GC/MS analysis of pesticides in the Ferrara area (Italy) surface water: a chemometric study.

PubMed

Pasti, Luisa; Nava, Elisabetta; Morelli, Marco; Bignami, Silvia; Dondi, Francesco

2007-01-01

The development of a network to monitor surface waters is a critical element in the assessment, restoration and protection of water quality. In this study, concentrations of 42 pesticides--determined by GC-MS on samples from 11 points along the Ferrara area rivers--have been analyzed by chemometric tools. The data were collected over a three-year period (2002-2004). Principal component analysis of the detected pesticides was carried out in order to define the best spatial locations for the sampling points. The results obtained have been interpreted in view of agricultural land use. Time series data regarding pesticide contents in surface waters has been analyzed using the Autocorrelation function. This chemometric tool allows for seasonal trends and makes it possible to optimize sampling frequency in order to detect the effective maximum pesticide content.

An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

NASA Astrophysics Data System (ADS)

Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

2017-04-01

Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis.

PubMed

Johnson, Kevin J; Wright, Bob W; Jarman, Kristin H; Synovec, Robert E

2003-05-09

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state

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.

Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists.

PubMed

Tauler, Roma; Parastar, Hadi

2018-03-23

This review aims to demonstrate abilities to analyze Big (Bio)Chemical Data (BBCD) with multivariate chemometric methods and to show some of the more important challenges of modern analytical researches. In this review, the capabilities and versatility of chemometric methods will be discussed in light of the BBCD challenges that are being encountered in chromatographic, spectroscopic and hyperspectral imaging measurements, with an emphasis on their application to omics sciences. In addition, insights and perspectives on how to address the analysis of BBCD are provided along with a discussion of the procedures necessary to obtain more reliable qualitative and quantitative results. In this review, the importance of Big Data and of their relevance to (bio)chemistry are first discussed. Then, analytical tools which can produce BBCD are presented as well as some basics needed to understand prospects and limitations of chemometric techniques when they are applied to BBCD are given. Finally, the significance of the combination of chemometric approaches with BBCD analysis in different chemical disciplines is highlighted with some examples. In this paper, we have tried to cover some of the applications of big data analysis in the (bio)chemistry field. However, this coverage is not extensive covering everything done in the field. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A modern approach to the authentication and quality assessment of thyme using UV spectroscopy and chemometric analysis.

PubMed

Gad, Haidy A; El-Ahmady, Sherweit H; Abou-Shoer, Mohamed I; Al-Azizi, Mohamed M

2013-01-01

Recently, the fields of chemometrics and multivariate analysis have been widely implemented in the quality control of herbal drugs to produce precise results, which is crucial in the field of medicine. Thyme represents an essential medicinal herb that is constantly adulterated due to its resemblance to many other plants with similar organoleptic properties. To establish a simple model for the quality assessment of Thymus species using UV spectroscopy together with known chemometric techniques. The success of this model may also serve as a technique for the quality control of other herbal drugs. The model was constructed using 30 samples of authenticated Thymus vulgaris and challenged with 20 samples of different botanical origins. The methanolic extracts of all samples were assessed using UV spectroscopy together with chemometric techniques: principal component analysis (PCA), soft independent modeling of class analogy (SIMCA) and hierarchical cluster analysis (HCA). The model was able to discriminate T. vulgaris from other Thymus, Satureja, Origanum, Plectranthus and Eriocephalus species, all traded in the Egyptian market as different types of thyme. The model was also able to classify closely related species in clusters using PCA and HCA. The model was finally used to classify 12 commercial thyme varieties into clusters of species incorporated in the model as thyme or non-thyme. The model constructed is highly recommended as a simple and efficient method for distinguishing T. vulgaris from other related species as well as the classification of marketed herbs as thyme or non-thyme. Copyright © 2013 John Wiley & Sons, Ltd.

Fluorescence Spectroscopy and Chemometric Modeling for Bioprocess Monitoring

PubMed Central

Faassen, Saskia M.; Hitzmann, Bernd

2015-01-01

On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables. PMID:25942644

Effect of emodin on Candida albicans growth investigated by microcalorimetry combined with chemometric analysis.

PubMed

Kong, W J; Wang, J B; Jin, C; Zhao, Y L; Dai, C M; Xiao, X H; Li, Z L

2009-07-01

Using the 3114/3115 thermal activity monitor (TAM) air isothermal microcalorimeter, ampoule mode, the heat output of Candida albicans growth at 37 degrees C was measured, and the effect of emodin on C. albicans growth was evaluated by microcalorimetry coupled with chemometric methods. The similarities between the heat flow power (HFP)-time curves of C. albicans growth affected by different concentrations of emodin were calculated by similarity analysis (SA). In the correspondence analysis (CA) diagram of eight quantitative parameters taken from the HFP-time curves, it could be deduced that emodin had definite dose-effect relationship as the distance between different concentrations of it increased along with the dosage and the effect. From the principal component analysis (PCA) on eight quantitative parameters, the action of emodin on C. albicans growth could be easily evaluated by analyzing the change of values of the main two parameters, growth rate constant k (2) and maximum power output P(2)(m). The coherent results of SA, CA, and PCA showed that emodin at different concentrations had different effects on C. albicans growth metabolism: A low concentration (0-10 microg ml(-1)) poorly inhibited the growth of C. albicans, and a high concentration (15-35 microg ml(-1)) could notably inhibit growth of this fungus. This work provided a useful idea of the combination of microcalorimetry and chemometric analysis for investigating the effect of drug and other compounds on microbes.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz).

PubMed

Moresco, Rodolfo; Uarrota, Virgílio Gavicho; Pereira, Aline; Tomazzoli, Maíra Maciel; Nunes, Eduardo da C; Peruch, Luiz Augusto Martins; Gazzola, Jussara; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-10-21

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in β-carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis-β-carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (red-fleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz).

PubMed

Moresco, Rodolfo; Uarrota, Virgílio G; Pereira, Aline; Tomazzoli, Maíra; Nunes, Eduardo da C; Martins Peruch, Luiz Augusto; Gazzola, Jussara; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-12-01

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in β-carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis- β-carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass

PubMed Central

Via, Brian K.; Fasina, Oladiran O.; Adhikari, Sushil; Billor, Nedret; Eckhardt, Lori G.

2017-01-01

The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2–0.92; RPD– 3.58) and lignin (R2–0.82; RPD– 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon. PMID:28253322

Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass.

PubMed

Acquah, Gifty E; Via, Brian K; Fasina, Oladiran O; Adhikari, Sushil; Billor, Nedret; Eckhardt, Lori G

2017-01-01

The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2-0.92; RPD- 3.58) and lignin (R2-0.82; RPD- 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.

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.

Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.

PubMed

Pavlovich, Matthew J; Dunn, Emily E; Hall, Adam B

2016-05-15

Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand. Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples. Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models. Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives. Copyright © 2016 John Wiley & Sons, Ltd.

Differentiation and classification of bacteria using vancomycin functionalized silver nanorods array based surface-enhanced raman spectroscopy an chemometric analysis

USDA-ARS?s Scientific Manuscript database

The intrinsic surface-enhanced Raman scattering (SERS) was used for differentiating and classifying bacterial species with chemometric data analysis. Such differentiation has often been conducted with an insufficient sample population and strong interference from the food matrices. To address these ...

New approach to the differentiation of marble samples using thermal analysis and chemometrics in order to identify provenance

PubMed Central

2014-01-01

Background The possibility of applying a novel chemometric approach which could allow the differentiation of marble samples, all from different quarries located in the Mediterranean basin and frequently used in ancient times for artistic purposes, was investigated. By suggesting tentative or allowing to rule out unlikely attributions, this kind of differentiation could, indeed, be of valuable support to restorers and other professionals in the field of cultural heritage. Experimental data were obtained only using thermal analytical techniques: Thermogravimetry (TG), Derivative Thermogravimetry (DTG) and Differential Thermal Analysis (DTA). Results The extraction of kinetic parameters from the curves obtained using these thermal analytical techniques allowed Activation Energy values to be evaluated together with the logarithm of the Arrhenius pre-exponential factor of the main TG-DTG process. The main data thus obtained after subsequent chemometric evaluation (using Principal Components Analysis) have already proved useful in the identification the original quarry of a small number of archaeological marble finds. Conclusion One of the most evident advantages of the thermoanalytical – chemometric approach adopted seems to be that it allows the certain identification of an unknown find composed of a marble known to be present among the reference samples considered, that is, contained in the reference file. On the other hand with equal certainty it prevents the occurrence of erroneous or highly uncertain identification if the find being tested does not belong to the reference file considered. PMID:24982691

Thin-layer chromatographic identification of Chinese propolis using chemometric fingerprinting.

PubMed

Tang, Tie-xin; Guo, Wei-yan; Xu, Ye; Zhang, Si-ming; Xu, Xin-jun; Wang, Dong-mei; Zhao, Zhi-min; Zhu, Long-ping; Yang, De-po

2014-01-01

Poplar tree gum has a similar chemical composition and appearance to Chinese propolis (bee glue) and has been widely used as a counterfeit propolis because Chinese propolis is typically the poplar-type propolis, the chemical composition of which is determined mainly by the resin of poplar trees. The discrimination of Chinese propolis from poplar tree gum is a challenging task. To develop a rapid thin-layer chromatographic (TLC) identification method using chemometric fingerprinting to discriminate Chinese propolis from poplar tree gum. A new TLC method using a combination of ammonia and hydrogen peroxide vapours as the visualisation reagent was developed to characterise the chemical profile of Chinese propolis. Three separate people performed TLC on eight Chinese propolis samples and three poplar tree gum samples of varying origins. Five chemometric methods, including similarity analysis, hierarchical clustering, k-means clustering, neural network and support vector machine, were compared for use in classifying the samples based on their densitograms obtained from the TLC chromatograms via image analysis. Hierarchical clustering, neural network and support vector machine analyses achieved a correct classification rate of 100% in classifying the samples. A strategy for TLC identification of Chinese propolis using chemometric fingerprinting was proposed and it provided accurate sample classification. The study has shown that the TLC identification method using chemometric fingerprinting is a rapid, low-cost method for the discrimination of Chinese propolis from poplar tree gum and may be used for the quality control of Chinese propolis. Copyright © 2014 John Wiley & Sons, Ltd.

Analysis of laser printer and photocopier toners by spectral properties and chemometrics

NASA Astrophysics Data System (ADS)

Verma, Neha; Kumar, Raj; Sharma, Vishal

2018-05-01

The use of printers to generate falsified documents has become a common practice in today's world. The examination and identification of the printed matter in the suspected documents (civil or criminal cases) may provide important information about the authenticity of the document. In the present study, a total number of 100 black toner samples both from laser printers and photocopiers were examined using diffuse reflectance UV-Vis Spectroscopy. The present research is divided into two parts; visual discrimination and discrimination by using multivariate analysis. A comparison between qualitative and quantitative analysis showed that multivariate analysis (Principal component analysis) provides 99.59%pair-wise discriminating power for laser printer toners while 99.84% pair-wise discriminating power for photocopier toners. The overall results obtained confirm the applicability of UV-Vis spectroscopy and chemometrics, in the nondestructive analysis of toner printed documents while enhancing their evidential value for forensic applications.

Chemometric Data Analysis for Deconvolution of Overlapped Ion Mobility Profiles

NASA Astrophysics Data System (ADS)

Zekavat, Behrooz; Solouki, Touradj

2012-11-01

We present the details of a data analysis approach for deconvolution of the ion mobility (IM) overlapped or unresolved species. This approach takes advantage of the ion fragmentation variations as a function of the IM arrival time. The data analysis involves the use of an in-house developed data preprocessing platform for the conversion of the original post-IM/collision-induced dissociation mass spectrometry (post-IM/CID MS) data to a Matlab compatible format for chemometric analysis. We show that principle component analysis (PCA) can be used to examine the post-IM/CID MS profiles for the presence of mobility-overlapped species. Subsequently, using an interactive self-modeling mixture analysis technique, we show how to calculate the total IM spectrum (TIMS) and CID mass spectrum for each component of the IM overlapped mixtures. Moreover, we show that PCA and IM deconvolution techniques provide complementary results to evaluate the validity of the calculated TIMS profiles. We use two binary mixtures with overlapping IM profiles, including (1) a mixture of two non-isobaric peptides (neurotensin (RRPYIL) and a hexapeptide (WHWLQL)), and (2) an isobaric sugar isomer mixture of raffinose and maltotriose, to demonstrate the applicability of the IM deconvolution.

Combined chemometric analysis of (1)H NMR, (13)C NMR and stable isotope data to differentiate organic and conventional milk.

PubMed

Erich, Sarah; Schill, Sandra; Annweiler, Eva; Waiblinger, Hans-Ulrich; Kuballa, Thomas; Lachenmeier, Dirk W; Monakhova, Yulia B

2015-12-01

The increased sales of organically produced food create a strong need for analytical methods, which could authenticate organic and conventional products. Combined chemometric analysis of (1)H NMR-, (13)C NMR-spectroscopy data, stable-isotope data (IRMS) and α-linolenic acid content (gas chromatography) was used to differentiate organic and conventional milk. In total 85 raw, pasteurized and ultra-heat treated (UHT) milk samples (52 organic and 33 conventional) were collected between August 2013 and May 2014. The carbon isotope ratios of milk protein and milk fat as well as the α-linolenic acid content of these samples were determined. Additionally, the milk fat was analyzed by (1)H and (13)C NMR spectroscopy. The chemometric analysis of combined data (IRMS, GC, NMR) resulted in more precise authentication of German raw and retail milk with a considerably increased classification rate of 95% compared to 81% for NMR and 90% for IRMS using linear discriminate analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Chemical Fingerprint and Quantitative Analysis for the Quality Evaluation of Docynia dcne Leaves by High-Performance Liquid Chromatography Coupled with Chemometrics Analysis.

PubMed

Zhang, Xiaoyu; Mei, Xueran; Wang, Zhanguo; Wu, Jing; Liu, Gang; Hu, Huiling; Li, Qijuan

2018-05-24

Docynia dcne leaf from the genus of Docynia Dcne (including three species of Docynia delavayi, Docynia indica and Docynia longiunguis.) is an important raw material of local ethnic minority tea, ethnomedicines and food supplements in southwestern areas of China. However, D. dcne leaves from these three species are usually used confusingly, which could influence the therapeutic effect of it. A rapid and effective method for the chemical fingerprint and quantitative analysis to evaluate the quality of D. dcne leaves was established. The chemometric methods, including similarity analysis, hierarchical cluster analysis and partial least-squares discrimination analysis, were applied to distinguish 30 batches of D. dcne leaf samples from these three species. The above results could validate each other and successfully group these samples into three categories which were closely related to the species of D. dcne leaves. Moreover, isoquercitrin and phlorizin were screened as the chemical markers to evaluate the quality of D. dcne leaves from different species. And the contents of isoquercitrin and phlorizin varied remarkably in these samples, with ranges of 6.41-38.84 and 95.73-217.76 mg/g, respectively. All the results indicated that an integration method of chemical fingerprint couple with chemometrics analysis and quantitative assessment was a powerful and beneficial tool for quality control of D. dcne leaves, and could be applied also for differentiation and quality control of other herbal preparations.

Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis.

PubMed

Roshan, Abdul-Rahman A; Gad, Haidy A; El-Ahmady, Sherweit H; Khanbash, Mohamed S; Abou-Shoer, Mohamed I; Al-Azizi, Mohamed M

2013-08-14

This work describes a simple model developed for the authentication of monofloral Yemeni Sidr honey using UV spectroscopy together with chemometric techniques of hierarchical cluster analysis (HCA), principal component analysis (PCA), and soft independent modeling of class analogy (SIMCA). The model was constructed using 13 genuine Sidr honey samples and challenged with 25 honey samples of different botanical origins. HCA and PCA were successfully able to present a preliminary clustering pattern to segregate the genuine Sidr samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model presented a clear demarcation of the samples and was used to identify genuine Sidr honey samples as well as detect admixture with lower priced polyfloral honey by detection limits >10%. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other honey types worldwide.

Practical aspects of chemometrics for oil spill fingerprinting.

PubMed

Christensen, Jan H; Tomasi, Giorgio

2007-10-26

Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration and classification that can increase the speed and the objectivity of the analysis and allow for more extensive use of the available data in this field. However, although the chemometric literature is extensive, it does not focus on practical issues that are relevant to oil spill fingerprinting. The aim of this review is to provide a framework for the use of chemometric approaches in tiered oil spill fingerprinting and to provide clear-cut practical details and experiences that can be used by the forensic chemist. The framework is based on methods for initial screening, which include classification of samples into oil type, detection of non matches and of weathering state, and detailed oil spill fingerprinting, in which a more rigorous matching of an oil spill sample to suspected source oils is obtained. This review is intended as a tutorial, and is based on two examples of initial screening using respectively gas chromatography with flame ionization detection and fluorescence spectroscopy; and two of detailed oil spill fingerprinting where gas chromatography-mass spectrometry data are analyzed according to two approaches: The first relying on sections of processed chromatograms and the second on diagnostic ratios.

Chemometric approach to texture profile analysis of kombucha fermented milk products.

PubMed

Malbaša, Radomir; Jevrić, Lidija; Lončar, Eva; Vitas, Jasmina; Podunavac-Kuzmanović, Sanja; Milanović, Spasenija; Kovačević, Strahinja

2015-09-01

In the present work, relationships between the textural characteristics of fermented milk products obtained by kombucha inoculums with various teas were investigated by using chemometric analysis. The presented data which describe numerically the textural characteristics (firmness, consistency, cohesiveness and index of viscosity) were analysed. The quadratic correlation was determined between the textural characteristics of fermented milk products obtained at fermentation temperatures of 40 and 43 °C, using milk with 0.8, 1.6 and 2.8% milk fat and kombucha inoculums cultivated on the extracts of peppermint, stinging nettle, wild thyme and winter savory. Hierarchical cluster analysis (HCA) was performed to identify the similarities among the fermented products. The best mathematical models predicting the textural characteristics of investigated samples were developed. The results of this study indicate that textural characteristics of sample based on winter savory have a significant effect on textural characteristics of samples based on peppermint, stinging nettle and wild thyme, which can be very useful in the determination of products texture profile.

