1994-12-01

It is demonstrated how Auger line shape analysis with factor analysis (FA), least-squares fitting and even simple peak height measurements may provide detailed information about the composition, different chemical states and also defect concentration or crystal order. Advantage is taken of the capability of Auger electron spectroscopy to give valence band structure information with high surface sensitivity and the special aspect of FA to identify and discriminate quantitatively unknown chemical species. Valence band spectra obtained from Ni, Fe, Cr and NiFe40Cr20 during oxygen exposure at room temperature reveal the oxidation process in the initial stage of the thin layer formation. Furthermore, the carbon chemical states that were formed during low energy C(+) and Ne(+) ion irradiation of graphite are delineated and the evolution of an amorphous network with sp3 bonds is disclosed. The analysis represents a unique method to quantify the fraction of sp3-hybridized carbon in diamond-like materials.

Distinguishing autocrine and paracrine signals in hematopoietic stem cell culture using a biofunctional microcavity platform

NASA Astrophysics Data System (ADS)

Müller, Eike; Wang, Weijia; Qiao, Wenlian; Bornhäuser, Martin; Zandstra, Peter W.; Werner, Carsten; Pompe, Tilo

2016-08-01

Homeostasis of hematopoietic stem cells (HSC) in the mammalian bone marrow stem cell niche is regulated by signals of the local microenvironment. Besides juxtacrine, endocrine and metabolic cues, paracrine and autocrine signals are involved in controlling quiescence, proliferation and differentiation of HSC with strong implications on expansion and differentiation ex vivo as well as in vivo transplantation. Towards this aim, a cell culture analysis on a polymer microcavity carrier platform was combined with a partial least square analysis of a mechanistic model of cell proliferation. We could demonstrate the discrimination of specific autocrine and paracrine signals from soluble factors as stimulating and inhibitory effectors in hematopoietic stem and progenitor cell culture. From that we hypothesize autocrine signals to be predominantly involved in maintaining the quiescent state of HSC in single-cell niches and advocate our analysis platform as an unprecedented option for untangling convoluted signaling mechanisms in complex cell systems being it of juxtacrine, paracrine or autocrine origin.

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

Shotgun metabolomic approach based on mass spectrometry for hepatic mitochondria of mice under arsenic exposure.

PubMed

García-Sevillano, M A; García-Barrera, T; Navarro, F; Montero-Lobato, Z; Gómez-Ariza, J L

2015-04-01

Mass spectrometry (MS)-based toxicometabolomics requires analytical approaches for obtaining unbiased metabolic profiles. The present work explores the general application of direct infusion MS using a high mass resolution analyzer (a hybrid systems triple quadrupole-time-of-flight) and a complementary gas chromatography-MS analysis to mitochondria extracts from mouse hepatic cells, emphasizing on mitochondria isolation from hepatic cells with a commercial kit, sample treatment after cell lysis, comprehensive metabolomic analysis and pattern recognition from metabolic profiles. Finally, the metabolomic platform was successfully checked on a case-study based on the exposure experiment of mice Mus musculus to inorganic arsenic during 12 days. Endogenous metabolites alterations were recognized by partial least squares-discriminant analysis. Subsequently, metabolites were identified by combining MS/MS analysis and metabolomics databases. This work reports for the first time the effects of As-exposure on hepatic mitochondria metabolic pathways based on MS, and reveals disturbances in Krebs cycle, β-oxidation pathway, amino acids degradation and perturbations in creatine levels. This non-target analysis provides extensive metabolic information from mitochondrial organelle, which could be applied to toxicology, pharmacology and clinical studies.

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…

GC-TOF/MS-based metabolomic profiling of estrogen deficiency-induced obesity in ovariectomized rats

PubMed Central

Ma, Bo; Zhang, Qi; Wang, Guang-ji; A, Ji-ye; Wu, Di; Liu, Ying; Cao, Bei; Liu, Lin-sheng; Hu, Ying-ying; Wang, Yong-lu; Zheng, Ya-ya

2011-01-01

Aim: To explore the alteration of endogenous metabolites and identify potential biomarkers using metabolomic profiling with gas chromatography coupled a time-of-flight mass analyzer (GC/TOF-MS) in a rat model of estrogen-deficiency-induced obesity. Methods: Twelve female Sprague-Dawley rats six month of age were either sham-operated or ovariectomized (OVX). Rat blood was collected, and serum was analyzed for biomarkers using standard colorimetric methods with commercial assay kits and a metabolomic approach with GC/TOF-MS. The data were analyzed using multivariate statistical techniques. Results: A high body weight and body mass index inversely correlated with serum estradiol (E2) in the OVX rats compared to the sham rats. Estrogen deficiency also significantly increased serum total cholesterol, triglycerides, and low-density lipoprotein cholesterol. Utilizing GC/TOF-MS-based metabolomic analysis and the partial least-squares discriminant analysis, the OVX samples were discriminated from the shams. Elevated levels of cholesterol, glycerol, glucose, arachidonic acid, glutamic acid, glycine, and cystine and reduced alanine levels were observed. Serum glucose metabolism, energy metabolism, lipid metabolism, and amino acid metabolism were involved in estrogen-deficiency-induced obesity in OVX rats. Conclusion: The series of potential biomarkers identified in the present study provided fingerprints of rat metabolomic changes during obesity and an overview of multiple metabolic pathways during the progression of obesity involving glucose metabolism, lipid metabolism, and amino acid metabolism. PMID:21293480

Changes in extra-virgin olive oil added with Lycium barbarum L. carotenoids during frying: Chemical analyses and metabolomic approach.

PubMed

Blasi, F; Rocchetti, G; Montesano, D; Lucini, L; Chiodelli, G; Ghisoni, S; Baccolo, G; Simonetti, M S; Cossignani, L

2018-03-01

In this work, an Italian extra-virgin olive oil (EVOO) sample and the same sample added with a carotenoid-rich nutraceutical extract from Lycium barbarum L. (EVOOCar) were subjected to a frying process to comparatively assess chemical and physical changes and heat stability. Oxidation progress was monitored by measuring oil quality changes such as peroxide value, free acidity, K232, K268, and fatty acid composition as well as minor compound content, phenols, α-tocopherol, and carotenoids. An UHPLC/QTOF-MS metabolomics approach discriminated the two oil samples based on their chemical changes during frying, identifying also the phenolic classes most exposed to statistically significant variations. Partial least square discriminant analysis and volcano analysis were applied together to identify the most significant markers allowing group separation. The decrease in total phenolic content was lower in EVOOCar than in EVOO during frying. Monounsaturated and polyunsaturated fatty acids showed a significant percentage loss, 3.7% and 17.2%, respectively, in EVOO after 180min frying at 180°C, while they remained constant or slightly changed in EVOOCar. Zeaxanthin added to the oil rapidly decreased during the frying process. These findings showed that the addition of a carotenoid extract from L. barbarum can help to improve the oxidative stability of extra-virgin olive oil. Copyright © 2017 Elsevier Ltd. All rights reserved.