Fingerprint analysis of polysaccharides from different Ganoderma by HPLC combined with chemometrics methods.

PubMed

Sun, Xiaomei; Wang, Haohao; Han, Xiaofeng; Chen, Shangwei; Zhu, Song; Dai, Jun

2014-12-19

A fingerprint analysis method has been developed for characterization and discrimination of polysaccharides from different Ganoderma by high performance liquid chromatography (HPLC) coupled with chemometrics means. The polysaccharides were extracted under ultrasonic-assisted condition, and then partly hydrolyzed with trifluoroacetic acid. Monosaccharides and oligosaccharides in the hydrolyzates were subjected to pre-column derivatization with 1-phenyl-3-methyl-5-pyrazolone and HPLC analysis, which will generate unique fingerprint information related to chemical composition and structure of polysaccharides. The peak data were imported to professional software in order to obtain standard fingerprint profiles and evaluate similarity of different samples. Meanwhile, the data were further processed by hierarchical cluster analysis and principal component analysis. Polysaccharides from different parts or species of Ganoderma or polysaccharides from the same parts of Ganoderma but from different geographical regions or different strains could be differentiated clearly. This fingerprint analysis method can be applied to identification and quality control of different Ganoderma and their products. Copyright © 2014 Elsevier Ltd. All rights reserved.

Infrared imaging spectroscopy and chemometric tools for in situ analysis of an imiquimod pharmaceutical preparation presented as cream

NASA Astrophysics Data System (ADS)

Carneiro, Renato Lajarim; Poppi, Ronei Jesus

2014-01-01

In the present work the homogeneity of a pharmaceutical formulation presented as a cream was studied using infrared imaging spectroscopy and chemometric methodologies such as principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS). A cream formulation, presented as an emulsion, was prepared using imiquimod as the active pharmaceutical ingredient (API) and the excipients: water, vaseline, an emulsifier and a carboxylic acid in order to dissolve the API. After exposure at 45 °C during 3 months to perform accelerated stability test, the presence of some crystals was observed, indicating homogeneity problems in the formulation. PCA exploratory analysis showed that the crystal composition was different from the composition of the emulsion, since the score maps presented crystal structures in the emulsion. MCR-ALS estimated the spectra of the crystals and the emulsion. The crystals presented amine and C-H bands, suggesting that the precipitate was a salt formed by carboxylic acid and imiquimod. These results indicate the potential of infrared imaging spectroscopy in conjunction with chemometric methodologies as an analytical tool to ensure the quality of cream formulations in the pharmaceutical industry.

Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review.

PubMed

Gómez-Caravaca, Ana M; Maggio, Rubén M; Cerretani, Lorenzo

2016-03-24

Today virgin and extra-virgin olive oil (VOO and EVOO) are food with a large number of analytical tests planned to ensure its quality and genuineness. Almost all official methods demand high use of reagents and manpower. Because of that, analytical development in this area is continuously evolving. Therefore, this review focuses on analytical methods for EVOO/VOO which use fast and smart approaches based on chemometric techniques in order to reduce time of analysis, reagent consumption, high cost equipment and manpower. Experimental approaches of chemometrics coupled with fast analytical techniques such as UV-Vis spectroscopy, fluorescence, vibrational spectroscopies (NIR, MIR and Raman fluorescence), NMR spectroscopy, and other more complex techniques like chromatography, calorimetry and electrochemical techniques applied to EVOO/VOO production and analysis have been discussed throughout this work. The advantages and drawbacks of this association have also been highlighted. Chemometrics has been evidenced as a powerful tool for the oil industry. In fact, it has been shown how chemometrics can be implemented all along the different steps of EVOO/VOO production: raw material input control, monitoring during process and quality control of final product. Copyright © 2016 Elsevier B.V. All rights reserved.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis.

PubMed

Li, Tao; Su, Chen

2018-06-02

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and

Quality Assessment of Kumu Injection, a Traditional Chinese Medicine Preparation, Using HPLC Combined with Chemometric Methods and Qualitative and Quantitative Analysis of Multiple Alkaloids by Single Marker.

PubMed

Wang, Ning; Li, Zhi-Yong; Zheng, Xiao-Li; Li, Qiao; Yang, Xin; Xu, Hui

2018-04-09

Kumu injection (KMI) is a common-used traditional Chinese medicine (TCM) preparation made from Picrasma quassioides (D. Don) Benn. rich in alkaloids. An innovative technique for quality assessment of KMI was developed using high performance liquid chromatography (HPLC) combined with chemometric methods and qualitative and quantitative analysis of multi-components by single marker (QAMS). Nigakinone (PQ-6, 5-hydroxy-4-methoxycanthin-6-one), one of the most abundant alkaloids responsible for the major pharmacological activities of Kumu, was used as a reference substance. Six alkaloids in KMI were quantified, including 6-hydroxy- β -carboline-1-carboxylic acid (PQ-1), 4,5-dimethoxycanthin-6-one (PQ-2), β -carboline-1-carboxylic acid (PQ-3), β -carboline-1-propanoic acid (PQ-4), 3-methylcanthin-5,6-dione (PQ-5), and PQ-6. Based on the outcomes of twenty batches of KMI samples, the contents of six alkaloids were used for further chemometric analysis. By hierarchical cluster analysis (HCA), radar plots, and principal component analysis (PCA), all the KMI samples could be categorized into three groups, which were closely related to production date and indicated the crucial influence of herbal raw material on end products of KMI. QAMS combined with chemometric analysis could accurately measure and clearly distinguish the different quality samples of KMI. Hence, QAMS is a feasible and promising method for the quality control of KMI.

An Advanced Undergraduate Chemistry Laboratory Experiment Exploring NIR Spectroscopy and Chemometrics

ERIC Educational Resources Information Center

Wanke, Randall; Stauffer, Jennifer

2007-01-01

An advanced undergraduate chemistry laboratory experiment to study the advantages and hazards of the coupling of NIR spectroscopy and chemometrics is described. The combination is commonly used for analysis and process control of various ingredients used in agriculture, petroleum and food products.

Classification of smoke tainted wines using mid-infrared spectroscopy and chemometrics.

PubMed

Fudge, Anthea L; Wilkinson, Kerry L; Ristic, Renata; Cozzolino, Daniel

2012-01-11

In this study, the suitability of mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and linear discriminant analysis (LDA), was evaluated as a rapid analytical technique to identify smoke tainted wines. Control (i.e., unsmoked) and smoke-affected wines (260 in total) from experimental and commercial sources were analyzed by MIR spectroscopy and chemometrics. The concentrations of guaiacol and 4-methylguaiacol were also determined using gas chromatography-mass spectrometry (GC-MS), as markers of smoke taint. LDA models correctly classified 61% of control wines and 70% of smoke-affected wines. Classification rates were found to be influenced by the extent of smoke taint (based on GC-MS and informal sensory assessment), as well as qualitative differences in wine composition due to grape variety and oak maturation. Overall, the potential application of MIR spectroscopy combined with chemometrics as a rapid analytical technique for screening smoke-affected wines was demonstrated.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

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.

HPLC fingerprint analysis combined with chemometrics for pattern recognition of ginger.

PubMed

Feng, Xu; Kong, Weijun; Wei, Jianhe; Ou-Yang, Zhen; Yang, Meihua

2014-03-01

Ginger, the fresh rhizome of Zingiber officinale Rosc. (Zingiberaceae), has been used worldwide; however, for a long time, there has been no standard approbated internationally for its quality control. To establish an efficacious and combinational method and pattern recognition technique for quality control of ginger. A simple, accurate and reliable method based on high-performance liquid chromatography with photodiode array (HPLC-PDA) detection was developed for establishing the chemical fingerprints of 10 batches of ginger from different markets in China. The method was validated in terms of precision, reproducibility and stability; and the relative standard deviations were all less than 1.57%. On the basis of this method, the fingerprints of 10 batches of ginger samples were obtained, which showed 16 common peaks. Coupled with similarity evaluation software, the similarities between each fingerprint of the sample and the simulative mean chromatogram were in the range of 0.998-1.000. Then, the chemometric techniques, including similarity analysis, hierarchical clustering analysis and principal component analysis were applied to classify the ginger samples. Consistent results were obtained to show that ginger samples could be successfully classified into two groups. This study revealed that HPLC-PDA method was simple, sensitive and reliable for fingerprint analysis, and moreover, for pattern recognition and quality control of ginger.

Using fluorescence spectroscopy coupled with chemometric analysis to investigate the origin, composition, and dynamics of dissolved organic matter in leachate-polluted groundwater.

PubMed

He, Xiao-Song; Xi, Bei-Dou; Gao, Ru-Tai; Wang, Lei; Ma, Yan; Cui, Dong-Yu; Tan, Wen-Bing

2015-06-01

Groundwater was collected in 2011 and 2012, and fluorescence spectroscopy coupled with chemometric analysis was employed to investigate the composition, origin, and dynamics of dissolved organic matter (DOM) in the groundwater. The results showed that the groundwater DOM comprised protein-, fulvic-, and humic-like substances, and the protein-like component originated predominantly from microbial production. The groundwater pollution by landfill leachate enhanced microbial activity and thereby increased microbial by-product-like material such as protein-like component in the groundwater. Excitation-emission matrix fluorescence spectra combined with parallel factor analysis showed that the protein-like matter content increased from 2011 to 2012 in the groundwater, whereas the fulvic- and humic-like matter concentration exhibited no significant changes. In addition, synchronous-scan fluorescence spectra coupled with two-dimensional correlation analysis showed that the change of the fulvic- and humic-like matter was faster than that of the protein-like substances, as the groundwater flowed from upstream to downstream in 2011, but slower than that of the protein-like substance in 2012 due to the enhancement of microbial activity. Fluorescence spectroscopy combined with chemometric analysis can investigate groundwater pollution characteristics and monitor DOM dynamics in groundwater.

Chemometric Methods to Quantify 1D and 2D NMR Spectral Differences Among Similar Protein Therapeutics.

PubMed

Chen, Kang; Park, Junyong; Li, Feng; Patil, Sharadrao M; Keire, David A

2018-04-01

NMR spectroscopy is an emerging analytical tool for measuring complex drug product qualities, e.g., protein higher order structure (HOS) or heparin chemical composition. Most drug NMR spectra have been visually analyzed; however, NMR spectra are inherently quantitative and multivariate and thus suitable for chemometric analysis. Therefore, quantitative measurements derived from chemometric comparisons between spectra could be a key step in establishing acceptance criteria for a new generic drug or a new batch after manufacture change. To measure the capability of chemometric methods to differentiate comparator NMR spectra, we calculated inter-spectra difference metrics on 1D/2D spectra of two insulin drugs, Humulin R® and Novolin R®, from different manufacturers. Both insulin drugs have an identical drug substance but differ in formulation. Chemometric methods (i.e., principal component analysis (PCA), 3-way Tucker3 or graph invariant (GI)) were performed to calculate Mahalanobis distance (D M ) between the two brands (inter-brand) and distance ratio (D R ) among the different lots (intra-brand). The PCA on 1D inter-brand spectral comparison yielded a D M value of 213. In comparing 2D spectra, the Tucker3 analysis yielded the highest differentiability value (D M  = 305) in the comparisons made followed by PCA (D M  = 255) then the GI method (D M  = 40). In conclusion, drug quality comparisons among different lots might benefit from PCA on 1D spectra for rapidly comparing many samples, while higher resolution but more time-consuming 2D-NMR-data-based comparisons using Tucker3 analysis or PCA provide a greater level of assurance for drug structural similarity evaluation between drug brands.

Circum-Arctic petroleum systems identified using decision-tree chemometrics

USGS Publications Warehouse

Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

2007-01-01

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

Direct rapid analysis of trace bioavailable soil macronutrients by chemometrics-assisted energy dispersive X-ray fluorescence and scattering spectrometry.

PubMed

Kaniu, M I; Angeyo, K H; Mwala, A K; Mangala, M J

2012-06-04

Precision agriculture depends on the knowledge and management of soil quality (SQ), which calls for affordable, simple and rapid but accurate analysis of bioavailable soil nutrients. Conventional SQ analysis methods are tedious and expensive. We demonstrate the utility of a new chemometrics-assisted energy dispersive X-ray fluorescence and scattering (EDXRFS) spectroscopy method we have developed for direct rapid analysis of trace 'bioavailable' macronutrients (i.e. C, N, Na, Mg, P) in soils. The method exploits, in addition to X-ray fluorescence, the scatter peaks detected from soil pellets to develop a model for SQ analysis. Spectra were acquired from soil samples held in a Teflon holder analyzed using (109)Cd isotope source EDXRF spectrometer for 200 s. Chemometric techniques namely principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANNs) were utilized for pattern recognition based on fluorescence and Compton scatter peaks regions, and to develop multivariate quantitative calibration models based on Compton scatter peak respectively. SQ analyses were realized with high CMD (R(2)>0.9) and low SEP (0.01% for N and Na, 0.05% for C, 0.08% for Mg and 1.98 μg g(-1) for P). Comparison of predicted macronutrients with reference standards using a one-way ANOVA test showed no statistical difference at 95% confidence level. To the best of the authors' knowledge, this is the first time that an XRF method has demonstrated utility in trace analysis of macronutrients in soil or related matrices. Copyright © 2012 Elsevier B.V. All rights reserved.

(Poly)phenolic fingerprint and chemometric analysis of white (Morus alba L.) and black (Morus nigra L.) mulberry leaves by using a non-targeted UHPLC-MS approach.

PubMed

Sánchez-Salcedo, Eva M; Tassotti, Michele; Del Rio, Daniele; Hernández, Francisca; Martínez, Juan José; Mena, Pedro

2016-12-01

This study reports the (poly)phenolic fingerprinting and chemometric discrimination of leaves of eight mulberry clones from Morus alba and Morus nigra cultivated in Spain. UHPLC-MS(n) (Ultra High Performance Liquid Chromatography-Mass Spectrometry) high-throughput analysis allowed the tentative identification of a total of 31 compounds. The phenolic profile of mulberry leaf was characterized by the presence of a high number of flavonol derivatives, mainly glycosylated forms of quercetin and kaempferol. Caffeoylquinic acids, simple phenolic acids, and some organic acids were also detected. Seven compounds were identified for the first time in mulberry leaves. The chemometric analysis (cluster analysis and principal component analysis) of the chromatographic data allowed the characterization of the different mulberry clones and served to explain the great intraspecific variability in mulberry secondary metabolism. This screening of the complete phenolic profile of mulberry leaves can assist the increasing interest for purposes related to quality control, germplasm screening, and bioactivity evaluation. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

THE NIST-EPA INTERAGENCY AGREEMENT ON MEASUREMENTS AND STANDARDS IN AEROSOL CARBON: SAMPLING REGIONAL PM 2.5 FOR THE CHEMOMETRIC OPTIMIZATION OF THERMAL-OPTICAL ANALYSIS

EPA Science Inventory

Results from the NIST-EPA Interagency Agreement on Measurements and Standards in Aerosol Carbon: Sampling Regional PM2.5 for the Chemometric Optimization of Thermal-Optical Analysis Study will be presented at the American Association for Aerosol Research (AAAR) 24th Annual Confer...

Chemotaxonomic Diversity of Three Ficus Species: Their Discrimination Using Chemometric Analysis and Their Role in Combating Oxidative Stress.

PubMed

Al-Musayeib, Nawal; Ebada, Sherif S; Gad, Haidy A; Youssef, Fadia S; Ashour, Mohamed Lotfy

2017-10-01

Genus Ficus (Moraceae) constitutes more than 850 species and about 2000 varieties and it acts as a golden mine that could afford effective and safe remedies combating many health disorders. Discrimination of Ficus cordata , Ficus ingens , and Ficus palmata using chemometric analysis and assessment of their role in combating oxidative stress. Phytochemical profiling of the methanol extracts of the three Ficus species and their successive fractions was performed using high-performance liquid chromatography/electrospray ionization mass spectrometry. Their discrimination was carried out using the obtained spectral data applying chemometric unsupervised pattern-recognition techniques, namely, principal component analysis and hierarchical cluster analysis. In vitro hepatoprotective and antioxidant evaluation of the samples was performed using human hepatocellular carcinoma cells challenged by carbon tetrachloride (CCl 4 ). Altogether, 22 compounds belonging to polyphenolics, flavonoids, and furanocoumarins were identified in the three Ficus species. Aviprin is the most abundant compound in F. cordata while chlorogenic acid and psoralen were present in high percentages in F. ingens and F. palmata , respectively. Chemometric analyses showed that F. palmata and F. cordata are more closely related chemically to each other rather than F. ingens . The ethyl acetate fractions of all the examined species showed a marked hepatoprotective efficacy accounting for 54.78%, 55.46%, and 56.42% reduction in serum level of alanine transaminase and 56.82%, 54.16%, and 57.06% suppression in serum level of aspartate transaminase, respectively, at 100 μg/mL comparable to CCl 4 -treated cells. Ficus species exhibited a no table antioxidant and hepatoprotective activity owing to their richness in polyphenolics and furanocoumarins. Ficus cordata , Ficus ingens , and Ficus palmata were analyzed using high-performance liquid chromatography/electrospray ionization mass spectrometry that revealed

Chemometric techniques in oil classification from oil spill fingerprinting.

PubMed

Ismail, Azimah; Toriman, Mohd Ekhwan; Juahir, Hafizan; Kassim, Azlina Md; Zain, Sharifuddin Md; Ahmad, Wan Kamaruzaman Wan; Wong, Kok Fah; Retnam, Ananthy; Zali, Munirah Abdul; Mokhtar, Mazlin; Yusri, Mohd Ayub

2016-10-15

Extended use of GC-FID and GC-MS in oil spill fingerprinting and matching is significantly important for oil classification from the oil spill sources collected from various areas of Peninsular Malaysia and Sabah (East Malaysia). Oil spill fingerprinting from GC-FID and GC-MS coupled with chemometric techniques (discriminant analysis and principal component analysis) is used as a diagnostic tool to classify the types of oil polluting the water. Clustering and discrimination of oil spill compounds in the water from the actual site of oil spill events are divided into four groups viz. diesel, Heavy Fuel Oil (HFO), Mixture Oil containing Light Fuel Oil (MOLFO) and Waste Oil (WO) according to the similarity of their intrinsic chemical properties. Principal component analysis (PCA) demonstrates that diesel, HFO, MOLFO and WO are types of oil or oil products from complex oil mixtures with a total variance of 85.34% and are identified with various anthropogenic activities related to either intentional releasing of oil or accidental discharge of oil into the environment. Our results show that the use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution in Malaysia. This, in consequence would result in cost and time saving in identification of the oil spill sources. Copyright © 2016. Published by Elsevier Ltd.