Diagnosis of colorectal cancer by near-infrared optical fiber spectroscopy and random forest

NASA Astrophysics Data System (ADS)

Chen, Hui; Lin, Zan; Wu, Hegang; Wang, Li; Wu, Tong; Tan, Chao

2015-01-01

Near-infrared (NIR) spectroscopy has such advantages as being noninvasive, fast, relatively inexpensive, and no risk of ionizing radiation. Differences in the NIR signals can reflect many physiological changes, which are in turn associated with such factors as vascularization, cellularity, oxygen consumption, or remodeling. NIR spectral differences between colorectal cancer and healthy tissues were investigated. A Fourier transform NIR spectroscopy instrument equipped with a fiber-optic probe was used to mimic in situ clinical measurements. A total of 186 spectra were collected and then underwent the preprocessing of standard normalize variate (SNV) for removing unwanted background variances. All the specimen and spots used for spectral collection were confirmed staining and examination by an experienced pathologist so as to ensure the representative of the pathology. Principal component analysis (PCA) was used to uncover the possible clustering. Several methods including random forest (RF), partial least squares-discriminant analysis (PLSDA), K-nearest neighbor and classification and regression tree (CART) were used to extract spectral features and to construct the diagnostic models. By comparison, it reveals that, even if no obvious difference of misclassified ratio (MCR) was observed between these models, RF is preferable since it is quicker, more convenient and insensitive to over-fitting. The results indicate that NIR spectroscopy coupled with RF model can serve as a potential tool for discriminating the colorectal cancer tissues from normal ones.

Comparison study of the volatile profiles and microbial communities of Wuyi Qu and Gutian Qu, two major types of traditional fermentation starters of Hong Qu glutinous rice wine.

PubMed

Liu, Zhibin; Wang, Zhiyao; Lv, Xucong; Zhu, Xiaoping; Chen, Liling; Ni, Li

2018-02-01

Hong Qu, which mainly contains Monascus sp. and other microorganisms, as well as numerous microbial metabolites, is used as the fermentation starter of Hong Qu glutinous rice wine, a traditional alcoholic beverage. Two widely-used types of Hong Qu, namely Wuyi Qu (WYQ) and Gutian Qu (GTQ), were thoroughly compared for their fermentation properties, volatile profiles, and microbiota structures in this study. Significantly higher color value, glucoamylase and α-amylase activities were discovered in WYQ. And substantial variation in volatile components and microbial communities were also observed between them. It was identified that bacterial genus Burkholderia dominated GTQ (71.62%) and Bacillus dominated WYQ (44.73%), while Monascus purpureus was the most abundant fungal species in both types of starters (76.99%). In addition, 213 bacterial genera and 150 fungal species with low-abundance were also detected. Since the Linear Discriminant Analysis Effect Size algorithm, 14 genus-level bacterial taxa and 10 species-level fungal taxa could be utilized to distinguish these two types of starters. Moreover, the potential correlation of the volatile components and microbiota within WYQ and GTQ were further analyzed, by utilizing Partial Least Squares Discriminant Analysis. Ultimately, this study provides detailed insight into the volatile profiles and microbial communities presented in Hong Qu. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigation of the composition of anabolic tablets using near infrared spectroscopy and Raman chemical imaging.

PubMed

Rebiere, Hervé; Ghyselinck, Céline; Lempereur, Laurent; Brenier, Charlotte

2016-01-01

The use of performance enhancing drugs is a widespread phenomenon in professional and leisure sports. A spectroscopic study was carried out on anabolic tablets labelled as 5 mg methandienone tablets provided by police departments. The analytical approach was based on a two-step methodology: a fast analysis of tablets using near infrared (NIR) spectroscopy to assess sample homogeneity based on their global composition, followed by Raman chemical imaging of one sample per NIR profile to obtain information on sample formulation. NIR spectroscopy assisted by a principal components analysis (PCA) enabled fast discrimination of different profiles based on the excipient formulation. Raman hyperspectral imaging and multivariate curve resolution - alternating least square (MCR-ALS) provided chemical images of the distribution of the active substance and excipients within tablets and facilitated identification of the active compounds. The combination of NIR spectroscopy and Raman chemical imaging highlighted dose-to-dose variations and succeeded in the discrimination of four different formulations out of eight similar samples of anabolic tablets. Some samples contained either methandienone or methyltestosterone whereas one sample did not contain an active substance. Other ingredients were sucrose, lactose, starch or talc. Both techniques were fast and non-destructive and therefore can be carried out as exploratory methods prior to destructive screening methods. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Reliability and validity of the workplace harassment questionnaire for Korean finance and service workers.

PubMed

Lee, Myeongjun; Kim, Hyunjung; Shin, Donghee; Lee, Sangyun

2016-01-01

Harassment means systemic and repeated unethical acts. Research on workplace harassment have been conducted widely and the NAQ-R has been widely used for the researches. But this tool, however the limitations in revealing differended in sub-factors depending on the culture and in reflecting that unique characteristics of the Koren society. So, The workplace harassment questionnaire for Korean finace and service workers has been developed to assess the level of personal harassment at work. This study aims to develop a tool to assess the level of personal harassment at work and to test its validity and reliability while examining specific characteristics of workplace harassment against finance and service workers in Korea. The framework of survey was established based on literature review, focused-group interview for the Korean finance and service workers. To verify its reliability, Cronbach's alpha coefficient was calculated; and to verify its validity, items and factors of the tool were analyzed. The correlation matrix analysis was examined to verify the tool's convergent validity and discriminant validity. Structural validity was verified by checking statistical significance in relation to the BDI-K. Cronbach's alpha coefficient of this survey was 0.93, which indicates a quite high level of reliability. To verify the appropriateness of this survey tool, its construct validity was examined through factor analysis. As a result of the factor analysis, 3 factors were extracted, explaining 56.5 % of the total variance. The loading values and communalities of the 20 items were 0.85 to 0.48 and 0.71 to 0.46. The convergent validity and discriminant validity were analyzed and rate of item discriminant validity was 100 %. Finally, for the concurrent validity, We examined the relationship between the WHI-KFSW and pschosocial stress by examining the correlation with the BDI-K. The results of chi-square test and multiple logistic analysis indicated that the correlation with the BDI-K was satatisctically significant. Workplace harassment in actual workplaces were investigated based on interviews, and the statistical analysis contributed to systematizing the types of actual workplace harassment. By statistical method, we developed the questionare, 20 items of 3 categories.

NanoESI-MS-based lipidomics to discriminate between cultivars, cultivation ages, and parts of Panax ginseng.

PubMed

Kim, So-Hyun; Shin, Yoo-Soo; Choi, Hyung-Kyoon

2016-03-01

Korean ginseng (Panax ginseng C.A. Meyer) is one of the most popular medicinal herbs used in Asia, including Korea and China. In the present study lipid profiling of two officially registered cultivars (P. ginseng 'Chunpoong' and P. ginseng 'Yunpoong') was performed at different cultivation ages (5 and 6 years) and on different parts (tap roots, lateral roots, and rhizomes) using nano-electrospray ionization-mass spectrometry (nanoESI-MS). In total, 30 compounds including galactolipids, phospholipids, triacylglycerols, and ginsenosides were identified. Among them, triacylglycerol 54:6 (18:2/18:2/18:2), phosphatidylglycerol 34:3 (16:0/18:3), monogalactosyldiacylglycerol 36:4 (18:2/18:2), phosphatidic acid species 36:4 (18:2/18:2), and 34:1 (16:0/18:1) were selected as biomarkers to discriminate cultivars, cultivation ages, and parts. In addition, an unknown P. ginseng sample was successfully predicted by applying validated partial least squares projection to latent structures regression models. This is the first study regarding the identification of intact lipid species from P. ginseng and to predict cultivars, cultivation ages, and parts of P. ginseng using nanoESI-MS-based lipidomic profiling with a multivariate statistical analysis.

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.

Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations.

PubMed

Hassebo, Yasser Y; Gross, Barry; Oo, Min; Moshary, Fred; Ahmed, Samir

2006-08-01

The impact and potential of a polarization-selection technique to reduce the sky background signal for linearly polarized monostatic elastic backscatter lidar measurements are examined. Taking advantage of naturally occurring polarization properties in scattered skylight, we devised a polarization-discrimination technique in which both the lidar transmitter and the receiver track and minimize detected sky background noise while maintaining maximum lidar signal throughput. Lidar elastic backscatter measurements, carried out continuously during daylight hours at 532 nm, show as much as a factor of square root 10 improvement in the signal-to-noise ratio (SNR) over conventional unpolarized schemes. For vertically pointing lidars, the largest improvements are limited to the early morning and late afternoon hours, while for lidars scanning azimuthally and in elevation at angles other than vertical, significant improvements are achievable over more extended time periods with the specific times and improvement factors depending on the specific angle between the lidar and the solar axes. The resulting diurnal variations in SNR improvement sometimes show an asymmetry with the solar angle that analysis indicates can be attributed to changes in observed relative humidity that modifies the underlying aerosol microphysics and observed optical depth.

Inferring diffusion in single live cells at the single-molecule level

PubMed Central

Robson, Alex; Burrage, Kevin; Leake, Mark C.

2013-01-01

The movement of molecules inside living cells is a fundamental feature of biological processes. The ability to both observe and analyse the details of molecular diffusion in vivo at the single-molecule and single-cell level can add significant insight into understanding molecular architectures of diffusing molecules and the nanoscale environment in which the molecules diffuse. The tool of choice for monitoring dynamic molecular localization in live cells is fluorescence microscopy, especially so combining total internal reflection fluorescence with the use of fluorescent protein (FP) reporters in offering exceptional imaging contrast for dynamic processes in the cell membrane under relatively physiological conditions compared with competing single-molecule techniques. There exist several different complex modes of diffusion, and discriminating these from each other is challenging at the molecular level owing to underlying stochastic behaviour. Analysis is traditionally performed using mean square displacements of tracked particles; however, this generally requires more data points than is typical for single FP tracks owing to photophysical instability. Presented here is a novel approach allowing robust Bayesian ranking of diffusion processes to discriminate multiple complex modes probabilistically. It is a computational approach that biologists can use to understand single-molecule features in live cells. PMID:23267182

Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations

NASA Astrophysics Data System (ADS)

Hassebo, Yasser Y.; Gross, Barry; Oo, Min; Moshary, Fred; Ahmed, Samir

2006-08-01

The impact and potential of a polarization-selection technique to reduce the sky background signal for linearly polarized monostatic elastic backscatter lidar measurements are examined. Taking advantage of naturally occurring polarization properties in scattered skylight, we devised a polarization-discrimination technique in which both the lidar transmitter and the receiver track and minimize detected sky background noise while maintaining maximum lidar signal throughput. Lidar elastic backscatter measurements, carried out continuously during daylight hours at 532 nm, show as much as a factor of square root 10 improvement in the signal-to-noise ratio (SNR) over conventional unpolarized schemes. For vertically pointing lidars, the largest improvements are limited to the early morning and late afternoon hours, while for lidars scanning azimuthally and in elevation at angles other than vertical, significant improvements are achievable over more extended time periods with the specific times and improvement factors depending on the specific angle between the lidar and the solar axes. The resulting diurnal variations in SNR improvement sometimes show an asymmetry with the solar angle that analysis indicates can be attributed to changes in observed relative humidity that modifies the underlying aerosol microphysics and observed optical depth.

Raman spectroscopic analysis of gunshot residue offering great potential for caliber differentiation.

PubMed

Bueno, Justin; Sikirzhytski, Vitali; Lednev, Igor K

2012-05-15

Near-infrared (NIR) Raman microspectroscopy combined with advanced statistics was used to differentiate gunshot residue (GSR) particles originating from different caliber ammunition. The firearm discharge process is analogous to a complex chemical reaction. The reagents of this process are represented by the chemical composition of the ammunition, firearm, and cartridge case. The specific firearm parameters determine the conditions of the reaction and thus the subsequent product, GSR. We found that Raman spectra collected from these products are characteristic for different caliber ammunition. GSR particles from 9 mm and 0.38 caliber ammunition, collected under identical discharge conditions, were used to demonstrate the capability of confocal Raman microspectroscopy for the discrimination and identification of GSR particles. The caliber differentiation algorithm is based on support vector machines (SVM) and partial least squares (PLS) discriminant analyses, validated by a leave-one-out cross-validation method. This study demonstrates for the first time that NIR Raman microspectroscopy has the potential for the reagentless differentiation of GSR based upon forensically relevant parameters, such as caliber size. When fully developed, this method should have a significant impact on the efficiency of crime scene investigations.

Deep and shallow water effects on developing preschoolers' aquatic skills.