The Use of Raman Tweezers and Chemometric Analysis to Discriminate the Urological Cell Lines, PC-3, LNCaP, BPH and MGH-U1

NASA Astrophysics Data System (ADS)

Harvey, T. J.; Hughes, C.; Ward, A. D.; Gazi, E.; Faria, E. Correia; Clarke, N. W.; Brown, M.; Snook, R.; Gardner, P.

2008-11-01

Here we report on investigations into using Raman optical tweezers to analyse both live and chemically fixed prostate and bladder cells. Spectra were subjected to chemometric analysis to discriminate and classify the cell types based on their spectra. Subsequent results revealed the potential of Raman tweezers as a potential clinical diagnostic tool.

Differentiation of fresh and frozen-thawed fish samples using Raman spectroscopy coupled with chemometric analysis.

PubMed

Velioğlu, Hasan Murat; Temiz, Havva Tümay; Boyaci, Ismail Hakki

2015-04-01

The potential of Raman spectroscopy was investigated in terms of its capability to discriminate the species of the fish samples and determine their freshness according to the number of freezing/thawing cycles they exposed. Species discrimination analysis was carried out on sixty-four fish samples from six different species, namely horse mackerel (Trachurus trachurus), European anchovy (Engraulis encrasicolus), red mullet (Mullus surmuletus), Bluefish (Pomatamus saltatrix), Atlantic salmon (Salmo salar) and flying gurnard (Trigla lucerna). Afterwards, fish samples were exposed to different numbers of freezing/thawing cycles and separated into three batches, namely (i) fresh, (ii) once frozen-thawed (OF) and (iii) twice frozen-thawed (TF) samples, in order to perform the freshness analysis. Raman data collected were used as inputs for chemometric analysis, which enabled us to develop two main PCA models to successfully terminate the studies for both species discrimination and freshness determination analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques.

PubMed

Ismail, Azimah; Toriman, Mohd Ekhwan; Juahir, Hafizan; Zain, Sharifuddin Md; Habir, Nur Liyana Abdul; Retnam, Ananthy; Kamaruddin, Mohd Khairul Amri; Umar, Roslan; Azid, Azman

2016-05-15

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time. The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chemometric Analysis of Multicomponent Biodegradable Plastics by Fourier Transform Infrared Spectrometry: The R-Matrix Method

USDA-ARS?s Scientific Manuscript database

A new chemometric method based on absorbance ratios from Fourier transform infrared spectra was devised to analyze multicomponent biodegradable plastics. The method uses the BeerLambert law to directly compute individual component concentrations and weight losses before and after biodegradation of c...

Provenance Establishment of Stingless Bee Honey Using Multi-element Analysis in Combination with Chemometrics Techniques.

PubMed

Shadan, Aidil Fahmi; Mahat, Naji A; Wan Ibrahim, Wan Aini; Ariffin, Zaiton; Ismail, Dzulkiflee

2018-01-01

As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location-specific, multi-element distribution patterns may prove useful for provenancing such product. Using the inductively coupled-plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi-elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross-validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi-element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported. © 2017 American Academy of Forensic Sciences.

Chemometric analysis for extraction of individual fluorescence spectrum and lifetimes from a target mixture

NASA Technical Reports Server (NTRS)

Hallidy, William H. (Inventor); Chin, Robert C. (Inventor)

1999-01-01

The present invention is a system for chemometric analysis for the extraction of the individual component fluorescence spectra and fluorescence lifetimes from a target mixture. The present invention combines a processor with an apparatus for generating an excitation signal to transmit at a target mixture and an apparatus for detecting the emitted signal from the target mixture. The present invention extracts the individual fluorescence spectrum and fluorescence lifetime measurements from the frequency and wavelength data acquired from the emitted signal. The present invention uses an iterative solution that first requires the initialization of several decision variables and the initial approximation determinations of intermediate matrices. The iterative solution compares the decision variables for convergence to see if further approximation determinations are necessary. If the solution converges, the present invention then determines the reduced best fit error for the analysis of the individual fluorescence lifetime and the fluorescence spectrum before extracting the individual fluorescence lifetime and fluorescence spectrum from the emitted signal of the target mixture.

A brief understanding of process optimisation in microwave-assisted extraction of botanical materials: options and opportunities with chemometric tools.

PubMed

Das, Anup Kumar; Mandal, Vivekananda; Mandal, Subhash C

2014-01-01

Extraction forms the very basic step in research on natural products for drug discovery. A poorly optimised and planned extraction methodology can jeopardise the entire mission. To provide a vivid picture of different chemometric tools and planning for process optimisation and method development in extraction of botanical material, with emphasis on microwave-assisted extraction (MAE) of botanical material. A review of studies involving the application of chemometric tools in combination with MAE of botanical materials was undertaken in order to discover what the significant extraction factors were. Optimising a response by fine-tuning those factors, experimental design or statistical design of experiment (DoE), which is a core area of study in chemometrics, was then used for statistical analysis and interpretations. In this review a brief explanation of the different aspects and methodologies related to MAE of botanical materials that were subjected to experimental design, along with some general chemometric tools and the steps involved in the practice of MAE, are presented. A detailed study on various factors and responses involved in the optimisation is also presented. This article will assist in obtaining a better insight into the chemometric strategies of process optimisation and method development, which will in turn improve the decision-making process in selecting influential extraction parameters. Copyright © 2013 John Wiley & Sons, Ltd.

Early detection of emerging street drugs by near infrared spectroscopy and chemometrics.

PubMed

Risoluti, R; Materazzi, S; Gregori, A; Ripani, L

2016-06-01

Near-infrared spectroscopy (NIRs) is spreading as the tool of choice for fast and non-destructive analysis and detection of different compounds in complex matrices. This paper investigated the feasibility of using near infrared (NIR) spectroscopy coupled to chemometrics calibration to detect new psychoactive substances in street samples. The capabilities of this approach in forensic chemistry were assessed in the determination of new molecules appeared in the illicit market and often claimed to contain "non-illegal" compounds, although exhibiting important psychoactive effects. The study focused on synthetic molecules belonging to the classes of synthetic cannabinoids and phenethylamines. The approach was validated comparing results with officials methods and has been successfully applied for "in site" determination of illicit drugs in confiscated real samples, in cooperation with the Scientific Investigation Department (Carabinieri-RIS) of Rome. The achieved results allow to consider NIR spectroscopy analysis followed by chemometrics as a fast, cost-effective and useful tool for the preliminary determination of new psychoactive substances in forensic science. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemometrics and the identification of counterfeit medicines-A review.

PubMed

Krakowska, B; Custers, D; Deconinck, E; Daszykowski, M

2016-08-05

This review article provides readers with a number of actual case studies dealing with verifying the authenticity of selected medicines supported by different chemometric approaches. In particular, a general data processing workflow is discussed with the major emphasis on the most frequently selected instrumental techniques to characterize drug samples and the chemometric methods being used to explore and/or model the analytical data. However, further discussion is limited to a situation in which the collected data describes two groups of drug samples - authentic ones and counterfeits. Copyright © 2016 Elsevier B.V. All rights reserved.

[Study on rapid determination and analysis of rocket kerosene by near infrared spectrum and chemometrics].

PubMed

Xia, Ben-Li; Cong, Ji-Xin; Li, Xia; Wang, Xuan-Jun

2011-06-01

The rocket kerosene quality properties such as density, distillation range, viscosity and iodine value were successfully measured based on their near-infrared spectrum (NIRS) and chemometrics. In the present paper, more than 70 rocket kerosene samples were determined by near infrared spectrum, the models were built using the partial least squares method within the appropriate wavelength range. The correlation coefficients (R2) of every rocket kerosene's quality properties ranged from 0.862 to 0.999. Ten unknown samples were determined with the model, and the result showed that the prediction accuracy of near infrared spectrum method accords with standard analysis requirements. The new method is well suitable for replacing the traditional standard method to rapidly determine the properties of the rocket kerosene.

Dataset of Fourier transform-infrared coupled with chemometric analysis used to distinguish accessions of Garcinia mangostana L. in Peninsular Malaysia.

PubMed

Samsir, Sri A'jilah; Bunawan, Hamidun; Yen, Choong Chee; Noor, Normah Mohd

2016-09-01

In this dataset, we distinguish 15 accessions of Garcinia mangostana from Peninsular Malaysia using Fourier transform-infrared spectroscopy coupled with chemometric analysis. We found that the position and intensity of characteristic peaks at 3600-3100 cm(-) (1) in IR spectra allowed discrimination of G. mangostana from different locations. Further principal component analysis (PCA) of all the accessions suggests the two main clusters were formed: samples from Johor, Melaka, and Negeri Sembilan (South) were clustered together in one group while samples from Perak, Kedah, Penang, Selangor, Kelantan, and Terengganu (North and East Coast) were in another clustered group.

Rapid qualitative and quantitative analysis of opiates in extract of poppy head via FTIR and chemometrics: towards in-field sensors.

PubMed

Turner, Nicholas W; Cauchi, Michael; Piletska, Elena V; Preston, Christopher; Piletsky, Sergey A

2009-07-15

Identification and quantification of the opiates morphine and thebaine has been achieved in three commercial poppy cultivars using FTIR-ATR spectroscopy, from a simple and rapid methanolic extraction, suitable for field analysis. The limits of detection were 0.13 mg/ml (0.013%, w/v) and 0.3 mg/ml (0.03%, w/v) respectively. The concentrations of opiates present were verified with HPLC-MS. The chemometrics has been used to identify specific "signature" peaks in the poppy IR spectra for characterisation of cultivar by its unique fingerprint offering a potential forensic application in opiate crop analysis.

Chemometric Analysis of Bacterial Peptidoglycan Reveals Atypical Modifications That Empower the Cell Wall against Predatory Enzymes and Fly Innate Immunity.

PubMed

Espaillat, Akbar; Forsmo, Oskar; El Biari, Khouzaima; Björk, Rafael; Lemaitre, Bruno; Trygg, Johan; Cañada, Francisco Javier; de Pedro, Miguel A; Cava, Felipe

2016-07-27

Peptidoglycan is a fundamental structure for most bacteria. It contributes to the cell morphology and provides cell wall integrity against environmental insults. While several studies have reported a significant degree of variability in the chemical composition and organization of peptidoglycan in the domain Bacteria, the real diversity of this polymer is far from fully explored. This work exploits rapid ultraperformance liquid chromatography and multivariate data analysis to uncover peptidoglycan chemical diversity in the Class Alphaproteobacteria, a group of Gram negative bacteria that are highly heterogeneous in terms of metabolism, morphology and life-styles. Indeed, chemometric analyses revealed novel peptidoglycan structures conserved in Acetobacteria: amidation at the α-(l)-carboxyl of meso-diaminopimelic acid and the presence of muropeptides cross-linked by (1-3) l-Ala-d-(meso)-diaminopimelate cross-links. Both structures are growth-controlled modifications that influence sensitivity to Type VI secretion system peptidoglycan endopeptidases and recognition by the Drosophila innate immune system, suggesting relevant roles in the environmental adaptability of these bacteria. Collectively our findings demonstrate the discriminative power of chemometric tools on large cell wall-chromatographic data sets to discover novel peptidoglycan structural properties in bacteria.

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.

Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

PubMed

Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

2017-04-15

HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.

GC-FID coupled with chemometrics for quantitative and chemical fingerprinting analysis of Alpinia oxyphylla oil.

PubMed

Miao, Qing; Kong, Weijun; Zhao, Xiangsheng; Yang, Shihai; Yang, Meihua

2015-01-01

Analytical methods for quantitative analysis and chemical fingerprinting of volatile oils from Alpinia oxyphylla were established. The volatile oils were prepared by hydrodistillation, and the yields were between 0.82% and 1.33%. The developed gas chromatography-flame ionization detection (GC-FID) method showed good specificity, linearity, reproducibility, stability and recovery, and could be used satisfactorily for quantitative analysis. The results showed that the volatile oils contained 2.31-77.30 μL/mL p-cymene and 12.38-99.34 mg/mL nootkatone. A GC-FID fingerprinting method was established, and the profiles were analyzed using chemometrics. GC-MS was used to identify the principal compounds in the GC-FID profiles. The profiles of almost all the samples were consistent and stable. The harvesting time and source were major factors that affected the profile, while the volatile oil yield and the nootkatone content had minor secondary effects. Copyright © 2014 Elsevier B.V. All rights reserved.

The spectral analysis of fuel oils using terahertz radiation and chemometric methods

NASA Astrophysics Data System (ADS)

Zhan, Honglei; Zhao, Kun; Zhao, Hui; Li, Qian; Zhu, Shouming; Xiao, Lizhi

2016-10-01

The combustion characteristics of fuel oils are closely related to both engine efficiency and pollutant emissions, and the analysis of oils and their additives is thus important. These oils and additives have been found to generate distinct responses to terahertz (THz) radiation as the result of various molecular vibrational modes. In the present work, THz spectroscopy was employed to identify a number of oils, including lubricants, gasoline and diesel, with different additives. The identities of dozens of these oils could be readily established using statistical models based on principal component analysis. The THz spectra of gasoline, diesel, sulfur and methyl methacrylate (MMA) were acquired and linear fittings were obtained. By using chemometric methods, including back propagation, artificial neural network and support vector machine techniques, typical concentrations of sulfur in gasoline (ppm-grade) could be detected, together with MMA in diesel below 0.5%. The absorption characteristics of the oil additives were also assessed using 2D correlation spectroscopy, and several hidden absorption peaks were discovered. The technique discussed herein should provide a useful new means of analyzing fuel oils with various additives and impurities in a non-destructive manner and therefore will be of benefit to the field of chemical detection and identification.

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.

Micro-Raman spectroscopy and chemometrical analysis for the distinction of copper phthalocyanine polymorphs in paint layers.

PubMed

Defeyt, C; Van Pevenage, J; Moens, L; Strivay, D; Vandenabeele, P

2013-11-01

In art analysis, copper phthalocyanine (CuPc) is often identified as an important pigment (PB15) in 20th century artworks. Raman spectroscopy is a very valuable technique for the detection of this pigment in paint systems. However, PB15 is used in different polymorphic forms and identification of the polymorph could retrieve information on the production process of the pigment at the moment. Raman spectroscopy, being a molecular spectroscopic method of analysis, is able to discriminate between polymorphs of crystals. However, in the case of PB15, spectral interpretation is not straightforward, and Raman data treatment requires some improvements concerning the PB15 polymorphic discrimination in paints. Here, Raman spectroscopy is combined with chemometrical analysis in order to develop a procedure allowing us to identify the PB15 crystalline structure in painted layers and in artworks. The results obtained by Linear Discriminant Analysis (LDA), using intensity ratios as variables, demonstrate the ability of this procedure to predict the crystalline structure of a PB15 pigment in unknown paint samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Diagnosis of oral lichen planus from analysis of saliva samples using terahertz time-domain spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Borisov, Alexey V.; Titarenko, Maria A.; Baydik, Olga D.; Shapovalov, Alexander V.

2018-04-01

The ability to diagnose oral lichen planus (OLP) based on saliva analysis using THz time-domain spectroscopy and chemometrics is discussed. The study involved 30 patients (2 male and 28 female) with OLP. This group consisted of two subgroups with the erosive form of OLP (n = 15) and with the reticular and papular forms of OLP (n = 15). The control group consisted of six healthy volunteers (one male and five females) without inflammation in the mucous membrane in the oral cavity and without periodontitis. Principal component analysis was used to reveal informative features in the experimental data. The one-versus-one multiclass classifier using support vector machine binary classifiers was used. The two-stage classification approach using several absorption spectra scans for an individual saliva sample provided 100% accuracy of differential classification between OLP subgroups and control group.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

PubMed

Martyna, Agnieszka; Zadora, Grzegorz; Neocleous, Tereza; Michalska, Aleksandra; Dean, Nema

2016-08-10

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. Copyright © 2016 Elsevier B.V. All rights reserved.

Metals and organic compounds in the biosynthesis of cannabinoids: a chemometric approach to the analysis of Cannabis sativa samples.

PubMed

Radosavljevic-Stevanovic, Natasa; Markovic, Jelena; Agatonovic-Kustrin, Snezana; Razic, Slavica

2014-01-01

Illicit production and trade of Cannabis sativa affect many societies. This drug is the most popular and easy to produce. Important information for the authorities is the production locality and the indicators of a particular production. This work is an attempt to recognise correlations between the metal content in the different parts of C. sativa L., in soils where plants were cultivated and the cannabinoids content, as a potential indicator. The organic fraction of the leaves of Cannabis plants was investigated by GC-FID analysis. In addition, the determination of Cu, Fe, Cr, Mn, Zn, Ca and Mg was realised by spectroscopic techniques (FAAS and GFAAS). In this study, numerous correlations between metal content in plants and soil, already confirmed in previous publications, were analysed applying chemometric unsupervised methods, that is, principal component analysis, factor analysis and cluster analysis, in order to highlight their role in the biosynthesis of cannabinoids.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics.

PubMed

Zhang, Chu; Shen, Tingting; Liu, Fei; He, Yong

2017-12-31

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

PubMed Central

Zhang, Chu; Shen, Tingting

2017-01-01

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied. PMID:29301228

Chemometric analysis of minerals and trace elements in Sicilian wines from two different grape cultivars.