PubMed

Costa, Aldo M; Marinho, Daniel A; Rocha, Helena; Silva, António J; Barbosa, Tiago M; Ferreira, Sandra S; Martins, Marta

2012-05-01

The aim of the study was to assess deep and shallow water teaching methods in swimming lessons for preschool children and identify variations in the basic aquatic skills acquired. The study sample included 32 swimming instructors (16 from deep water programs and 16 from shallow water programs) and 98 preschool children (50 from deep water swimming pool and 48 from shallow water swimming pool). The children were also studied regarding their previous experience in swimming (6, 12 and 18 months or practice). Chi-Square test and Fisher's exact test were used to compare the teaching methodology. A discriminant analysis was conducted with Λ wilk's method to predict under what conditions students are better or worse (aquatic competence). Results suggest that regardless of the non-significant variations found in teaching methods, the water depth can affect aquatic skill acquisition - shallow water lessons seem to impose greater water competence particularly after 6 months of practice. The discriminant function revealed a significant association between groups and all predictors for 6 months of swimming practice (p<0.001). Body position in gliding and leg displacements were the main predictors. For 12 and 18 months of practice, the discriminant function do not revealed any significant association between groups. As a conclusion, it seems that the teaching methodology of aquatic readiness based on deep and shallow water programs for preschoolers is not significantly different. However, shallow water lessons could be preferable for the development of basic aquatic skills.

Magnetoencephalographic responses to illusory figures: early evoked gamma is affected by processing of stimulus features.

PubMed

Herrmann, C S; Mecklinger, A

2000-12-01

We examined evoked and induced responses in event-related fields and gamma activity in the magnetoencephalogram (MEG) during a visual classification task. The objective was to investigate the effects of target classification and the different levels of discrimination between certain stimulus features. We performed two experiments, which differed only in the subjects' task while the stimuli were identical. In Experiment 1, subjects responded by a button-press to rare Kanizsa squares (targets) among Kanizsa triangles and non-Kanizsa figures (standards). This task requires the processing of both stimulus features (colinearity and number of inducer disks). In Experiment 2, the four stimuli of Experiment 1 were used as standards and the occurrence of an additional stimulus without any feature overlap with the Kanizsa stimuli (a rare and highly salient red fixation cross) had to be detected. Discrimination of colinearity and number of inducer disks was not necessarily required for task performance. We applied a wavelet-based time-frequency analysis to the data and calculated topographical maps of the 40 Hz activity. The early evoked gamma activity (100-200 ms) in Experiment 1 was higher for targets as compared to standards. In Experiment 2, no significant differences were found in the gamma responses to the Kanizsa figures and non-Kanizsa figures. This pattern of results suggests that early evoked gamma activity in response to visual stimuli is affected by the targetness of a stimulus and the need to discriminate between the features of a stimulus.

A Comparative Metabolomic Evaluation of Behcet’s Disease with Arthritis and Seronegative Arthritis Using Synovial Fluid

PubMed Central

Kim, Jungyeon; Hwang, Jiwon; Kim, Kyoung Heon; Cha, Hoon-Suk

2015-01-01

Behcet’s disease (BD) with arthritis is often confused with seronegative arthritis (SNA) because of shared clinical symptoms and the lack of definitive biomarkers for BD. To investigate possible metabolic patterns and potential biomarkers of BD with arthritis, metabolomic profiling of synovial fluid (SF) from 6 patients with BD with arthritis and 18 patients with SNA was performed using gas chromatography/time-of-flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. A total of 123 metabolites were identified from samples. Orthogonal partial least square-discriminant analysis showed clear discrimination between BD with arthritis and SNA. A set of 11 metabolites were identified as potential biomarkers for BD using variable importance for projection values and the Wilcoxon-Mann-Whitney test. Compared with SNA, BD with arthritis exhibited relatively high levels of glutamate, valine, citramalate, leucine, methionine sulfoxide, glycerate, phosphate, lysine, isoleucine, urea, and citrulline. There were two markers identified, elevated methionine sulfoxide and citrulline, that were associated with increased oxidative stress, providing a potential link to BD-associated neutrophil hyperactivity. Glutamate, citramalate, and valine were selected and validated as putative biomarkers for BD with arthritis (sensitivity, 100%; specificity, 61.1%). This is the first report to present potential biomarkers from SF for discriminating BD with arthritis from SNA. The metabolomics of SF may be helpful in searching for potential biomarkers and elucidating the clinicopathogenesis of BD with arthritis. PMID:26270538

An enzyme free electrochemical biosensor for sensitive detection of miRNA with a high discrimination factor by coupling the strand displacement reaction and catalytic hairpin assembly recycling.

PubMed

Yao, Juan; Zhang, Zhang; Deng, Zhenghua; Wang, Youqiang; Guo, Yongcan

2017-10-23

An isothermal, enzyme free, ultra-specific and ultra-sensitive protocol for electrochemical detection of miRNAs is proposed based on the toehold-mediated strand displacement reaction (SDR) and non-enzymatic catalytic hairpin reaction (CHA) recycling. The SDR was first triggered only in the presence of target miRNA and this process also affects other miRNA interferences having similar target sequences, thus guaranteeing a high discrimination factor and could be used in rare content miRNA detection with various amounts of interferences having similar target sequences. The output protector strand then triggered enzyme free CHA amplification and generates plenty of hairpin self-assembly products. This process in turn influences SDR equilibrium to move to the right and generates large amounts of protector output to ensure analysis sensitivity. Compared with traditional CHA, our proposed method greatly improved the signal to noise ratio and shows excellent performance in rare miRNA detection with miRNA analogue interference. Under the optimal experimental conditions and using square wave voltammetry, the established biosensor could detect target miRNA-21 down to 30 fM (S/N = 3) with a dynamic range from 100 fM to 2 nM, and discriminate rare target miRNA-21 from mismatched miRNA with high selectivity. This method holds great promise in miRNA detection from human cancer cell lines and would be a versatile and powerful tool for clinical molecular diagnostics.

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.

Deep and Shallow Water Effects on Developing Preschoolers’ Aquatic Skills

PubMed Central

Costa, Aldo M.; Marinho, Daniel A.; Rocha, Helena; Silva, António J.; Barbosa, Tiago M.; Ferreira, Sandra S.; Martins, Marta

2012-01-01

The aim of the study was to assess deep and shallow water teaching methods in swimming lessons for preschool children and identify variations in the basic aquatic skills acquired. The study sample included 32 swimming instructors (16 from deep water programs and 16 from shallow water programs) and 98 preschool children (50 from deep water swimming pool and 48 from shallow water swimming pool). The children were also studied regarding their previous experience in swimming (6, 12 and 18 months or practice). Chi-Square test and Fisher’s exact test were used to compare the teaching methodology. A discriminant analysis was conducted with Λ wilk’s method to predict under what conditions students are better or worse (aquatic competence). Results suggest that regardless of the non-significant variations found in teaching methods, the water depth can affect aquatic skill acquisition - shallow water lessons seem to impose greater water competence particularly after 6 months of practice. The discriminant function revealed a significant association between groups and all predictors for 6 months of swimming practice (p<0.001). Body position in gliding and leg displacements were the main predictors. For 12 and 18 months of practice, the discriminant function do not revealed any significant association between groups. As a conclusion, it seems that the teaching methodology of aquatic readiness based on deep and shallow water programs for preschoolers is not significantly different. However, shallow water lessons could be preferable for the development of basic aquatic skills. PMID:23487406

Cross-cultural adaptation and validation of the osteoporosis assessment questionnaire short version (OPAQ-SV) for Chinese osteoporotic fracture females.

PubMed

Zhang, Yin-Ping; Wei, Huan-Huan; Wang, Wen; Xia, Ru-Yi; Zhou, Xiao-Ling; Porr, Caroline; Lammi, Mikko

2016-04-01

The Osteoporosis Assessment Questionnaire Short Version (OPAQ-SV) was cross-culturally adapted to measure health-related quality of life in Chinese osteoporotic fracture females and then validated in China for its psychometric properties. Cross-cultural adaptation, including translation of the original OPAQ-SV into Mandarin Chinese language, was performed according to published guidelines. Validation of the newly cross-culturally adapted OPAQ-SV was conducted by sampling 234 Chinese osteoporotic fracture females and also a control group of 235 Chinese osteoporotic females without fractures, producing robust content, construct, and discriminant validation results. Major categories of reliability were also met: the Cronbach alpha coefficient was 0.975, indicating good internal consistency; the test-retest reliability was 0.80; and principal component analysis resulted in a 6-factor structure explaining 75.847 % of the total variance. Further, the Comparative Fit Index result was 0.922 following the modified model confirmatory factor analysis, and the chi-squared test was 1.98. The root mean squared error of approximation was 0.078. Moreover, significant differences were revealed between females with fractures and those without fractures across all domains (p < 0.001). Overall, the newly cross-culturally adapted OPAQ-SV appears to possess adequate validity and reliability and may be utilized in clinical trials to assess the health-related quality of life in Chinese osteoporotic fracture females.

Early detection of germinated wheat grains using terahertz image and chemometrics

NASA Astrophysics Data System (ADS)

Jiang, Yuying; Ge, Hongyi; Lian, Feiyu; Zhang, Yuan; Xia, Shanhong

2016-02-01

In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

Gender differences associated with enrollment in the Texas Academy of Mathematics and Science

NASA Astrophysics Data System (ADS)

Burns, Robert Thomas

This study sought to determine if different factors had influenced females and males to select engineering/science-related studies at the Texas Academy of Mathematics and Science (TAMS). The data were collected in the fall semester in 1997 at TAMS located on the University of North Texas campus from a survey of factors reported in the literature that had influenced students to enroll in engineering/science-related curriculum. Of the 380 TAMS students enrolled fall semester, 303 or 85% participated in the study. Those who participated included 135 or 45% females and 168 or 55% males. A dichotomous discriminant function analysis to identify relationships between the criterion variable (gender) and the predictor variable (factors) was used. The analysis of variance (ANOVA) was used to identify any significant predictor (factor) when the criterion was gender. Analysis of the data indicated no difference between females and males concerning factors that influenced them to enroll in TAMS. Neither discriminant function analysis nor the regression analysis using weighted least squares could significantly establish any relationship that could predict a student to be female or male with respect to factors that influenced them to enroll in TAMS. The factors were ranked utilizing the Thurstone equal appearing intervals scale for both females and males. Both females and males in TAMS ranked extrinsic interest including job opportunity, salary, and promotion, as the most important factor. The least important factor for both females and males was family encouragement. The findings indicate that TAMS students based their enrollment decision on factors independent of those suggested in the literature as applying to males and females. This may have resulted from the fact that these students are a unique population biased toward valuing a math/science curriculum.

Analysis of volatile compounds in exhaled breath condensate in patients with severe pulmonary arterial hypertension.

PubMed

Mansoor, J K; Schelegle, Edward S; Davis, Cristina E; Walby, William F; Zhao, Weixiang; Aksenov, Alexander A; Pasamontes, Alberto; Figueroa, Jennifer; Allen, Roblee

2014-01-01

An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH. Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients. These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH.

Analysis of Volatile Compounds in Exhaled Breath Condensate in Patients with Severe Pulmonary Arterial Hypertension

PubMed Central

Mansoor, J. K.; Schelegle, Edward S.; Davis, Cristina E.; Walby, William F.; Zhao, Weixiang; Aksenov, Alexander A.; Pasamontes, Alberto; Figueroa, Jennifer; Allen, Roblee

2014-01-01

Background An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH. Methodolgy and Findings Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients. Conclusions These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH. PMID:24748102

Retargeted Least Squares Regression Algorithm.

PubMed

Zhang, Xu-Yao; Wang, Lingfeng; Xiang, Shiming; Liu, Cheng-Lin

2015-09-01

This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero-one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with the traditional least squares regression (LSR) and a recently proposed discriminative LSR models, ReLSR is much more accurate in measuring the classification error of the regression model. Furthermore, ReLSR is a single and compact model, hence there is no need to train two-class (binary) machines that are independent of each other. The convex optimization problem of ReLSR is solved elegantly and efficiently with an alternating procedure including regression and retargeting as substeps. The experimental evaluation over a range of databases identifies the validity of our method.

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.

The validation of Huffaz Intelligence Test (HIT)

NASA Astrophysics Data System (ADS)

Rahim, Mohd Azrin Mohammad; Ahmad, Tahir; Awang, Siti Rahmah; Safar, Ajmain

2017-08-01

In general, a hafiz who can memorize the Quran has many specialties especially in respect to their academic performances. In this study, the theory of multiple intelligences introduced by Howard Gardner is embedded in a developed psychometric instrument, namely Huffaz Intelligence Test (HIT). This paper presents the validation and the reliability of HIT of some tahfiz students in Malaysia Islamic schools. A pilot study was conducted involving 87 huffaz who were randomly selected to answer the items in HIT. The analysis method used includes Partial Least Square (PLS) on reliability, convergence and discriminant validation. The study has validated nine intelligences. The findings also indicated that the composite reliabilities for the nine types of intelligences are greater than 0.8. Thus, the HIT is a valid and reliable instrument to measure the multiple intelligences among huffaz.

Recidivism following spouse abuse abatement counseling: treatment program implications.