PubMed

Potortί, Angela Giorgia; Lo Turco, Vincenzo; Saitta, Marcello; Bua, Giuseppe Daniel; Tropea, Alessia; Dugo, Giacomo; Di Bella, Giuseppa

2017-05-01

Chemometric analysis are used for food authenticity evaluation, correlating botanical and geographical origins with food chemical composition. This research was carried out in order to prove that it is possible linked red wines to Nero d'Avola and Syrah cultivars of Vitis vinifera according to their mineral content, while the values of the physical and chemical parameters do not affect relevantly this discrimination. The levels of mineral elements were determined by ICP-OES and ICP-MS. Samples from cv Nero d'Avola had the highest content of Zn, Cr, Ni, As and Cd, whereas the highest mineral concentration in cv Syrah samples was represented by K, Mg, Cu, and Sb. The research highlights that it is possible linked red wines to Nero d'Avola and Syrah cultivars of V. vinifera according to their mineral contents, adding knowledge to the determination studies of the wine botanical origin.

Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef.

PubMed

Zhao, Ming; Nian, Yingqun; Allen, Paul; Downey, Gerard; Kerry, Joseph P; O'Donnell, Colm P

2018-05-01

This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples (n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300-2800 cm -1 . Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (R 2 CV values of 0.50-0.84 and RMSECV values of 1.31-9.07) and were particularly high for desirable flavour attributes (R 2 CVs of 0.80-0.84, RMSECVs of 4.21-4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or breed type, significant improvements on prediction performances were achieved for overall sensory attributes (R 2 CVs of 0.63-0.89 and RMSECVs of 0.38-6.88 for each breed type; R 2 CVs of 0.52-0.89 and RMSECVs of 0.96-6.36 for each age group). Chemometric analysis revealed strong correlations between sensory attributes. Raman spectroscopy combined with chemometric analysis was demonstrated to have high potential as a rapid and non-destructive technique to predict the sensory quality traits of young dairy bull beef. Copyright © 2018. Published by Elsevier Ltd.

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.

Fourier transform infrared spectroscopy and chemometrics for the characterization and discrimination of writing/photocopier paper types: Application in forensic document examinations

NASA Astrophysics Data System (ADS)

Kumar, Raj; Kumar, Vinay; Sharma, Vishal

2017-01-01

The aim of the present work is to explore the non-destructive application of ATR-FTIR technique for characterization and discrimination of paper samples which could be helpful to give forensic aid in resolving legal cases. Twenty-four types of paper brands were purchased from local market in and around Chandigarh, India. All the paper samples were subjected to ATR-FTIR analysis from 400 to 4000 cm- 1 wavenumber range. The qualitative feature and Chemometrics of the obtained spectral data are used for characterization and discrimination. Characterization is achieved by matching the peaks with standards of cellulose and inorganic fillers, a usual constituents of paper. Three different regions of IR, i.e. 400-2000 cm- 1, 2000-4000 cm- 1 and 400-4000 cm- 1 were selected for differentiation by Chemometrics analysis. The discrimination is achieved on the basis of three principal components, i.e. PC 1, PC 2 and PC 3. It is observed that maximum discrimination was procured in the wave number range of i.e. 2000-4000 cm- 1. Discriminating power was calculated on the basis of qualitative features as well, and it is found that the discrimination of paper samples was better achieved by Chemometrics analysis rather than qualitative features. The discriminating power by Chemometrics is 99.64% and which is larger as ever achieved by any group for present number of samples. The present result confirms that this study will be highly useful in forensic document examination work in the legal cases, where the authenticity of the document is challenged. The results are completely analytical and, therefore, overcome the problem encounter in traditional routine light/radiation scanning methods which are still in practice by various questioned document laboratories.

Fourier transform infrared spectroscopy and chemometrics for the characterization and discrimination of writing/photocopier paper types: Application in forensic document examinations.

PubMed

Kumar, Raj; Kumar, Vinay; Sharma, Vishal

2017-01-05

The aim of the present work is to explore the non-destructive application of ATR-FTIR technique for characterization and discrimination of paper samples which could be helpful to give forensic aid in resolving legal cases. Twenty-four types of paper brands were purchased from local market in and around Chandigarh, India. All the paper samples were subjected to ATR-FTIR analysis from 400 to 4000cm(-1) wavenumber range. The qualitative feature and Chemometrics of the obtained spectral data are used for characterization and discrimination. Characterization is achieved by matching the peaks with standards of cellulose and inorganic fillers, a usual constituents of paper. Three different regions of IR, i.e. 400-2000cm(-1), 2000-4000cm(-1) and 400-4000cm(-1) were selected for differentiation by Chemometrics analysis. The discrimination is achieved on the basis of three principal components, i.e. PC 1, PC 2 and PC 3. It is observed that maximum discrimination was procured in the wave number range of i.e. 2000-4000cm(-1). Discriminating power was calculated on the basis of qualitative features as well, and it is found that the discrimination of paper samples was better achieved by Chemometrics analysis rather than qualitative features. The discriminating power by Chemometrics is 99.64% and which is larger as ever achieved by any group for present number of samples. The present result confirms that this study will be highly useful in forensic document examination work in the legal cases, where the authenticity of the document is challenged. The results are completely analytical and, therefore, overcome the problem encounter in traditional routine light/radiation scanning methods which are still in practice by various questioned document laboratories. Copyright © 2016 Elsevier B.V. All rights reserved.

Selection of quantum chemical descriptors by chemometric methods in the study of antioxidant activity of flavonoid compounds

NASA Astrophysics Data System (ADS)

Weber, K. C.; Honório, K. M.; da Silva, S. L.; Mercadante, R.; da Silva, A. B. F.

In the present study, the aim was to select electronic properties responsible for free radical scavenging ability of a set of 25 flavonoid compounds employing chemometric methods. Electronic parameters were calculated using the AM1 semiempirical method, and chemometric methods (principal component analysis, hierarchical cluster analysis, and k-nearest neighbor) were used with the aim to build models able to find relationships between electronic features and the antioxidant activity presented by the compounds studied. According to these models, four electronic variables can be considered important to discriminate more and less antioxidant flavonoid compounds: polarizability (α), charge at carbon 3 (QC3), total charge at substituent 5 (QS5), and total charge at substituent 3' (QS3'). The features found as being responsible for the antioxidant activity of the flavonoid compounds studied are consistent with previous results found in the literature. The results obtained can also bring improvements in the search for better antioxidant flavonoid compounds.

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

Differentiation of whole grain and refined wheat (T. aestivum) flour using a fuzzy mass spectrometric fingerprinting and chemometric approaches

USDA-ARS?s Scientific Manuscript database

A fuzzy mass spectrometric (MS) fingerprinting method combined with chemometric analysis was established to provide rapid discrimination between whole grain and refined wheat flour. Twenty one samples, including thirteen samples from three cultivars and eight from local grocery store, were studied....

Using of laser spectroscopy and chemometrics methods for identification of patients with lung cancer, patients with COPD and healthy people from absorption spectra of exhaled air

NASA Astrophysics Data System (ADS)

Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kistenev, Yury V.; Kuzmin, Dmitry A.; Nikiforova, Olga Yu.; Ponomarev, Yurii N.; Tuzikov, Sergei A.; Yumov, Evgeny L.

2014-11-01

The results of application of the joint use of laser photoacoustic spectroscopy and chemometrics methods in gas analysis of exhaled air of patients with chronic respiratory diseases (chronic obstructive pulmonary disease and lung cancer) are presented. The absorption spectra of exhaled breath of representatives of the target groups and healthy volunteers were measured; the selection by chemometrics methods of the most informative absorption coefficients in scan spectra in terms of the separation investigated nosology was implemented.

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural

Metabolomic differentiation of maca (Lepidium meyenii) accessions cultivated under different conditions using NMR and chemometric analysis.

PubMed

Zhao, Jianping; Avula, Bharathi; Chan, Michael; Clément, Céline; Kreuzer, Michael; Khan, Ikhlas A

2012-01-01

To gain insights on the effects of color type, cultivation history, and growing site on the composition alterations of maca (Lepidium meyenii Walpers) hypocotyls, NMR profiling combined with chemometric analysis was applied to investigate the metabolite variability in different maca accessions. Maca hypocotyls with different colors (yellow, pink, violet, and lead-colored) cultivated at different geographic sites and different areas were examined for differences in metabolite expression. Differentiations of the maca accessions grown under the different cultivation conditions were determined by principle component analyses (PCAs) which were performed on the datasets derived from their ¹H NMR spectra. A total of 16 metabolites were identified by NMR analysis, and the changes in metabolite levels in relation to the color types and growing conditions of maca hypocotyls were evaluated using univariate statistical analysis. In addition, the changes of the correlation pattern among the metabolites identified in the maca accessions planted at the two different sites were examined. The results from both multivariate and univariate analysis indicated that the planting site was the major determining factor with regards to metabolite variations in maca hypocotyls, while the color of maca accession seems to be of minor importance in this respect. © Georg Thieme Verlag KG Stuttgart · New York.

Identification of Three Kinds of Citri Reticulatae Pericarpium Based on Deoxyribonucleic Acid Barcoding and High-performance Liquid Chromatography-diode Array Detection-electrospray Ionization/Mass Spectrometry/Mass Spectrometry Combined with Chemometric Analysis

PubMed Central

Yu, Xiaoxue; Zhang, Yafeng; Wang, Dongmei; Jiang, Lin; Xu, Xinjun

2018-01-01

Background: Citri Reticulatae Pericarpium is the dried mature pericarp of Citrus reticulata Blanco which can be divided into “Chenpi” and “Guangchenpi.” “Guangchenpi” is the genuine Chinese medicinal material in Xinhui, Guangdong province; based on the greatest quality and least amount, it is most expensive among others. Hesperidin is used as the marker to identify Citri Reticulatae Pericarpium described in the Chinese Pharmacopoeia 2010. However, both “Chenpi” and “Guangchenpi” contain hesperidin so that it is impossible to differentiate them by measuring hesperidin. Objective: Our study aims to develop an efficient and accurate method to separate and identify “Guangchenpi” from other Citri Reticulatae Pericarpium. Materials and Methods: The genomic deoxyribonucleic acid (DNA) of all the materials was extracted and then the internal transcribed spacer 2 was amplified, sequenced, aligned, and analyzed. The secondary structures were created in terms of the database and website established by Jörg Schultz et al. High-performance liquid chromatography-diode array detection-electrospray Ionization/mass spectrometry (HPLC-DAD-ESI-MS)/MS coupled with chemometric analysis was applied to compare the differences in chemical profiles of the three kinds of Citri Reticulatae Pericarpium. Results: A total of 22 samples were classified into three groups. The results of DNA barcoding were in accordance with principal component analysis and hierarchical cluster analysis. Eight compounds were deduced from HPLC-DAD-ESI-MS/MS. Conclusions: This method is a reliable and effective tool to differentiate the three Citri Reticulatae Pericarpium. SUMMARY The internal transcribed spacer 2 regions and the secondary structure among three kinds of Citri Reticulatae Pericarpium varied considerablyAll the 22 samples were analyzed by high-performance liquid chromatography (HPLC) to obtain the chemical profilesPrincipal component analysis and hierarchical cluster analysis

Chemometric analysis of voltammetric data on metal ion binding by selenocystine.

PubMed

Gusmão, Rui; Díaz-Cruz, José Manuel; Ariño, Cristina; Esteban, Miquel

2012-06-28

The behavior of selenocystine (SeCyst) alone or in the presence of various metal ions (Bi(3+), Cd(2+), Co(2+), Cu(2+), Cr(3+), Ni(2+), Pb(2+), and Zn(2+)) was studied using differential pulse voltammetry (DPV) over a wide pH range. Voltammetric data matrices were analyzed using chemometric tools recently developed for nonlinear data: pHfit and Gaussian Peak Adjustment (GPA). Under the experimental conditions tested, no evidence was found for the formation of metal complexes with Bi(3+), Cu(2+), Cr(3+), and Pb(2+). In contrast, SeCyst formed electroinactive complexes with Co(2+) and Ni(2+) and kinetically inert but electroactive complexes with Cd(2+) and Zn(2+). Titrations with Cd(2+), Co(2+), Ni(2+), and Zn(2+) produced data that were reasonably consistent with the formation of stable 1:1 M(SeCyst) complexes.

Chemometric comparison of polychlorinated biphenyl residues and toxicologically active polychlorinated biphenyl congeners in the eggs of Forster's Terns (Sterna fosteri)

USGS Publications Warehouse

Schwartz, Ted R.; Stalling, David L.

1991-01-01

The separation and characterization of complex mixtures of polychlorinated biphenyls (PCBs) is approached from the perspective of a problem in chemometrics. A technique for quantitative determination of PCB congeners is described as well as an enrichment technique designed to isolate only those congener residues which induce mixed aryl hydrocarbon hydroxylase enzyme activity. A congener-specific procedure is utilized for the determination of PCBs in whichn-alkyl trichloroacetates are used as retention index marker compounds. Retention indices are reproducible in the range of ±0.05 to ±0.7 depending on the specific congener. A laboratory data base system developed to aid in the editing and quantitation of data generated from capillary gas chromatography was employed to quantitate chromatographic data. Data base management was provided by computer programs written in VAX-DSM (Digital Standard MUMPS) for the VAX-DEC (Digital Equipment Corp.) family of computers.In the chemometric evaluation of these complex chromatographic profiles, data are viewed from a single analysis as a point in multi-dimensional space. Principal Components Analysis was used to obtain a representation of the data in a lower dimensional space. Two-and three-dimensional proections based on sample scores from the principal components models were used to visualize the behavior of Aroclor® mixtures. These models can be used to determine if new sample profiles may be represented by Aroclor profiles. Concentrations of individual congeners of a given chlorine substitution may be summed to form homologue concentration. However, the use of homologue concentrations in classification studies with environmental samples can lead to erroneous conclusions about sample similarity. Chemometric applications are discussed for evaluation of Aroclor mixture analysis and compositional description of environmental residues of PCBs in eggs of Forster's terns (Sterna fosteri) collected from colonies near Lake Poygan

Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules

NASA Astrophysics Data System (ADS)

Cramer, Christopher J.; Johnson, Joshua L.; Kamel, Amin M.

2017-02-01

A method is developed for the prediction of mass spectral ion counts of drug-like molecules using in silico calculated chemometric data. Various chemometric data, including polar and molecular surface areas, aqueous solvation free energies, and gas-phase and aqueous proton affinities were computed, and a statistically significant relationship between measured mass spectral ion counts and the combination of aqueous proton affinity and total molecular surface area was identified. In particular, through multilinear regression of ion counts on predicted chemometric data, we find that log10(MS ion counts) = -4.824 + c 1•PA + c 2•SA, where PA is the aqueous proton affinity of the molecule computed at the SMD(aq)/M06-L/MIDI!//M06-L/MIDI! level of electronic structure theory, SA is the total surface area of the molecule in its conjugate base form, and c 1 and c 2 have values of -3.912 × 10-2 mol kcal-1 and 3.682 × 10-3 Å-2. On a 66-molecule training set, this regression exhibits a multiple R value of 0.791 with p values for the intercept, c 1, and c 2 of 1.4 × 10-3, 4.3 × 10-10, and 2.5 × 10-6, respectively. Application of this regression to an 11-molecule test set provides a good correlation of prediction with experiment ( R = 0.905) albeit with a systematic underestimation of about 0.2 log units. This method may prove useful for semiquantitative analysis of drug metabolites for which MS response factors or authentic standards are not readily available.

Simultaneous spectrophotometric determination of glimepiride and pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques

NASA Astrophysics Data System (ADS)

El-Zaher, Asmaa A.; Elkady, Ehab F.; Elwy, Hanan M.; Saleh, Mahmoud Abo El Makarim

2017-07-01

In the present work, pioglitazone and glimepiride, 2 widely used antidiabetics, were simultaneously determined by a chemometric-assisted UV-spectrophotometric method which was applied to a binary synthetic mixture and a pharmaceutical preparation containing both drugs. Three chemometric techniques - Concentration residual augmented classical least-squares (CRACLS), principal component regression (PCR), and partial least-squares (PLS) were implemented by using the synthetic mixtures containing the two drugs in acetonitrile. The absorbance data matrix corresponding to the concentration data matrix was obtained by the measurements of absorbencies in the range between 215 and 235 nm in the intervals with Δλ = 0.4 nm in their zero-order spectra. Then, calibration or regression was obtained by using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of pioglitazone and glimepiride in their mixtures. The described techniques have been validated by analyzing synthetic mixtures containing the two drugs showing good mean recovery values lying between 98 and 100%. In addition, accuracy and precision of the three methods have been assured by recovery values lying between 98 and 102% and R.S.D. % ˂0.6 for intra-day precision and ˂1.2 for inter-day precision. The proposed chemometric techniques were successfully applied to a pharmaceutical preparation containing a combination of pioglitazone and glimepiride in the ratio of 30: 4, showing good recovery values. Finally, statistical analysis was carried out to add a value to the verification of the proposed methods. It was carried out by an intrinsic comparison between the 3 chemometric techniques and by comparing values of present methods with those obtained by implementing reference pharmacopeial methods for each of pioglitazone and glimepiride.

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.

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks.

PubMed

Özbalci, Beril; Boyaci, İsmail Hakkı; Topcu, Ali; Kadılar, Cem; Tamer, Uğur

2013-02-15

The aim of this study was to quantify glucose, fructose, sucrose and maltose contents of honey samples using Raman spectroscopy as a rapid method. By performing a single measurement, quantifications of sugar contents have been said to be unaffordable according to the molecular similarities between sugar molecules in honey matrix. This bottleneck was overcome by coupling Raman spectroscopy with chemometric methods (principal component analysis (PCA) and partial least squares (PLS)) and an artificial neural network (ANN). Model solutions of four sugars were processed with PCA and significant separation was observed. This operation, done with the spectral features by using PLS and ANN methods, led to the discriminant analysis of sugar contents. Models/trained networks were created using a calibration data set and evaluated using a validation data set. The correlation coefficient values between actual and predicted values of glucose, fructose, sucrose and maltose were determined as 0.964, 0.965, 0.968 and 0.949 for PLS and 0.965, 0.965, 0.978 and 0.956 for ANN, respectively. The requirement of rapid analysis of sugar contents of commercial honeys has been met by the data processed within this article. Copyright © 2012 Elsevier Ltd. All rights reserved.

Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools.

PubMed

Vaclavik, Lukas; Hrbek, Vojtech; Cajka, Tomas; Rohlik, Bo-Anne; Pipek, Petr; Hajslova, Jana

2011-06-08

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.

The employment of FTIR spectroscopy in combination with chemometrics for analysis of rat meat in meatball formulation.

PubMed

Rahmania, Halida; Sudjadi; Rohman, Abdul

2015-02-01

For Indonesian community, meatball is one of the favorite meat food products. In order to gain economical benefits, the substitution of beef meat with rat meat can happen due to the different prices between rat meat and beef. In this present research, the feasibility of FTIR spectroscopy in combination with multivariate calibration of partial least square (PLS) was used for the quantitative analysis of rat meat in the binary mixture of beef in meatball formulation. Meanwhile, the chemometrics of principal component analysis (PCA) was used for the classification between rat meat and beef meatballs. Some frequency regions in mid infrared region were optimized, and finally, the frequency region of 750-1000 cm(-1) was selected during PLS and PCA modeling.For quantitative analysis, the relationship between actual values (x-axis) and FTIR predicted values (y-axis) of rat meat is described by the equation of y= 0.9417x+ 2.8410 with coefficient of determination (R2) of 0.993, and root mean square error of calibration (RMSEC) of 1.79%. Furthermore, PCA was successfully used for the classification of rat meat meatball and beef meatball.

Phytochemical diversity of cranberry (Vaccinium macrocarpon Aiton) cultivars by anthocyanin determination and metabolomic profiling with chemometric analysis.

PubMed

Brown, Paula N; Murch, Susan J; Shipley, Paul

2012-01-11

Originally native to the eastern United States, American cranberry ( Vaccinium macrocarpon Aiton, family Ericaceae) cultivation of native and hybrid varieties has spread across North America. Herein is reported the phytochemical diversity of five cranberry cultivars (Stevens, Ben Lear, Bergman, Pilgrim, and GH1) collected in the Greater Vancouver Regional District, by anthocyanin content and UPLC-TOF-MS metabolomic profiling. The anthocyanin content for biological replicates (n = 5) was determined as 7.98 ± 5.83, Ben Lear; 7.02 ± 1.75, Bergman; 6.05 ± 2.51, GH1; 3.28 ± 1.88, Pilgrim; and 2.81 ± 0.81, Stevens. Using subtractive metabonomic algorithms 6481 compounds were found conserved across all varietals, with 136 (Ben Lear), 84 (Bergman), 91 (GH1), 128 (Pilgrim), and 165 (Stevens) unique compounds observed. Principal component analysis (PCA) did not differentiate varieties, whereas partial least-squares discriminate analysis (PLS-DA) exhibited clustering patterns. Univariate statistical approaches were applied to the data set, establishing significance of values and assessing quality of the models. Metabolomic profiling with chemometric analysis proved to be useful for characterizing metabonomic changes across cranberry varieties.

Application of chemometric analysis based on physicochemical and chromatographic data for the differentiation origin of plant protection products containing chlorpyrifos.

PubMed

Miszczyk, Marek; Płonka, Marlena; Bober, Katarzyna; Dołowy, Małgorzata; Pyka, Alina; Pszczolińska, Klaudia

2015-01-01

The aim of this study was to investigate the similarities and dissimilarities between the pesticide samples in form of emulsifiable concentrates (EC) formulation containing chlorpyrifos as active ingredient coming from different sources (i.e., shops and wholesales) and also belonging to various series. The results obtained by the Headspace Gas Chromatography-Mass Spectrometry method and also some selected physicochemical properties of examined pesticides including pH, density, stability, active ingredient and water content in pesticides tested were compared using two chemometric methods. Applicability of simple cluster analysis and also principal component analysis of obtained data in differentiation of examined plant protection products coming from different sources was confirmed. It would be advantageous in the routine control of originality and also in the detection of counterfeit pesticides, respectively, among commercially available pesticides containing chlorpyrifos as an active ingredient.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics.

PubMed

Lubes, Giuseppe; Goodarzi, Mohammad

2017-05-10

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

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

Chemometric classification of casework arson samples based on gasoline content.

PubMed

Sinkov, Nikolai A; Sandercock, P Mark L; Harynuk, James J

2014-02-01

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2018-02-05

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

Effect of the statin therapy on biochemical laboratory tests--a chemometrics study.

PubMed

Durceková, Tatiana; Mocák, Ján; Boronová, Katarína; Balla, Ján

2011-01-05

Statins are the first-line choice for lowering total and LDL cholesterol levels and very important medicaments for reducing the risk of coronary artery disease. The aim of this study is therefore assessment of the results of biochemical tests characterizing the condition of 172 patients before and after administration of statins. For this purpose, several chemometric tools, namely principal component analysis, cluster analysis, discriminant analysis, logistic regression, KNN classification, ROC analysis, descriptive statistics and ANOVA were used. Mutual relations of 11 biochemical laboratory tests, the patient's age and gender were investigated in detail. Achieved results enable to evaluate the extent of the statin treatment in each individual case. They may also help in monitoring the dynamic progression of the disease. Copyright © 2010 Elsevier B.V. All rights reserved.

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Rapid discrimination of the causal agents of urinary tract infection using ToF-SIMS with chemometric cluster analysis

NASA Astrophysics Data System (ADS)

Fletcher, John S.; Henderson, Alexander; Jarvis, Roger M.; Lockyer, Nicholas P.; Vickerman, John C.; Goodacre, Royston

2006-07-01

Advances in time of flight secondary ion mass spectrometry (ToF-SIMS) have enabled this technique to become a powerful tool for the analysis of biological samples. Such samples are often very complex and as a result full interpretation of the acquired data can be extremely difficult. To simplify the interpretation of these information rich data, the use of chemometric techniques is becoming widespread in the ToF-SIMS community. Here we discuss the application of principal components-discriminant function analysis (PC-DFA) to the separation and classification of a number of bacterial samples that are known to be major causal agents of urinary tract infection. A large data set has been generated using three biological replicates of each isolate and three machine replicates were acquired from each biological replicate. Ordination plots generated using the PC-DFA are presented demonstrating strain level discrimination of the bacteria. The results are discussed in terms of biological differences between certain species and with reference to FT-IR, Raman spectroscopy and pyrolysis mass spectrometric studies of similar samples.

Discrimination of Clover and Citrus Honeys from Egypt According to Floral Type Using Easily Assessable Physicochemical Parameters and Discriminant Analysis: An External Validation of the Chemometric Approach.

PubMed

Karabagias, Ioannis K; Karabournioti, Sofia

2018-05-03

Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014⁻2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin ( p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone.

Discrimination of Clover and Citrus Honeys from Egypt According to Floral Type Using Easily Assessable Physicochemical Parameters and Discriminant Analysis: An External Validation of the Chemometric Approach

PubMed Central

Karabournioti, Sofia

2018-01-01

Twenty-two honey samples, namely clover and citrus honeys, were collected from the greater Cairo area during the harvesting year 2014–2015. The main purpose of the present study was to characterize the aforementioned honey types and to investigate whether the use of easily assessable physicochemical parameters, including color attributes in combination with chemometrics, could differentiate honey floral origin. Parameters taken into account were: pH, electrical conductivity, ash, free acidity, lactonic acidity, total acidity, moisture content, total sugars (degrees Brix-°Bx), total dissolved solids and their ratio to total acidity, salinity, CIELAB color parameters, along with browning index values. Results showed that all honey samples analyzed met the European quality standards set for honey and had variations in the aforementioned physicochemical parameters depending on floral origin. Application of linear discriminant analysis showed that eight physicochemical parameters, including color, could classify Egyptian honeys according to floral origin (p < 0.05). Correct classification rate was 95.5% using the original method and 90.9% using the cross validation method. The discriminatory ability of the developed model was further validated using unknown honey samples. The overall correct classification rate was not affected. Specific physicochemical parameter analysis in combination with chemometrics has the potential to enhance the differences in floral honeys produced in a given geographical zone. PMID:29751543

Differentiation of Bread Made with Whole Grain and Refined Wheat (T. aestivum) Flour Using LC/MS-based chromatographic Fingerprinting and Chemometric Approaches

USDA-ARS?s Scientific Manuscript database

A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with chemometric analysis was established to diffrentiate between whole wheat (WW) flours and refined wheat (RW) flour, and the breads made from them. The chemical compositions of the bread samples were profiled using h...

An Advanced Analytical Chemistry Experiment Using Gas Chromatography-Mass Spectrometry, MATLAB, and Chemometrics to Predict Biodiesel Blend Percent Composition

ERIC Educational Resources Information Center

Pierce, Karisa M.; Schale, Stephen P.; Le, Trang M.; Larson, Joel C.

2011-01-01

We present a laboratory experiment for an advanced analytical chemistry course where we first focus on the chemometric technique partial least-squares (PLS) analysis applied to one-dimensional (1D) total-ion-current gas chromatography-mass spectrometry (GC-TIC) separations of biodiesel blends. Then, we focus on n-way PLS (n-PLS) applied to…

Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method

NASA Astrophysics Data System (ADS)

Gurbanov, Rafig; Gozen, Ayse Gul; Severcan, Feride

2018-01-01

Rapid, cost-effective, sensitive and accurate methodologies to classify bacteria are still in the process of development. The major drawbacks of standard microbiological, molecular and immunological techniques call for the possible usage of infrared (IR) spectroscopy based supervised chemometric techniques. Previous applications of IR based chemometric methods have demonstrated outstanding findings in the classification of bacteria. Therefore, we have exploited an IR spectroscopy based chemometrics using supervised method namely Soft Independent Modeling of Class Analogy (SIMCA) technique for the first time to classify heavy metal-exposed bacteria to be used in the selection of suitable bacteria to evaluate their potential for environmental cleanup applications. Herein, we present the powerful differentiation and classification of laboratory strains (Escherichia coli and Staphylococcus aureus) and environmental isolates (Gordonia sp. and Microbacterium oxydans) of bacteria exposed to growth inhibitory concentrations of silver (Ag), cadmium (Cd) and lead (Pb). Our results demonstrated that SIMCA was able to differentiate all heavy metal-exposed and control groups from each other with 95% confidence level. Correct identification of randomly chosen test samples in their corresponding groups and high model distances between the classes were also achieved. We report, for the first time, the success of IR spectroscopy coupled with supervised chemometric technique SIMCA in classification of different bacteria under a given treatment.

Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis.

PubMed

Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni

2016-09-01

A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS-MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre-separation by creating ions outside the instrument. The present study reports a PS-MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non-volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS-MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS-MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross-validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Sequential (step-by-step) detection, identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles.

PubMed

Capote, F Priego; Jiménez, J Ruiz; de Castro, M D Luque

2007-08-01

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-03-01

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

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.

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics.

PubMed

Ni, Yongnian; Wang, Lin; Kokot, Serge

2011-01-01

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D - first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.

Chemometric analysis of soil pollution data using the Tucker N-way method.

PubMed

Stanimirova, I; Zehl, K; Massart, D L; Vander Heyden, Y; Einax, J W

2006-06-01

N-way methods, particularly the Tucker method, are often the methods of choice when analyzing data sets arranged in three- (or higher) way arrays, which is the case for most environmental data sets. In the future, applying N-way methods will become an increasingly popular way to uncover hidden information in complex data sets. The reason for this is that classical two-way approaches such as principal component analysis are not as good at revealing the complex relationships present in data sets. This study describes in detail the application of a chemometric N-way approach, namely the Tucker method, in order to evaluate the level of pollution in soil from a contaminated site. The analyzed soil data set was five-way in nature. The samples were collected at different depths (way 1) from two locations (way 2) and the levels of thirteen metals (way 3) were analyzed using a four-step-sequential extraction procedure (way 4), allowing detailed information to be obtained about the bioavailability and activity of the different binding forms of the metals. Furthermore, the measurements were performed under two conditions (way 5), inert and non-inert. The preferred Tucker model of definite complexity showed that there was no significant difference in measurements analyzed under inert or non-inert conditions. It also allowed two depth horizons, characterized by different accumulation pathways, to be distinguished, and it allowed the relationships between chemical elements and their biological activities and mobilities in the soil to be described in detail.

Simultaneous chemometric determination of pyridoxine hydrochloride and isoniazid in tablets by multivariate regression methods.

PubMed

Dinç, Erdal; Ustündağ, Ozgür; Baleanu, Dumitru

2010-08-01

The sole use of pyridoxine hydrochloride during treatment of tuberculosis gives rise to pyridoxine deficiency. Therefore, a combination of pyridoxine hydrochloride and isoniazid is used in pharmaceutical dosage form in tuberculosis treatment to reduce this side effect. In this study, two chemometric methods, partial least squares (PLS) and principal component regression (PCR), were applied to the simultaneous determination of pyridoxine (PYR) and isoniazid (ISO) in their tablets. A concentration training set comprising binary mixtures of PYR and ISO consisting of 20 different combinations were randomly prepared in 0.1 M HCl. Both multivariate calibration models were constructed using the relationships between the concentration data set (concentration data matrix) and absorbance data matrix in the spectral region 200-330 nm. The accuracy and the precision of the proposed chemometric methods were validated by analyzing synthetic mixtures containing the investigated drugs. The recovery results obtained by applying PCR and PLS calibrations to the artificial mixtures were found between 100.0 and 100.7%. Satisfactory results obtained by applying the PLS and PCR methods to both artificial and commercial samples were obtained. The results obtained in this manuscript strongly encourage us to use them for the quality control and the routine analysis of the marketing tablets containing PYR and ISO drugs. Copyright © 2010 John Wiley & Sons, Ltd.

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.

NMR spectroscopy and chemometrics to evaluate different processing of coconut water.

PubMed

Sucupira, N R; Alves Filho, E G; Silva, L M A; de Brito, E S; Wurlitzer, N J; Sousa, P H M

2017-02-01

NMR and chemometrics was applied to understand the variations in chemical composition of coconut water under different processing. Six processing treatments were applied to coconut water and analyzed: two control (with and without sulphite), and four samples thermally processed at 110°C and 136°C (with and without sulphite). Samples processed at lower temperature and without sulphite presented pink color under storage. According to chemometrics, samples processed at higher temperature exhibited lower levels of glucose and malic acid. Samples with sulphite processed at 136°C presented lower amount of sucrose, suggesting the degradation of the carbohydrates after harshest thermal treatment. Samples with sulphite and processed at lower temperature showed higher concentration of ethanol. However, no significant changes were verified in coconut water composition as a whole. Sulphite addition and the temperature processing to 136°C were effective to prevent the pinking and to maintain the levels of main organic compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

PubMed Central

Myakalwar, Ashwin Kumar; Sreedhar, S.; Barman, Ishan; Dingari, Narahara Chari; Rao, S. Venugopal; Kiran, P. Prem; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen to nitrogen compositional values yielded an optimal value (at 746.83 nm) with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry. PMID:22099648

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.

Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods

NASA Astrophysics Data System (ADS)

Glavanović, Siniša; Glavanović, Marija; Tomišić, Vladislav

2016-03-01

The UV spectrophotometric methods for simultaneous quantitative determination of paracetamol and tramadol in paracetamol-tramadol tablets were developed. The spectrophotometric data obtained were processed by means of partial least squares (PLS) and genetic algorithm coupled with PLS (GA-PLS) methods in order to determine the content of active substances in the tablets. The results gained by chemometric processing of the spectroscopic data were statistically compared with those obtained by means of validated ultra-high performance liquid chromatographic (UHPLC) method. The accuracy and precision of data obtained by the developed chemometric models were verified by analysing the synthetic mixture of drugs, and by calculating recovery as well as relative standard error (RSE). A statistically good agreement was found between the amounts of paracetamol determined using PLS and GA-PLS algorithms, and that obtained by UHPLC analysis, whereas for tramadol GA-PLS results were proven to be more reliable compared to those of PLS. The simplest and the most accurate and precise models were constructed by using the PLS method for paracetamol (mean recovery 99.5%, RSE 0.89%) and the GA-PLS method for tramadol (mean recovery 99.4%, RSE 1.69%).

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.

Chemometric evaluation of concentrations of trace elements in intervertebral disc tissue in patient with degenerative disc disease.

PubMed

Kubaszewski, Łukasz; Zioła-Frankowska, Anetta; Gasik, Zuzanna; Frankowski, Marcin; Dąbrowski, Mikołaj; Molisak, Bartłomiej; Kaczmarczyk, Jacek; Gasik, Robert

2017-12-23

The work is designed to uncover the pattern of mutual relation among trace elements and epidemiological data in the degenerated intervertebral disk tissue in humans. Hitherto the reason of the degenerative process is not fully understood. Trace elements are the basic components of the biological compound related both its metabolism as well as environmental exposure. The relation pattern among elements occurs gives new perspective in solving the cause of the disease. We have analysed trace elements content in the 30 intervertebral disc from 22 patients with degenerative disc disease. The concentrations of Al, Cu, Cd, Mo, Ni and Pb were determined with Atomic Absorption Spectrometry. To analyse the multidimentional relation between trace element concentration and epidemiological data the chemometric analysis was applied. The similarity have been shown in occurrence of following pairs: Cd-Mo as well as Mg-Zn. The second pair was correlated with Pb concentration. Pb levels are observed to be competitive to Cu concentration. Cd concentration was related to Zn and Mg deficiency. No single but rather cluster of epidemiological data show observable influence on the TE tissue variance. Zn and Cu was related to the male sex. Operation with orthopedic implants were related to combined Al, Mo and Zn concentration. This is the first chemometric analysis of trace elements in disk tissue. It shows multidimentional relations that are missed by the classical statistic. The analysis shows significant relation. The nature of the relations is the basis for further metabolic and environmental research.