PubMed

Hamberger, L K; Hastings, J E

1990-01-01

This paper examined demographic and personality characteristics of violence-free completers (n = 74) and violence repeating completers (n = 32) of a spouse abuse abatement counseling program. Chi-square analyses on categorical data, and analyses of variance on personality test data revealed several predicted findings. Compared to violence-free completers, recidivists reported higher levels of substance abuse both before and after treatment. Recidivists also showed evidence of higher narcissism, measured by the Narcissistic, Gregarious and Aggressive subscales of the Millon Clinical Multiaxial Inventory. Referral source (self or court) did not differentiate the two groups, nor did record of criminal activity. Subsequent discriminant function analysis, entering all predicted variables, correctly identified 65.4% of the recidivists and 73.1% of violence-free completers. Clinical and research implications of the findings are discussed.

Face recognition based on two-dimensional discriminant sparse preserving projection

NASA Astrophysics Data System (ADS)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

[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.

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.

Multivariate classification of edible salts: Simultaneous Laser-Induced Breakdown Spectroscopy and Laser-Ablation Inductively Coupled Plasma Mass Spectrometry Analysis

NASA Astrophysics Data System (ADS)

Lee, Yonghoon; Nam, Sang-Ho; Ham, Kyung-Sik; Gonzalez, Jhanis; Oropeza, Dayana; Quarles, Derrick; Yoo, Jonghyun; Russo, Richard E.

2016-04-01

Laser-Induced Breakdown Spectroscopy (LIBS) and Laser-Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), both based on laser ablation sampling, can be employed simultaneously to obtain different chemical fingerprints from a sample. We demonstrated that this analysis approach can provide complementary information for improved classification of edible salts. LIBS could detect several of the minor metallic elements along with Na and Cl, while LA-ICP-MS spectra were used to measure non-metallic and trace heavy metal elements. Principal component analysis using LIBS and LA-ICP-MS spectra showed that their major spectral variations classified the sample salts in different ways. Three classification models were developed by using partial least squares-discriminant analysis based on the LIBS, LA-ICP-MS, and their fused data. From the cross-validation performances and confusion matrices of these models, the minor metallic elements (Mg, Ca, and K) detected by LIBS and the non-metallic (I) and trace heavy metal (Ba, W, and Pb) elements detected by LA-ICP-MS provided complementary chemical information to distinguish particular salt samples.

Laser-induced breakdown spectroscopy in industrial and security applications

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

Bol'shakov, Alexander A.; Yoo, Jong H.; Liu Chunyi

2010-05-01

Laser-induced breakdown spectroscopy (LIBS) offers rapid, localized chemical analysis of solid or liquid materials with high spatial resolution in lateral and depth profiling, without the need for sample preparation. Principal component analysis and partial least squares algorithms were applied to identify a variety of complex organic and inorganic samples. This work illustrates how LIBS analyzers can answer a multitude of real-world needs for rapid analysis, such as determination of lead in paint and children's toys, analysis of electronic and solder materials, quality control of fiberglass panels, discrimination of coffee beans from different vendors, and identification of generic versus brand-name drugs.more » Lateral and depth profiling was performed on children's toys and paint layers. Traditional one-element calibration or multivariate chemometric procedures were applied for elemental quantification, from single laser shot determination of metal traces at {approx}10 {mu}g/g to determination of halogens at 90 {mu}g/g using 50-shot spectral accumulation. The effectiveness of LIBS for security applications was demonstrated in the field by testing the 50-m standoff LIBS rasterizing detector.« less

Audible acoustics in high-shear wet granulation: application of frequency filtering.

PubMed

Hansuld, Erin M; Briens, Lauren; McCann, Joe A B; Sayani, Amyn

2009-08-13

Previous work has shown analysis of audible acoustic emissions from high-shear wet granulation has potential as a technique for end-point detection. In this research, audible acoustic emissions (AEs) from three different formulations were studied to further develop this technique as a process analytical technology. Condenser microphones were attached to three different locations on a PMA-10 high-shear granulator (air exhaust, bowl and motor) to target different sound sources. Size, flowability and tablet break load data was collected to support formulator end-point ranges and interpretation of AE analysis. Each formulation had a unique total power spectral density (PSD) profile that was sensitive to granule formation and end-point. Analyzing total PSD in 10 Hz segments identified profiles with reduced run variability and distinct maxima and minima suitable for routine granulation monitoring and end-point control. A partial least squares discriminant analysis method was developed to automate selection of key 10 Hz frequency groups using variable importance to projection. The results support use of frequency refinement as a way forward in the development of acoustic emission analysis for granulation monitoring and end-point control.

An American Dilemma. The Negro Problem and Modern Democracy. Volume I.

ERIC Educational Resources Information Center

Myrdal, Gunnar

The American dilemma, with regard to race, is posed as the question of how the nation can square its lofty ideals with the base realities of racial discrimination. This study, originally commissioned by the Carnegie Corporation in 1938, makes it clear that the struggle goes on in the hearts of all Americans. The root of the "Negro…

Discrimination, Racial Identity, and Cytokine Levels Among African American Adolescents

PubMed Central

Brody, Gene H.; Yu, Tianyi; Miller, Gregory E.; Chen, Edith

2015-01-01

Purpose Low-grade inflammation, measured by circulating levels of cytokines, is a pathogenic mechanism for several chronic diseases of aging. Identifying factors related to inflammation among African American youths may yield insights into mechanisms underlying racial disparities in health. The purpose of the study was to determine whether (a) reported racial discrimination from ages 17 to 19 forecast heightened cytokine levels at age 22, and (b) this association is lower for youths with positive racial identities. Methods A longitudinal research design was used with a community sample of 160 African Americans who were 17 at the beginning of the study. Discrimination and racial identity were measured with questionnaires, and blood was drawn to measure basal cytokine levels. Ordinary least squares regression analyses were used to examine the hypotheses. Results After controlling for socioeconomic risk, life stress, depressive symptoms, and body mass index, racial discrimination (β = .307, p < .01), racial identity (β = −.179, p < .05), and their interaction (β = −.180, p < .05) forecast cytokine levels. Youths exposed to high levels of racial discrimination evinced elevated cytokine levels 3 years later. This association was not significant for young adults with positive racial identities. Conclusions High levels of interpersonal racial discrimination and the development of a positive racial identity operate jointly to determine low-grade inflammation levels that have been found to forecast chronic diseases of aging, such as coronary disease and stroke. PMID:25907649

Translation-aware semantic segmentation via conditional least-square generative adversarial networks

NASA Astrophysics Data System (ADS)

Zhang, Mi; Hu, Xiangyun; Zhao, Like; Pang, Shiyan; Gong, Jinqi; Luo, Min

2017-10-01

Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.

[Features and influencing factors of self-discrimination among HIV/AIDS patients according to sex].

PubMed

Ju, L H; Lyu, P; Xu, P; Chen, W Y; He, H J; Ma, L P

2016-10-06

Objective: To investigate the features and influencing factors of self-discrimination among patients with HIV/AIDS according to sex. Methods: A total of 2 432 HIV/AIDS patients were recruited in Yunnan, Henan, Hubei, Jiangsu, Shanxi, Jilin, and Inner Mongolia provinces by a multistage stratified cluster sampling method, based on HIV epidemic and transmission modes, from May 2013 to October 2013. All participants were ≥18 years old, and we excluded those with mental disorders, hearing loss or other factors that prevented them from properly answering questions, and those who were unwilling to participate. A self-designed questionnaire was conducted to collect information about self-discrimination features and social behavior changes among HIV/AIDS patients. Differences in performance and self-discrimination features between participants of different sexes were compared using the chi-squared test. Factors influencing self-discrimination were analyzed by sex, using unconditional logistic regression. Results: Of the 2 432 cases, 78.9%(1 918 cases)were male and 21.1%(514 cases)female. The proportion of self-discrimination overall was 76.1%(1 850 cases); this proportion among female HIV/AIDS patients was 80.5%(414 cases), which was higher than that among men(74.9%, 1 436 cases)(χ 2 =7.17, P= 0.007). Of the 11 forms of self-discrimination performance, proportions of feeling guilt, shame, and self-abasement among participants were greater than 50%. Proportions of feeling shame, inferiority, and blaming others among females were 61.3%, 59.5%, and 45.3%, respectively, which were higher than these among males(49.8%, 50.0%, 28.4%, respectively)( P< 0.01). Multivariate unconditional logistic regression analysis showed that the risk of self-discrimination among those with HIV confirmatory testing time ≥1 year was higher than those with HIV confirmatory testing time <1 year(females: OR= 35.67, 95 %CI :17.28-73.64; males: OR= 8.74, 95 % CI :6.79-11.25). Compared with other occupations, the risk of self-discrimination among male farm workers was higher( OR= 1.62, 95 % CI :1.03-2.54). The risks of self-discrimination in males who had been infected with HIV by transmission routes of blood transfusion or blood collection( OR= 2.38, 95 % CI :1.31-4.30), injection drug use( OR= 1.78, 95 % CI :1.09-2.91), and male-to-male sexual behavior( OR= 1.48, 95 %CI :1.08-2.03)were higher than in males infected via heterosexual behavior. Conclusion: Female HIV/AIDS patients are more likely to engage in self-discrimination than male patients. Self-discrimination mainly takes the form of feeling remorse, shame, and inferiority. Confirmatory testing time ≥1 year, occupation as a farm work, and routes of transmission via blood transfusion or collection, injection drug use, and male-to-male sexual behavior are influencing factors of self-discrimination among male HIV/AIDS patients. Confirmatory testing time ≥1 year is the influencing factor of self-discrimination among female HIV/AIDS patients.

Minimum error discrimination between similarity-transformed quantum states

NASA Astrophysics Data System (ADS)

Jafarizadeh, M. A.; Sufiani, R.; Mazhari Khiavi, Y.

2011-07-01

Using the well-known necessary and sufficient conditions for minimum error discrimination (MED), we extract an equivalent form for the MED conditions. In fact, by replacing the inequalities corresponding to the MED conditions with an equivalent but more suitable and convenient identity, the problem of mixed state discrimination with optimal success probability is solved. Moreover, we show that the mentioned optimality conditions can be viewed as a Helstrom family of ensembles under some circumstances. Using the given identity, MED between N similarity transformed equiprobable quantum states is investigated. In the case that the unitary operators are generating a set of irreducible representation, the optimal set of measurements and corresponding maximum success probability of discrimination can be determined precisely. In particular, it is shown that for equiprobable pure states, the optimal measurement strategy is the square-root measurement (SRM), whereas for the mixed states, SRM is not optimal. In the case that the unitary operators are reducible, there is no closed-form formula in the general case, but the procedure can be applied in each case in accordance to that case. Finally, we give the maximum success probability of optimal discrimination for some important examples of mixed quantum states, such as generalized Bloch sphere m-qubit states, spin-j states, particular nonsymmetric qudit states, etc.

Hiring discrimination against people with disabilities under the ADA: characteristics of charging parties.

PubMed

McMahon, Brian T; Roessler, Richard; Rumrill, Philip D; Hurley, Jessica E; West, Steven L; Chan, Fong; Carlson, Linnea

2008-06-01

This article describes findings from a causal comparative study of the characteristics of Charging Parties who filed allegations of Hiring discrimination with the U.S. Equal Employment Opportunity Commission (EEOC) under Title I of the Americans with Disabilities Act (ADA) between 1992 and 2005. Charging Party Characteristics derived from 19,527 closed Hiring allegations are compared and contrasted to 259,680 closed allegations aggregated from six other prevalent forms of discrimination including Discharge and Constructive Discharge, Reasonable Accommodation, Disability Harassment and Intimidation, and Terms and Conditions of Employment. Tests of Proportion distributed as chi-square are used to form comparisons along a variety of factors including age, gender, impairment, and ethnicity. Most allegations of ADA job discrimination fall into the realm of job retention and career advancement as opposed to job acquisition. Hiring allegations, however, tend to be filed by Charging Parties who are disproportionately male, younger or older applicants, white, and coping with physical or sensory disabilities. Prevailing theories about stigma suggest that negative attitudes are more prevalent toward persons with behavioral disabilities. However, this study provides clear evidence that one behavioral manifestation of negative attitudes, Hiring discrimination, is more often directed at persons with physical or sensory impairments. More outreach regarding ADA rights appears indicated for individuals who share the aforementioned characteristics.