Introducing Chemometrics to the Analytical Curriculum: Combining Theory and Lab Experience

ERIC Educational Resources Information Center

Gilbert, Michael K.; Luttrell, Robert D.; Stout, David; Vogt, Frank

2008-01-01

Beer's law is an ideal technique that works only in certain situations. A method for dealing with more complex conditions needs to be integrated into the analytical chemistry curriculum. For that reason, the capabilities and limitations of two common chemometric algorithms, classical least squares (CLS) and principal component regression (PCR),…

Determination of benzo[a]pyrene in cigarette mainstream smoke by using mid-infrared spectroscopy associated with a novel chemometric algorithm.

PubMed

Zhang, Yan; Zou, Hong-Yan; Shi, Pei; Yang, Qin; Tang, Li-Juan; Jiang, Jian-Hui; Wu, Hai-Long; Yu, Ru-Qin

2016-01-01

Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke. Copyright © 2015 Elsevier B.V. All rights reserved.

FTIR characterization of Mexican honey and its adulteration with sugar syrups by using chemometric methods

NASA Astrophysics Data System (ADS)

Rios-Corripio, M. A.; Rios-Leal, E.; Rojas-López, M.; Delgado-Macuil, R.

2011-01-01

A chemometric analysis of adulteration of Mexican honey by sugar syrups such as corn syrup and cane sugar syrup was realized. Fourier transform infrared spectroscopy (FTIR) was used to measure the absorption of a group of bee honey samples from central region of Mexico. Principal component analysis (PCA) was used to process FTIR spectra to determine the adulteration of bee honey. In addition to that, the content of individual sugars from honey samples: glucose, fructose, sucrose and monosaccharides was determined by using PLS-FTIR analysis validated by HPLC measurements. This analytical methodology which is based in infrared spectroscopy and chemometry can be an alternative technique to characterize and also to determine the purity and authenticity of nutritional products as bee honey and other natural products.

The relationship between mineral contents, particle matter and bottom ash distribution during pellet combustion: molar balance and chemometric analysis.

PubMed

Jeguirim, Mejdi; Kraiem, Nesrine; Lajili, Marzouk; Guizani, Chamseddine; Zorpas, Antonis; Leva, Yann; Michelin, Laure; Josien, Ludovic; Limousy, Lionel

2017-04-01

This paper aims to identify the correlation between the mineral contents in agropellets and particle matter and bottom ash characteristics during combustion in domestic boilers. Four agrifood residues with higher mineral contents, namely grape marc (GM), tomato waste (TW), exhausted olive mill solid waste (EOMSW) and olive mill wastewater (OMWW), were selected. Then, seven different pellets were produced from pure residues or their mixture and blending with sawdust. The physico-chemical properties of the produced pellets were analysed using different analytical techniques, and a particular attention was paid to their mineral contents. Combustion tests were performed in 12-kW domestic boiler. The particle matter (PM) emission was characterised through the particle number and mass quantification for different particle size. The bottom ash composition and size distribution were also characterised. Molar balance and chemometric analyses were performed to identify the correlation between the mineral contents and PM and bottom ash characteristics. The performed analyses indicate that K, Na, S and Cl are released partially or completely during combustion tests. In contrast, Ca, Mg, Si, P, Al, Fe and Mn are retained in the bottom ash. The chemometric analyses indicate that, in addition to the operating conditions and the pellet ash contents, K and Si concentrations have a significant effect on the PM emissions as well as on the agglomeration of bottom ash.

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

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

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

NASA Astrophysics Data System (ADS)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

Chemometric techniques in distribution, characterisation and source apportionment of polycyclic aromatic hydrocarbons (PAHS) in aquaculture sediments in Malaysia.

PubMed

Retnam, Ananthy; Zakaria, Mohamad Pauzi; Juahir, Hafizan; Aris, Ahmad Zaharin; Zali, Munirah Abdul; Kasim, Mohd Fadhil

2013-04-15

This study investigated polycyclic aromatic hydrocarbons (PAHs) pollution in surface sediments within aquaculture areas in Peninsular Malaysia using chemometric techniques, forensics and univariate methods. The samples were analysed using soxhlet extraction, silica gel column clean-up and gas chromatography mass spectrometry. The total PAH concentrations ranged from 20 to 1841 ng/g with a mean of 363 ng/g dw. The application of chemometric techniques enabled clustering and discrimination of the aquaculture sediments into four groups according to the contamination levels. A combination of chemometric and molecular indices was used to identify the sources of PAHs, which could be attributed to vehicle emissions, oil combustion and biomass combustion. Source apportionment using absolute principle component scores-multiple linear regression showed that the main sources of PAHs are vehicle emissions 54%, oil 37% and biomass combustion 9%. Land-based pollution from vehicle emissions is the predominant contributor of PAHs in the aquaculture sediments of Peninsular Malaysia. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development and Validation of Chemometric Spectrophotometric Methods for Simultaneous Determination of Simvastatin and Nicotinic Acid in Binary Combinations.

PubMed

Alahmad, Shoeb; Elfatatry, Hamed M; Mabrouk, Mokhtar M; Hammad, Sherin F; Mansour, Fotouh R

2018-01-01

The development and introduction of combined therapy represent a challenge for analysis due to severe overlapping of their UV spectra in case of spectroscopy or the requirement of a long tedious and high cost separation technique in case of chromatography. Quality control laboratories have to develop and validate suitable analytical procedures in order to assay such multi component preparations. New spectrophotometric methods for the simultaneous determination of simvastatin (SIM) and nicotinic acid (NIA) in binary combinations were developed. These methods are based on chemometric treatment of data, the applied chemometric techniques are multivariate methods including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS). In these techniques, the concentration data matrix were prepared by using the synthetic mixtures containing SIM and NIA dissolved in ethanol. The absorbance data matrix corresponding to the concentration data matrix was obtained by measuring the absorbance at 12 wavelengths in the range 216 - 240 nm at 2 nm intervals in the zero-order. The spectrophotometric procedures do not require any separation step. The accuracy, precision and the linearity ranges of the methods have been determined and validated by analyzing synthetic mixtures containing the studied drugs. Chemometric spectrophotometric methods have been developed in the present study for the simultaneous determination of simvastatin and nicotinic acid in their synthetic binary mixtures and in their mixtures with possible excipients present in tablet dosage form. The validation was performed successfully. The developed methods have been shown to be accurate, linear, precise, and so simple. The developed methods can be used routinely for the determination dosage form. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Chemometrics-assisted spectrophotometry method for the determination of chemical oxygen demand in pulping effluent.

PubMed

Chen, Honglei; Chen, Yuancai; Zhan, Huaiyu; Fu, Shiyu

2011-04-01

A new method has been developed for the determination of chemical oxygen demand (COD) in pulping effluent using chemometrics-assisted spectrophotometry. Two calibration models were established by inducing UV-visible spectroscopy (model 1) and derivative spectroscopy (model 2), combined with the chemometrics software Smica-P. Correlation coefficients of the two models are 0.9954 (model 1) and 0.9963 (model 2) when COD of samples is in the range of 0 to 405 mg/L. Sensitivities of the two models are 0.0061 (model 1) and 0.0056 (model 2) and method detection limits are 2.02-2.45 mg/L (model 1) and 2.13-2.51 mg/L (model 2). Validation experiment showed that the average standard deviation of model 2 was 1.11 and that of model 1 was 1.54. Similarly, average relative error of model 2 (4.25%) was lower than model 1 (5.00%), which indicated that the predictability of model 2 was better than that of model 1. Chemometrics-assisted spectrophotometry method did not need chemical reagents and digestion which were required in the conventional methods, and the testing time of the new method was significantly shorter than the conventional ones. The proposed method can be used to measure COD in pulping effluent as an environmentally friendly approach with satisfactory results.

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.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches

PubMed Central

Ortiz-Villanueva, Elena; Tauler, Romà

2017-01-01

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase. PMID:29064436

A chemometric approach to the characterisation of historical mortars

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

Rampazzi, L.; Pozzi, A.; Sansonetti, A.

2006-06-15

The compositional knowledge of historical mortars is of great concern in case of provenance and dating investigations and of conservation works since the nature of the raw materials suggests the most compatible conservation products. The classic characterisation usually goes through various analytical determinations, while conservation laboratories call for simple and quick analyses able to enlighten the nature of mortars, usually in terms of the binder fraction. A chemometric approach to the matter is here undertaken. Specimens of mortars were prepared with calcitic and dolomitic binders and analysed by Atomic Spectroscopy. Principal Components Analysis (PCA) was used to investigate the featuresmore » of specimens and samples. A Partial Least Square (PLS1) regression was done in order to predict the binder/aggregate ratio. The model was applied to historical mortars from the churches of St. Lorenzo (Milan) and St. Abbondio (Como). The accordance between the predictive model and the real samples is discussed.« less

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

Trbović, D.; Petronijević, R.; Đorđević, V.

2017-09-01

Milk and milk-based products are among the leading food categories according to reported cases of food adulteration. Although many authentication problems exist in all areas of the food industry, adequate control methods are required to evaluate the authenticity of milk and milk products in the dairy industry. Moreover, gas chromatography (GC) analysis of triacylglycerols (TAGs) or fatty acid (FA) profiles of milk fat (MF) in combination with multivariate statistical data processing have been used to detect adulterations of milk and dairy products with foreign fats. The adulteration of milk and butter is a major issue for the dairy industry. The major adulterants of MF are vegetable oils (soybean, sunflower, groundnut, coconut, palm and peanut oil) and animal fat (cow tallow and pork lard). Multivariate analysis enables adulterated MF to be distinguished from authentic MF, while taking into account many analytical factors. Various multivariate analysis methods have been proposed to quantitatively detect levels of adulterant non-MFs, with multiple linear regression (MLR) seemingly the most suitable. There is a need for increased use of chemometric data analyses to detect adulterated MF in foods and for their expanded use in routine quality assurance testing.

Screening and Analysis of the Marker Components in Ganoderma lucidum by HPLC and HPLC-MSn with the Aid of Chemometrics.

PubMed

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

2017-04-06

Ganoderma triterpenes (GTs) are the major secondary metabolites of Ganoderma lucidum , which is a popularly used traditional Chinese medicine for complementary cancer therapy. The present study was to establish a fingerprint evaluation system based on Similarity Analysis (SA), Cluster Analysis (CA) and Principal Component Analysis (PCA) for the identification and quality control of G. lucidum . Fifteen samples from the Chinese provinces of Hainan, Neimeng, Shangdong, Jilin, Anhui, Henan, Yunnan, Guangxi and Fujian were analyzed by HPLC-PAD and HPLC-MS n . Forty-seven compounds were detected by HPLC, of which forty-two compounds were tentatively identified by comparing their retention times and mass spectrometry data with that of reference compounds and reviewing the literature. Ganoderic acid B, 3,7,15-trihydroxy-11,23-dioxolanost-8,16-dien-26-oic acid, lucidenic acid A, ganoderic acid G, and 3,7-oxo-12-acetylganoderic acid DM were deemed to be the marker compounds to distinguish the samples with different quality according to both CA and PCA. This study provides helpful chemical information for further research on the anti-tumor activity and mechanism of action of G. lucidum . The results proved that fingerprints combined with chemometrics are a simple, rapid and effective method for the quality control of G. lucidum .

Chemometric studies on potential larvicidal compounds against Aedes aegypti.

PubMed

Scotti, Luciana; Scotti, Marcus Tullius; Silva, Viviane Barros; Santos, Sandra Regina Lima; Cavalcanti, Sócrates C H; Mendonça, Francisco J B

2014-03-01

The mosquito Aedes aegypti (Diptera, Culicidae) is the vector of yellow and dengue fever. In this study, chemometric tools, such as, Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares Regression (PLS), were applied to a set of fifty five active compounds against Ae. aegypti larvae, which includes terpenes, cyclic alcohols, phenolic compounds, and their synthetic derivatives. The calculations were performed using the VolSurf+ program. CPCA analysis suggests that the higher weight blocks of descriptors were SIZE/SHAPE, DRY, and H2O. The PCA was generated with 48 descriptors selected from the previous blocks. The scores plot showed good separation between more and less potent compounds. The first two PCs accounted for over 60% of the data variance. The best model obtained in PLS, after validation leave-one-out, exhibited q(2) = 0.679 and r(2) = 0.714. External prediction model was R(2) = 0.623. The independent variables having a hydrophobic profile were strongly correlated to the biological data. The interaction maps generated with the GRID force field showed that the most active compounds exhibit more interaction with the DRY probe.

Multi-way chemometric methodologies and applications: a central summary of our research work.

PubMed

Wu, Hai-Long; Nie, Jin-Fang; Yu, Yong-Jie; Yu, Ru-Qin

2009-09-14

Multi-way data analysis and tensorial calibration are gaining widespread acceptance with the rapid development of modern analytical instruments. In recent years, our group working in State Key Laboratory of Chemo/Biosensing and Chemometrics in Hunan University has carried out exhaustive scientific research work in this area, such as building more canonical symbol systems, seeking the inner mathematical cyclic symmetry property for trilinear or multilinear decomposition, suggesting a series of multi-way calibration algorithms, exploring the rank estimation of three-way trilinear data array and analyzing different application systems. In this present paper, an overview from second-order data to third-order data covering about theories and applications in analytical chemistry has been presented.

Mid-infrared spectroscopy combined with chemometrics to detect Sclerotinia stem rot on oilseed rape (Brassica napus L.) leaves.

PubMed

Zhang, Chu; Feng, Xuping; Wang, Jian; Liu, Fei; He, Yong; Zhou, Weijun

2017-01-01

Detection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves. The average mid-infrared spectra showed differences between healthy and infected leaves, and the differences varied among different sample sets. Optimal wavenumbers for the 2 sample sets selected by the second derivative spectra were similar, indicating the efficacy of selecting optimal wavenumbers. Chemometric methods were further used to quantitatively detect the oilseed rape leaves infected by SSR, including the partial least squares-discriminant analysis, support vector machine and extreme learning machine. The discriminant models using the full spectra and the optimal wavenumbers of the 2 sample sets were effective for classification accuracies over 80%. The discriminant results for the 2 sample sets varied due to variations in the samples. The use of two sample sets proved and validated the feasibility of using mid-infrared spectroscopy and chemometric methods for detecting SSR on oilseed rape leaves. The similarities among the selected optimal wavenumbers in different sample sets made it feasible to simplify the models and build practical models. Mid-infrared spectroscopy is a reliable and promising technique for SSR control. This study helps in developing practical application of using mid-infrared spectroscopy combined with chemometrics to detect plant disease.

Simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii using excitation-emission matrix fluorescence coupled with chemometrics methods

NASA Astrophysics Data System (ADS)

Bai, Xue-Mei; Liu, Tie; Liu, De-Long; Wei, Yong-Ju

2018-02-01

A chemometrics-assisted excitation-emission matrix (EEM) fluorescence method was proposed for simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii. Using the strategy of combining EEM data with chemometrics methods, the simultaneous determination of α-asarone and β-asarone in the complex Traditional Chinese medicine system was achieved successfully, even in the presence of unexpected interferents. The physical or chemical separation step was avoided due to the use of ;mathematical separation;. Six second-order calibration methods were used including parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), alternating penalty trilinear decomposition (APTLD), self-weighted alternating trilinear decomposition (SWATLD), the unfolded partial least-squares (U-PLS) and multidimensional partial least-squares (N-PLS) with residual bilinearization (RBL). In addition, HPLC method was developed to further validate the presented strategy. Consequently, for the validation samples, the analytical results obtained by six second-order calibration methods were almost accurate. But for the Acorus tatarinowii samples, the results indicated a slightly better predictive ability of N-PLS/RBL procedure over other methods.

Rapid and sensitive analysis of 27 underivatized free amino acids, dipeptides, and tripeptides in fruits of Siraitia grosvenorii Swingle using HILIC-UHPLC-QTRAP(®)/MS (2) combined with chemometrics methods.

PubMed

Zhou, Guisheng; Wang, Mengyue; Li, Yang; Peng, Ying; Li, Xiaobo

2015-08-01

In the present study, a new strategy based on chemical analysis and chemometrics methods was proposed for the comprehensive analysis and profiling of underivatized free amino acids (FAAs) and small peptides among various Luo-Han-Guo (LHG) samples. Firstly, the ultrasound-assisted extraction (UAE) parameters were optimized using Plackett-Burman (PB) screening and Box-Behnken designs (BBD), and the following optimal UAE conditions were obtained: ultrasound power of 280 W, extraction time of 43 min, and the solid-liquid ratio of 302 mL/g. Secondly, a rapid and sensitive analytical method was developed for simultaneous quantification of 24 FAAs and 3 active small peptides in LHG at trace levels using hydrophilic interaction ultra-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (HILIC-UHPLC-QTRAP(®)/MS(2)). The analytical method was validated by matrix effects, linearity, LODs, LOQs, precision, repeatability, stability, and recovery. Thirdly, the proposed optimal UAE conditions and analytical methods were applied to measurement of LHG samples. It was shown that LHG was rich in essential amino acids, which were beneficial nutrient substances for human health. Finally, based on the contents of the 27 analytes, the chemometrics methods of unsupervised principal component analysis (PCA) and supervised counter propagation artificial neural network (CP-ANN) were applied to differentiate and classify the 40 batches of LHG samples from different cultivated forms, regions, and varieties. As a result, these samples were mainly clustered into three clusters, which illustrated the cultivating disparity among the samples. In summary, the presented strategy had potential for the investigation of edible plants and agricultural products containing FAAs and small peptides.

Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance.

PubMed

Judycka-Proma, U; Bober, L; Gajewicz, A; Puzyn, T; Błażejowski, J

2015-03-05

Forty ampholytic compounds of biological and pharmaceutical relevance were subjected to chemometric analysis based on unsupervised and supervised learning algorithms. This enabled relations to be found between empirical spectral characteristics derived from electronic absorption data and structural and physicochemical parameters predicted by quantum chemistry methods or phenomenological relationships based on additivity rules. It was found that the energies of long wavelength absorption bands are correlated through multiparametric linear relationships with parameters reflecting the bulkiness features of the absorbing molecules as well as their nucleophilicity and electrophilicity. These dependences enable the quantitative analysis of spectral features of the compounds, as well as a comparison of their similarities and certain pharmaceutical and biological features. Three QSPR models to predict the energies of long-wavelength absorption in buffers with pH=2.5 and pH=7.0, as well as in methanol, were developed and validated in this study. These models can be further used to predict the long-wavelength absorption energies of untested substances (if they are structurally similar to the training compounds). Copyright © 2014 Elsevier B.V. All rights reserved.