Minimum error discrimination between similarity-transformed quantum states

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

Jafarizadeh, M. A.; Institute for Studies in Theoretical Physics and Mathematics, Tehran 19395-1795; Research Institute for Fundamental Sciences, Tabriz 51664

2011-07-15

Using the well-known necessary and sufficient conditions for minimum error discrimination (MED), we extract an equivalent form for the MED conditions. In fact, by replacing the inequalities corresponding to the MED conditions with an equivalent but more suitable and convenient identity, the problem of mixed state discrimination with optimal success probability is solved. Moreover, we show that the mentioned optimality conditions can be viewed as a Helstrom family of ensembles under some circumstances. Using the given identity, MED between N similarity transformed equiprobable quantum states is investigated. In the case that the unitary operators are generating a set of irreduciblemore » representation, the optimal set of measurements and corresponding maximum success probability of discrimination can be determined precisely. In particular, it is shown that for equiprobable pure states, the optimal measurement strategy is the square-root measurement (SRM), whereas for the mixed states, SRM is not optimal. In the case that the unitary operators are reducible, there is no closed-form formula in the general case, but the procedure can be applied in each case in accordance to that case. Finally, we give the maximum success probability of optimal discrimination for some important examples of mixed quantum states, such as generalized Bloch sphere m-qubit states, spin-j states, particular nonsymmetric qudit states, etc.« less

A Search for WIMP Dark Matter using an Optimized Chi-square Technique on the Final Data from the Cryogenic Dark Matter Search Experiment (CDMS II)

NASA Astrophysics Data System (ADS)

Manungu Kiveni, Joseph

2012-06-01

During the last two decades, cosmology has become a precision observational science thanks (in part) to the incredible number of experiments performed to better understand the composition of the universe. The large amount of data accumulated strongly indicates that the bulk of the universe's matter is in the form of non-baryonic matter that does not interact electromagnetically. Combined evidence from the dynamics of galaxies and galaxy clusters confirms that most of the mass in the universe is not composed of any known form of matter. Measurements of the cosmic microwave background, big bang nucleosynthesis and many other experiments indicate that ˜80% of the matter in the universe is dark, non-relativistic and cold. The dark matter resides in the halos surrounding galaxies, galaxy clusters and other large-scale structures. Weakly Interacting Massive Particles (WIMPs) are well motivated class of dark matter candidates that arise naturally in supersymmetric extensions to the Standard Model of particles physics, and can be produced as non-relativistic thermal relics in the early universe with about the right density to account for the missing mass. The Cryogenic Dark Matter Search (CDMS) experiment seeks to directly detect the keV-scale energy deposited by WIMPs in the galactic halo when they scatter from nuclei in the crystalline detectors made of germanium and silicon. These detectors, called Z-sensitive Ionization and Phonon detectors (ZIPs) are operated at ˜ 45 mK and simultaneously measure the ionization and the (athermal) phonons produced by particle interactions. The ratio of ionization and phonon energies allows discrimination of a low rate of nuclear recoils (expected for WIMPs) from an overwhelming rate of electron recoils (expected for most backgrounds). Phonon-pulse shape and timing enables further suppression of lower-rate interactions at the detector surfaces. This dissertation describes the results of a WIMP search using CDMS II data sets accumulated at the Soudan Underground Laboratory in Minnesota. Results from the original analysis of these data were published in 2009; two events were observed in the signal region with an expected leakage of 0.9 events. Further investigation revealed an issue with the ionization-pulse reconstruction algorithm leading to a software upgrade and a subsequent reanalysis of the data. As part of the reanalysis, I performed an advanced discrimination technique to better distinguish (potential) signal events from backgrounds using a 5-dimensional chi-square method. This data-analysis technique combines the event information recorded for each WIMP-search event to derive a background-discrimination parameter capable of reducing the expected background to less than one event, while maintaining high efficiency for signal events. Furthermore, optimizing the cut positions of this 5-dimensional chi-square parameter for the 14 viable germanium detectors yields an improved expected sensitivity to WIMP interactions relative to previous CDMS results. This dissertation describes my improved (and optimized) discrimination technique and the results obtained from a blind application to the reanalyzed CDMS II WIMP-search data. This analysis achieved the best expected sensitivity of the three techniques developed for the reanalysis and so was chosen as the primary timing analysis whose limit will be quoted in a on-going publication paper which is currently in preparation. For this analysis, a total raw exposure of 612.17 kg-days are analyzed for this work. No candidate events was observed, and a corresponding upper limit on the WIMP-nucleon scattering cross section as a function of WIMP mass is defined. These data set a 90% upper limit on spin-independent WIMP-nucleon elastic-scattering cross section of 3.19 × 10-44 cm2 for a WIMP mass of 60 GeV c 2. Combining this result with all previous CDMS II data gives an upper limit of 1.96 ×10-44 cm2 for a WIMP of mass 60 GeV/c2 (a factor of 2 better than the original analysis). At the moment this analysis is being written, the WIMP-search results obtained with the reanalyzed CDMS II data occupies the second most stringent limits on WIMP-nucleon scattering, after XENON100, excluding previously unexplored parameter space. Interesting parameter space is excluded for WIMP-nucleon cross section as function of WIMP masse under standard assumptions, the parameter space favored by interpretations of other experiments's data as low-mass WIMP signals due to an excess of low energy events and annual modulation is partially excluded for DAMA/LIBRA and CoGeNT.

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

Chinese version of Impact of Weight on Quality of Life for Kids: psychometric properties in a large school-based sample.

PubMed

He, Jinbo; Zhu, Hong; Luo, Xingwei; Cai, Taisheng; Wu, Siyao; Lu, Yao

2016-06-01

The Impact of Weight on Quality of Life for Kids (IWQOL-Kids) is the first self-report questionnaire for assessing weight-related quality of life for youth. However, there is no Chinese version of IWQOL-Kids. Thus, the objective of this research was to translate IWQOL-Kids into Mandarin and evaluate its psychometric properties in a large school-based sample. The total sample included 2282 participants aged 11-18 years old, including 1703 non-overweight, 386 overweight and 193 obese students. IWQOL-Kids was translated and culturally adapted by following the international guidelines for instrument linguistic validation procedures. The psychometric evaluation included internal consistency, test-retest reliability, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), convergent validity and discriminant validity. Cronbach's α for the Chinese version of IWQOL-Kids (IWQOL-Kids-C) was 0.956 and ranged from 0.891 to 0.927 for subscales. IWQOL-Kids-C showed a test-retest coefficient of 0.937 after 2 weeks and ranged from 0.847 to 0.903 for subscales. The original four-factor model was reproduced by EFA after seven iterations, accounting for 69.28% of the total variance. CFA demonstrated that the four-factor model had good fit indices with comparative fit index = 0.92, normed fit index = 0.91, goodness of fit index = 0.86, root mean square error of approximation = 0.07 and root mean square residual = 0.03. Convergent validity and discriminant validity were demonstrated with higher correlations between similar constructs and lower correlations between dissimilar constructs of IWQOL-Kids-C and PedsQL™ 4.0. The significant differences were found across the body mass index groups, and IWQOL-Kids-C had higher effect sizes than PedsQL™4.0 when comparing non-overweight and obese groups, supporting the sensitivity of IWQOL-Kids-C. IWQOL-Kids-C is a satisfactory, valid and reliable instrument to assess weight-related quality of life for Chinese children and adolescents aged 11-18 years old. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

PubMed

Mohamadi Monavar, H; Afseth, N K; Lozano, J; Alimardani, R; Omid, M; Wold, J P

2013-07-15

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively. Crown Copyright © 2013. Published by 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.

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.