Chemometric study of Maya Blue from the voltammetry of microparticles approach.

PubMed

Doménech, Antonio; Doménech-Carbó, María Teresa; de Agredos Pascual, María Luisa Vazquez

2007-04-01

The use of the voltammetry of microparticles at paraffin-impregnated graphite electrodes allows for the characterization of different types of Maya Blue (MB) used in wall paintings from different archaeological sites of Campeche and YucatAn (Mexico). Using voltammetric signals for electron-transfer processes involving palygorskite-associated indigo and quinone functionalities generated by scratching the graphite surface, voltammograms provide information on the composition and texture of MB samples. Application of hierarchical cluster analysis and other chemometric methods allows us to characterize samples from different archaeological sites and to distinguish between samples proceeding from different chronological periods. Comparison between microscopic, spectroscopic, and electrochemical examination of genuine MB samples and synthetic specimens indicated that the preparation procedure of the pigment evolved in time via successive steps anticipating modern synthetic procedures, namely, hybrid organic-inorganic synthesis, temperature control of chemical reactivity, and template-like synthesis.

Chemometric studies for the characterization and differentiation of microorganisms using in situ derivatization and thermal desorption ion mobility spectrometry.

PubMed

Ochoa, Mariela L; Harrington, Peter B

2005-02-01

Whole-cell bacteria were characterized and differentiated by thermal desorption ion mobility spectrometry and chemometric modeling. Principal component analysis was used to evaluate the differences in the ion mobility spectra of whole-cell bacteria and the fatty acid methyl esters (FAMEs) generated in situ after derivatization of the bacterial lipids. Alternating least squares served to extract bacterial peaks from the complex ion mobility spectra of intact microorganisms and, therefore, facilitated the characterization of bacterial strains, species, and Gram type. In situ thermal hydrolysis/methylation with tetramethylammonium hydroxide was necessary for the differentiation of Escherichia coli strains, which otherwise could not be distinguished by spectra acquired with the ITEMISER ion mobility spectrometer. The addition of the methylating agent had no effect on Gram-positive bacteria, and therefore, they could not be differentiated by genera. The classification of E. coli strains was possible by analysis of the IMS spectra from the FAMEs generated in situ. By using the fuzzy multivariate rule-building expert system and cross-validation, a correct classification rate of 96% (22 out of 23 spectra) was obtained. Chemometric modeling on bacterial ion mobility spectra coupled to thermal hydrolysis/methylation proved a simple, rapid (2 min/sample), inexpensive, and sensitive technique to characterize and differentiate intact microorganisms. The ITEMISER ion mobility spectrometer could detect as few as 4 x 10(6) cells/sample.

Direct analysis of six antibiotics in wastewater samples using rapid high-performance liquid chromatography coupled with diode array detector: a chemometric study towards green analytical chemistry.

PubMed

Vosough, Maryam; Rashvand, Masoumeh; Esfahani, Hadi M; Kargosha, Kazem; Salemi, Amir

2015-04-01

In this work, a rapid HPLC-DAD method has been developed for the analysis of six antibiotics (amoxicillin, metronidazole, sulfamethoxazole, ofloxacine, sulfadiazine and sulfamerazine) in the sewage treatment plant influent and effluent samples. Decreasing the chromatographic run time to less than 4 min as well as lowering the cost per analysis, were achieved through direct injection of the samples into the HPLC system followed by chemometric analysis. The problem of the complete separation of the analytes from each other and/or from the matrix ingredients was resolved as a posteriori. The performance of MCR/ALS and U-PLS/RBL, as second-order algorithms, was studied and comparable results were obtained from implication of these modeling methods. It was demonstrated that the proposed methods could be used promisingly as green analytical strategies for detection and quantification of the targeted pollutants in wastewater samples while avoiding the more complicated high cost instrumentations. Copyright © 2014 Elsevier B.V. All rights reserved.

Best conditions for biodegradation of diesel oil by chemometric tools.

PubMed

Kaczorek, Ewa; Bielicka-Daszkiewicz, Katarzyna; Héberger, Károly; Kemény, Sándor; Olszanowski, Andrzej; Voelkel, Adam

2014-01-01

Diesel oil biodegradation by different bacteria-yeast-rhamnolipids consortia was tested. Chromatographic analysis of post-biodegradation residue was completed with chemometric tools (ANOVA, and a novel ranking procedure based on the sum of ranking differences). These tools were used in the selection of the most effective systems. The best results of aliphatic fractions of diesel oil biodegradation were observed for a yeast consortia with Aeromonas hydrophila KR4. For these systems the positive effect of rhamnolipids on hydrocarbon biodegradation was observed. However, rhamnolipids addition did not always have a positive influence on the biodegradation process (e.g. in case of yeast consortia with Stenotrophomonas maltophila KR7). Moreover, particular differences in the degradation pattern were observed for lower and higher alkanes than in the case with C22. Normally, the best conditions for "lower" alkanes are Aeromonas hydrophila KR4 + emulsifier independently from yeasts and e.g. Pseudomonas stutzeri KR7 for C24 alkane.

Best conditions for biodegradation of diesel oil by chemometric tools

PubMed Central

Kaczorek, Ewa; Bielicka-Daszkiewicz, Katarzyna; Héberger, Károly; Kemény, Sándor; Olszanowski, Andrzej; Voelkel, Adam

2014-01-01

Diesel oil biodegradation by different bacteria-yeast-rhamnolipids consortia was tested. Chromatographic analysis of post-biodegradation residue was completed with chemometric tools (ANOVA, and a novel ranking procedure based on the sum of ranking differences). These tools were used in the selection of the most effective systems. The best results of aliphatic fractions of diesel oil biodegradation were observed for a yeast consortia with Aeromonas hydrophila KR4. For these systems the positive effect of rhamnolipids on hydrocarbon biodegradation was observed. However, rhamnolipids addition did not always have a positive influence on the biodegradation process (e.g. in case of yeast consortia with Stenotrophomonas maltophila KR7). Moreover, particular differences in the degradation pattern were observed for lower and higher alkanes than in the case with C22. Normally, the best conditions for “lower” alkanes are Aeromonas hydrophila KR4 + emulsifier independently from yeasts and e.g. Pseudomonas stutzeri KR7 for C24 alkane. PMID:24948922

Metabolic fingerprint of Brazilian maize landraces silk (stigma/styles) using NMR spectroscopy and chemometric methods.

PubMed

Kuhnen, Shirley; Bernardi Ogliari, Juliana; Dias, Paulo Fernando; da Silva Santos, Maiara; Ferreira, Antônio Gilberto; Bonham, Connie C; Wood, Karl Vernon; Maraschin, Marcelo

2010-02-24

Aqueous extract from maize silks is used by traditional medicine for the treatment of several ailments, mainly related to the urinary system. This work focuses on the application of NMR spectroscopy and chemometric analysis for the determination of metabolic fingerprint and pattern recognition of silk extracts from seven maize landraces cultivated in southern Brazil. Principal component analysis (PCA) of the (1)H NMR data set showed clear discrimination among the maize varieties by PC1 and PC2, pointing out three distinct metabolic profiles. Target compounds analysis showed significant differences (p < 0.05) in the contents of protocatechuic acid, gallic acid, t-cinnamic acid, and anthocyanins, corroborating the discrimination of the genotypes in this study as revealed by PCA analysis. Thus the combination of (1)H NMR and PCA is a useful tool for the discrimination of maize silks in respect to their chemical composition, including rapid authentication of the raw material of current pharmacological interest.

Chromatographic fingerprinting through chemometric techniques for herbal slimming pills: A way of adulterant identification.

PubMed

Shekari, Nafiseh; Vosough, Maryam; Tabar Heidar, Kourosh

2018-05-01

In the current study, gas chromatography-mass spectrometry (GC-MS) fingerprinting of herbal slimming pills assisted by chemometric methods has been presented. Deconvolution of two-way chromatographic signals of nine herbal slimming pills into pure chromatographic and spectral patterns was performed. The peak clusters were resolved using multivariate curve resolution-alternating least squares (MCR-ALS) by employing appropriate constraints. It was revealed that more useful chemical information about the composition of the slimming pills can be obtained by employing sophisticated GC-MS method coupled with proper chemometric tools yielding the extended number of identified constituents. The thorough fingerprinting of the complex mixtures proved the presence of some toxic or carcinogen components, such as toluene, furfural, furfuryl alcohol, styrene, itaconic anhydride, citraconic anhydride, trimethyl phosphate, phenol, pyrocatechol, p-propenylanisole and pyrogallol. In addition, some samples were shown to be adulterated with undeclared ingredients, including stimulants, anorexiant and laxatives such as phenolphthalein, amfepramone, caffeine and sibutramine. Copyright © 2018 Elsevier B.V. All rights reserved.

Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data.

PubMed

Yu, Yong-Jie; Xia, Qiao-Ling; Wang, Sheng; Wang, Bing; Xie, Fu-Wei; Zhang, Xiao-Bing; Ma, Yun-Ming; Wu, Hai-Long

2014-09-12

Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure. Copyright © 2014 Elsevier B.V. All rights reserved.

Comprehensive analysis of yeast metabolite GC x GC-TOFMS data: combining discovery-mode and deconvolution chemometric software.

PubMed

Mohler, Rachel E; Dombek, Kenneth M; Hoggard, Jamin C; Pierce, Karisa M; Young, Elton T; Synovec, Robert E

2007-08-01

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine

NASA Astrophysics Data System (ADS)

Weng, Shizhuang; Dong, Ronglu; Zhu, Zede; Zhang, Dongyan; Zhao, Jinling; Huang, Linsheng; Liang, Dong

2018-01-01

Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30 ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of > 92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2 μL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

A Combination of Chemometrics and Quantum Mechanics Methods Applied to Analysis of Femtosecond Transient Absorption Spectrum of Ortho-Nitroaniline

PubMed Central

Yi, Jing; Xiong, Ying; Cheng, Kemei; Li, Menglong; Chu, Genbai; Pu, Xuemei; Xu, Tao

2016-01-01

A combination of the advanced chemometrics method with quantum mechanics calculation was for the first time applied to explore a facile yet efficient analysis strategy to thoroughly resolve femtosecond transient absorption spectroscopy of ortho-nitroaniline (ONA), served as a model compound of important nitroaromatics and explosives. The result revealed that the ONA molecule is primarily excited to S3 excited state from the ground state and then ultrafast relaxes to S2 state. The internal conversion from S2 to S1 occurs within 0.9 ps. One intermediate state S* was identified in the intersystem crossing (ISC) process, which is different from the specific upper triplet receiver state proposed in some other nitroaromatics systems. The S1 state decays to the S* one within 6.4 ps and then intersystem crossing to the lowest triplet state within 19.6 ps. T1 was estimated to have a lifetime up to 2 ns. The relatively long S* state and very long-lived T1 one should play a vital role as precursors to various nitroaromatic and explosive photoproducts. PMID:26781083

Chemometric Approach to the Calibration of Light Emitting Diode Based Optical Gas Sensors Using High-Resolution Transmission Molecular Absorption Data.

PubMed

Mahbub, Parvez; Leis, John; Macka, Mirek

2018-05-15

Modeling the propagation of light from LED sources is problematic since the emission covers a broad range of wavelengths and thus cannot be considered as monochromatic. Furthermore, the lack of directivity of such sources is also problematic. Both attributes are characteristic of LEDs. Here we propose a HITRAN ( high-resolution transmission molecular absorption database) based chemometric approach that incorporates not-perfect-monochromaticity and spatial directivity of near-infrared (NIR) LED for absorbance calculations in 1-6% methane (CH 4 ) in air, considering CH 4 as a model absorbing gas. We employed the absorbance thus calculated using HITRAN to validate the experimentally measured absorbance of CH 4 . The maximum error between the measured and calculated absorbance values were within 1%. The approach can be generalized as a chemometric calibration technique for measuring gases and gas mixtures that absorb emissions from polychromatic or not-perfect-monochromatic sources, provided the gas concentration, optical path length, as well as blank and attenuated emission spectra of the light source are incorporated into the proposed chemometric approach.

Ion mobility based on column leaching of South African gold tailings dam with chemometric evaluation.

PubMed

Cukrowska, Ewa M; Govender, Koovila; Viljoen, Morris

2004-07-01

New column leaching experiments were designed and used as an alternative rapid screening approach to element mobility assessment. In these experiments, field-moist material was treated with an extracting solution to assess the effects of acidification on element mobility in mine tailings. The main advantage of this version of column leaching experiments with partitioned segments is that they give quick information on current element mobility in conditions closely simulating field conditions to compare with common unrepresentative air-dried, sieved samples used for column leaching experiments. Layers from the tailings dump material were sampled and packed into columns. The design of columns allows extracting leachates from each layer. The extracting solutions used were natural (pH 6.8) and acidified (pH 4.2) rainwater. Metals and anions were determined in the leachates. The concentrations of metals (Ca, Mg, Fe, Mn, Al, Cr, Ni, Co, Zn, and Cu) in sample leachates were determined using ICP OES. The most important anions (NO3-, Cl-, and SO4(2)-) were determined using the closed system izotacophoresis ITP analyser. The chemical analytical data from tailings leaching and physico-chemical data from field measurements (including pH, conductivity, redox potential, temperature) were used for chemometric evaluation of element mobility. Principal factor analysis (PFA) was used to evaluate ions mobility from different layers of tailings dump arising from varied pH and redox conditions. It was found that the results from the partitioned column leaching illustrate much better complex processes of metals mobility from tailings dump than the total column. The chemometric data analysis (PFA) proofed the differences in the various layers leachability that are arising from physico-chemical processes due to chemical composition of tailings dump deposit. Copyright 2004 Elsevier Ltd.

Signature-Discovery Approach for Sample Matching of a Nerve-Agent Precursor using Liquid Chromatography–Mass Spectrometry, XCMS, and Chemometrics

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

Fraga, Carlos G.; Clowers, Brian H.; Moore, Ronald J.

2010-05-15

This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography mass spectrometry (LC-MS) data for the discovery of ultra-trace forensic signatures for sample matching of various stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). The use of the bioinformatic tool known as XCMS was used to comprehensively search and find candidate LC-MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source.more » Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by k-nearest neighbors. Using this approach, a test set of dichlor samples were all correctly matched to their source stock. The sample preparation and LC-MS method permitted the detection of dichlor impurities presumably in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to help in criminal investigations following chemical attacks.« less

Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling.

PubMed

Vargas-Bello-Pérez, Einar; Toro-Mujica, Paula; Enriquez-Hidalgo, Daniel; Fellenberg, María Angélica; Gómez-Cortés, Pilar

2017-06-01

We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Botanical discrimination of Greek unifloral honeys with physico-chemical and chemometric analyses.

PubMed

Karabagias, Ioannis K; Badeka, Anastasia V; Kontakos, Stavros; Karabournioti, Sofia; Kontominas, Michael G

2014-12-15

The aim of the present study was to investigate the possibility of characterisation and classification of Greek unifloral honeys (pine, thyme, fir and orange blossom) according to botanical origin using volatile compounds, conventional physico-chemical parameters and chemometric analyses (MANOVA and Linear Discriminant Analysis). For this purpose, 119 honey samples were collected during the harvesting period 2011 from 14 different regions in Greece known to produce unifloral honey of good quality. Physico-chemical analysis included the identification and semi quantification of fifty five volatile compounds performed by Headspace Solid Phase Microextraction coupled to gas chromatography/mass spectroscopy and the determination of conventional quality parameters such as pH, free, lactonic, total acidity, electrical conductivity, moisture, ash, lactonic/free acidity ratio and colour parameters L, a, b. Results showed that using 40 diverse variables (30 volatile compounds of different classes and 10 physico-chemical parameters) the honey samples were satisfactorily classified according to botanical origin using volatile compounds (84.0% correct prediction), physicochemical parameters (97.5% correct prediction), and the combination of both (95.8% correct prediction) indicating that multi element analysis comprises a powerful tool for honey discrimination purposes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Detection of counterfeit electronic components through ambient mass spectrometry and chemometrics.

PubMed

Pfeuffer, Kevin P; Caldwell, Jack; Shelley, Jake T; Ray, Steven J; Hieftje, Gary M

2014-09-21

In the last several years, illicit electronic components have been discovered in the inventories of several distributors and even installed in commercial and military products. Illicit or counterfeit electronic components include a broad category of devices that can range from the correct unit with a more recent date code to lower-specification or non-working systems with altered names, manufacturers and date codes. Current methodologies for identification of counterfeit electronics rely on visual microscopy by expert users and, while effective, are very time-consuming. Here, a plasma-based ambient desorption/ionization source, the flowing atmospheric pressure afterglow (FAPA) is used to generate a mass-spectral fingerprint from the surface of a variety of discrete electronic integrated circuits (ICs). Chemometric methods, specifically principal component analysis (PCA) and the bootstrapped error-adjusted single-sample technique (BEAST), are used successfully to differentiate between genuine and counterfeit ICs. In addition, chemical and physical surface-removal techniques are explored and suggest which surface-altering techniques were utilized by counterfeiters.

Improving reliability of chemometric models for authentication of species origin of heparin by switching from 1D to 2D NMR experiments.

PubMed

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

2018-05-10

Nuclear magnetic resonance (NMR) spectroscopy is regarded as one of the most powerful and versatile analytical approaches to assure the quality of heparin preparations. In particular, it was recently demonstrated that by using 1 H NMR coupled with chemometrics heparin and low molecular weight heparin (LMWH) samples derived from three major animal species (porcine, ovine and bovine) can be differentiated [Y.B. Monakhova et al. J. Pharm. Anal. 149 (2018) 114-119]. In this study, significant improvement of existing chemometric models was achieved by switching to 2D NMR experiments (heteronuclear multiple-quantum correlation (HMQC) and diffusion-ordered spectroscopy (DOSY)). Two representative data sets (sixty-nine heparin and twenty-two LMWH) belonged to different batches and distributed by different commercial companies were investigated. A trend for animal species differentiation was observed in the principal component analysis (PCA) score plot built based on the DOSY data. A superior model was constructed using HMQC experiments, where individual heparin (LMWH) clusters as well as their blends were clearly differentiated. The predictive power of different classification methods as well as unsupervised techniques (independent components analysis, ICA) clearly proved applicability of the model for routine heparin and LMWH analysis. The switch from 1D to 2D NMR techniques provides a wealth of additional information, which is beneficial for multivariate modeling of NMR spectroscopic data for heparin preparations. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed Central

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

2017-01-01

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

Chemometric Profile of Root Extracts of Rhodiola imbricata Edgew. with Hyphenated Gas Chromatography Mass Spectrometric Technique

PubMed Central

Tayade, Amol B.; Dhar, Priyanka; Kumar, Jatinder; Sharma, Manu; Chauhan, Rajinder S.; Chaurasia, Om P.; Srivastava, Ravi B.

2013-01-01

Rhodiola imbricata Edgew. (Rose root or Arctic root or Golden root or Shrolo), belonging to the family Crassulaceae, is an important food crop and medicinal plant in the Indian trans-Himalayan cold desert. Chemometric profile of the n-hexane, chloroform, dichloroethane, ethyl acetate, methanol, and 60% ethanol root extracts of R. imbricata were performed by hyphenated gas chromatography mass spectrometry (GC/MS) technique. GC/MS analysis was carried out using Thermo Finnigan PolarisQ Ion Trap GC/MS MS system comprising of an AS2000 liquid autosampler. Interpretation on mass spectrum of GC/MS was done using the NIST/EPA/NIH Mass Spectral Database, with NIST MS search program v.2.0g. Chemometric profile of root extracts revealed the presence of 63 phyto-chemotypes, among them, 1-pentacosanol; stigmast-5-en-3-ol, (3β,24S); 1-teracosanol; 1-henteracontanol; 17-pentatriacontene; 13-tetradecen-1-ol acetate; methyl tri-butyl ammonium chloride; bis(2-ethylhexyl) phthalate; 7,8-dimethylbenzocyclooctene; ethyl linoleate; 3-methoxy-5-methylphenol; hexadecanoic acid; camphor; 1,3-dimethoxybenzene; thujone; 1,3-benzenediol, 5-pentadecyl; benzenemethanol, 3-hydroxy, 5-methoxy; cholest-4-ene-3,6-dione; dodecanoic acid, 3-hydroxy; octadecane, 1-chloro; ethanone, 1-(4-hydroxyphenyl); α-tocopherol; ascaridole; campesterol; 1-dotriacontane; heptadecane, 9-hexyl were found to be present in major amount. Eventually, in the present study we have found phytosterols, terpenoids, fatty acids, fatty acid esters, alkyl halides, phenols, alcohols, ethers, alkanes, and alkenes as the major group of phyto-chemotypes in the different root extracts of R. imbricata. All these compounds identified by GC/MS analysis were further investigated for their biological activities and it was found that they possess a diverse range of positive pharmacological actions. In future, isolation of individual phyto-chemotypes and subjecting them to biological activity will definitely prove fruitful results in

Use of Vis/NIRS for the determination of sugar content of cola soft drinks based on chemometric methods

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-03-01

Three different chemometric methods were performed for the determination of sugar content of cola soft drinks using visible and near infrared spectroscopy (Vis/NIRS). Four varieties of colas were prepared and 180 samples (45 samples for each variety) were selected for the calibration set, while 60 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay, standard normal variate (SNV) and Savitzky-Golay first derivative transformation were applied for the pre-processing of spectral data. The first eleven principal components (PCs) extracted by partial least squares (PLS) analysis were employed as the inputs of BP neural network (BPNN) and least squares-support vector machine (LS-SVM) model. Then the BPNN model with the optimal structural parameters and LS-SVM model with radial basis function (RBF) kernel were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.971, 1.259 and -0.335 for PLS, 0.986, 0.763, and -0.042 for BPNN, while 0.978, 0.995 and -0.227 for LS-SVM, respectively. All the three methods supplied a high and satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be utilized as a high precision way for the determination of sugar content of cola soft drinks.

Chemometric dissimilarity in nutritive value of popularly consumed Nigerian brown and white common beans.

PubMed

Moyib, Oluwasayo Kehinde; Alashiri, Ganiyy Olasunkanmi; Adejoye, Oluseyi Damilola

2015-01-01

Brown beans are the preferred varieties over the white beans in Nigeria due to their assumed richer nutrients. This study was aimed at assessing and characterising some popular Nigerian common beans for their nutritive value based on seed coat colour. Three varieties, each, of Nigerian brown and white beans, and one, each, of French bean and soybean were analysed for 19 nutrients. Z-statistics test showed that Nigerian beans are nutritionally analogous to French bean and soybean. Analysis of variance showed that seed coat colour varied with proximate nutrients, Ca, Fe, and Vit C. Chemometric analysis methods revealed superior beans for macro and micro nutrients and presented clearer groupings among the beans for seed coat colour. The study estimated a moderate genetic distance (GD) that will facilitate transfer of useful genes and intercrossing among the beans. It also offers an opportunity to integrate French bean and soybean into genetic improvement programs in Nigerian common beans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rapid determination of crocins in saffron by near-infrared spectroscopy combined with chemometric techniques

NASA Astrophysics Data System (ADS)

Li, Shuailing; Shao, Qingsong; Lu, Zhonghua; Duan, Chengli; Yi, Haojun; Su, Liyang

2018-02-01

Saffron is an expensive spice. Its primary effective constituents are crocin I and II, and the contents of these compounds directly affect the quality and commercial value of saffron. In this study, near-infrared spectroscopy was combined with chemometric techniques for the determination of crocin I and II in saffron. Partial least squares regression models were built for the quantification of crocin I and II. By comparing different spectral ranges and spectral pretreatment methods (no pretreatment, vector normalization, subtract a straight line, multiplicative scatter correction, minimum-maximum normalization, eliminate the constant offset, first derivative, and second derivative), optimum models were developed. The root mean square error of cross-validation values of the best partial least squares models for crocin I and II were 1.40 and 0.30, respectively. The coefficients of determination for crocin I and II were 93.40 and 96.30, respectively. These results show that near-infrared spectroscopy can be combined with chemometric techniques to determine the contents of crocin I and II in saffron quickly and efficiently.

Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods

PubMed Central

Gong, Aiping; Zhu, Susu; He, Yong; Zhang, Chu

2017-01-01

Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874–1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages. PMID:28757578

Inorganic elemental determinations of marine traditional Chinese Medicine Meretricis concha from Jiaozhou Bay: The construction of inorganic elemental fingerprint based on chemometric analysis

NASA Astrophysics Data System (ADS)

Shao, Mingying; Li, Xuejie; Zheng, Kang; Jiang, Man; Yan, Cuiwei; Li, Yantuan

2016-04-01

The goal of this paper is to explore the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, which is a commonly used marine traditional Chinese medicine (TCM) for the treatment of asthma and scald burns. For that, the inorganic elemental contents of Meretricis concha from five sampling points in Jiaozhou Bay have been determined by means of inductively coupled plasma optical emission spectrometry, and the comparative investigations based on the contents of 14 inorganic elements (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se and Zn) of the samples from Jiaozhou Bay and the previous reported Rushan Bay were performed. It has been found that the samples from the two bays are approximately classified into two kinds using hierarchical cluster analysis, and a four-factor model based on principle component analysis could explain approximately 75% of the detection data, also linear discriminant analysis can be used to develop a prediction model to distinguish the samples from Jiaozhou Bay and Rushan Bay with accuracy of about 93%. The results of the present investigation suggested that the inorganic elemental fingerprint based on the combination of the measured elemental content and chemometric analysis is a promising approach for verifying the geographical origin of Meretricis concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

Impact of Cultivation Conditions, Ethylene Treatment, and Postharvest Storage on Selected Quality and Bioactivity Parameters of Kiwifruit "Hayward" Evaluated by Analytical and Chemometric Methods.

PubMed

Park, Yong Seo; Polovka, Martin; Ham, Kyung-Sik Ham; Park, Yang-Kyun; Vearasilp, Suchada; Namieśnik, Jacek; Toledo, Fernando; Arancibia-Avila, Patricia; Gorinstein, Shela

2016-09-01

Organic, semiorganic, and conventional "Hayward" kiwifruits, treated with ethylene for 24 h and stored during 10 days, were assessed by UV spectrometry, fluorometry, and chemometrical analysis for changes in selected characteristics of quality (firmness, dry matter and soluble solid contents, pH, and acidity) and bioactivity (concentration of polyphenols via Folin-Ciocalteu and p-hydroxybenzoic acid assays). All of the monitored qualitative parameters and characteristics related to bioactivity were affected either by cultivation practices or by ethylene treatment and storage. Results obtained, supported by statistical evaluation (Friedman two-way ANOVA) and chemometric analysis, clearly proved that the most significant impact on the majority of the evaluated parameters of quality and bioactivity of "Hayward" kiwifruit had the ethylene treatment followed by the cultivation practices and the postharvest storage. Total concentration of polyphenols expressed via p-hydroxybenzoic acid assay exhibited the most significant sensitivity to all three evaluated parameters, reaching a 16.5% increase for fresh organic compared to a conventional control sample. As a result of postharvest storage coupled with ethylene treatment, the difference increased to 26.3%. Three-dimensional fluorescence showed differences in the position of the main peaks and their fluorescence intensity for conventional, semiorganic, and organic kiwifruits in comparison with ethylene nontreated samples.

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride.

PubMed

Katsarov, Plamen; Gergov, Georgi; Alin, Aylin; Pilicheva, Bissera; Al-Degs, Yahya; Simeonov, Vasil; Kassarova, Margarita

2018-03-01

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

Simultaneous kinetic spectrometric determination of three flavonoid antioxidants in fruit with the aid of chemometrics

NASA Astrophysics Data System (ADS)

Sun, Ruiling; Wang, Yong; Ni, Yongnian; Kokot, Serge

2014-03-01

A simple, inexpensive and sensitive kinetic spectrophotometric method was developed for the simultaneous determination of three anti-carcinogenic flavonoids: catechin, quercetin and naringenin, in fruit samples. A yellow chelate product was produced in the presence neocuproine and Cu(I) - a reduction product of the reaction between the flavonoids with Cu(II), and this enabled the quantitative measurements with UV-vis spectrophotometry. The overlapping spectra obtained, were resolved with chemometrics calibration models, and the best performing method was the fast independent component analysis (fast-ICA/PCR (Principal component regression)); the limits of detection were 0.075, 0.057 and 0.063 mg L-1 for catechin, quercetin and naringenin, respectively. The novel method was found to outperform significantly the common HPLC procedure.

Chemometric classification of gunshot residues based on energy dispersive X-ray microanalysis and inductively coupled plasma analysis with mass-spectrometric detection

NASA Astrophysics Data System (ADS)

Steffen, S.; Otto, M.; Niewoehner, L.; Barth, M.; Bro¿żek-Mucha, Z.; Biegstraaten, J.; Horváth, R.

2007-09-01

A gunshot residue sample that was collected from an object or a suspected person is automatically searched for gunshot residue relevant particles. Particle data (such as size, morphology, position on the sample for manual relocation, etc.) as well as the corresponding X-ray spectra and images are stored. According to these data, particles are classified by the analysis-software into different groups: 'gunshot residue characteristic', 'consistent with gunshot residue' and environmental particles, respectively. Potential gunshot residue particles are manually checked and - if necessary - confirmed by the operating forensic scientist. As there are continuing developments on the ammunition market worldwide, it becomes more and more difficult to assign a detected particle to a particular ammunition brand. As well, the differentiation towards environmental particles similar to gunshot residue is getting more complex. To keep external conditions unchanged, gunshot residue particles were collected using a specially designed shooting device for the test shots revealing defined shooting distances between the weapon's muzzle and the target. The data obtained as X-ray spectra of a number of particles (3000 per ammunition brand) were reduced by Fast Fourier Transformation and subjected to a chemometric evaluation by means of regularized discriminant analysis. In addition to the scanning electron microscopy in combination with energy dispersive X-ray microanalysis results, isotope ratio measurements based on inductively coupled plasma analysis with mass-spectrometric detection were carried out to provide a supplementary feature for an even lower risk of misclassification.

A novel exploratory chemometric approach to environmental monitorring by combining block clustering with Partial Least Square (PLS) analysis

PubMed Central

2013-01-01

Background Given the serious threats posed to terrestrial ecosystems by industrial contamination, environmental monitoring is a standard procedure used for assessing the current status of an environment or trends in environmental parameters. Measurement of metal concentrations at different trophic levels followed by their statistical analysis using exploratory multivariate methods can provide meaningful information on the status of environmental quality. In this context, the present paper proposes a novel chemometric approach to standard statistical methods by combining the Block clustering with Partial least square (PLS) analysis to investigate the accumulation patterns of metals in anthropized terrestrial ecosystems. The present study focused on copper, zinc, manganese, iron, cobalt, cadmium, nickel, and lead transfer along a soil-plant-snai food chain, and the hepatopancreas of the Roman snail (Helix pomatia) was used as a biological end-point of metal accumulation. Results Block clustering deliniates between the areas exposed to industrial and vehicular contamination. The toxic metals have similar distributions in the nettle leaves and snail hepatopancreas. PLS analysis showed that (1) zinc and copper concentrations at the lower trophic levels are the most important latent factors that contribute to metal accumulation in land snails; (2) cadmium and lead are the main determinants of pollution pattern in areas exposed to industrial contamination; (3) at the sites located near roads lead is the most threatfull metal for terrestrial ecosystems. Conclusion There were three major benefits by applying block clustering with PLS for processing the obtained data: firstly, it helped in grouping sites depending on the type of contamination. Secondly, it was valuable for identifying the latent factors that contribute the most to metal accumulation in land snails. Finally, it optimized the number and type of data that are best for monitoring the status of metallic

Quantitative analysis of multiple high-resolution mass spectrometry images using chemometric methods: quantitation of chlordecone in mouse liver.

PubMed

Mohammadi, Saeedeh; Parastar, Hadi

2018-05-15

In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI). In this regard, quantification of chlordecone as a carcinogenic organochlorinated pesticide (C10Cll0O) in mouse liver using the matrix-assisted laser desorption ionization MSI (MALDI-MSI) method is used as a case study. The MSI datasets corresponded to 1, 5 and 10 days of mouse exposure to the standard chlordecone in the quantity range of 0 to 450 μg g-1. The binning approach in the m/z direction is used to group high resolution m/z values and to reduce the big data size. To consider the effect of bin size on the quality of results, three different bin sizes of 0.25, 0.5 and 1.0 were chosen. Afterwards, three-way MSI data arrays (two spatial and one m/z dimensions) for seven standards and four unknown samples were column-wise augmented with m/z values as the common mode. Then, these datasets were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) using proper constraints. The resolved mass spectra were used for identification of chlordecone in the presence of a complex background and interference. Additionally, the augmented spatial profiles were post-processed and 2D images for each component were obtained in calibration and unknown samples. The sum of these profiles was utilized to set the calibration curve and to obtain the analytical figures of merit (AFOMs). Inspection of the results showed that the lower bin size (i.e., 0.25) provides more accurate results. Finally, the obtained results by MCR for three datasets were compared with those of gas chromatography-mass spectrometry (GC-MS) and MALDI-MSI. The results showed that the MCR-assisted method gives a higher amount of chlordecone than MALDI-MSI and a lower amount than GC-MS. It is concluded that a combination of chemometric methods with MSI can be considered as an alternative way for MSI quantification.

Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin.

PubMed

Rathore, Anurag S; Mittal, Shachi; Lute, Scott; Brorson, Kurt

2012-01-01

Separation media, in particular chromatography media, is typically one of the major contributors to the cost of goods for production of a biotechnology therapeutic. To be cost-effective, it is industry practice that media be reused over several cycles before being discarded. The traditional approach for estimating the number of cycles a particular media can be reused for involves performing laboratory scale experiments that monitor column performance and carryover. This dataset is then used to predict the number of cycles the media can be used at manufacturing scale (concurrent validation). Although, well accepted and widely practiced, there are challenges associated with extrapolating the laboratory scale data to manufacturing scale due to differences that may exist across scales. Factors that may be different include: level of impurities in the feed material, lot to lot variability in feedstock impurities, design of the column housing unit with respect to cleanability, and homogeneity of the column packing. In view of these challenges, there is a need for approaches that may be able to predict column underperformance at the manufacturing scale over the product lifecycle. In case such an underperformance is predicted, the operators can unpack and repack the chromatography column beforehand and thus avoid batch loss. Chemometrics offers one such solution. In this article, we present an application of chemometrics toward the analysis of a set of chromatography profiles with the intention of predicting the various events of column underperformance including the backpressure buildup and inefficient deoxyribonucleic acid clearance. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

Determination and discrimination of biodiesel fuels by gas chromatographic and chemometric methods

NASA Astrophysics Data System (ADS)

Milina, R.; Mustafa, Z.; Bojilov, D.; Dagnon, S.; Moskovkina, M.

2016-03-01

Pattern recognition method (PRM) was applied to gas chromatographic (GC) data for a fatty acid methyl esters (FAME) composition of commercial and laboratory synthesized biodiesel fuels from vegetable oils including sunflower, rapeseed, corn and palm oils. Two GC quantitative methods to calculate individual fames were compared: Area % and internal standard. The both methods were applied for analysis of two certified reference materials. The statistical processing of the obtained results demonstrates the accuracy and precision of the two methods and allows them to be compared. For further chemometric investigations of biodiesel fuels by their FAME-profiles any of those methods can be used. PRM results of FAME profiles of samples from different vegetable oils show a successful recognition of biodiesels according to the feedstock. The information obtained can be used for selection of feedstock to produce biodiesels with certain properties, for assessing their interchangeability, for fuel spillage and remedial actions in the environment.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum

NASA Astrophysics Data System (ADS)

Fu, Haiyan; Yin, Qiaobo; Xu, Lu; Wang, Weizheng; Chen, Feng; Yang, Tianming

2017-07-01

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.

Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools

NASA Astrophysics Data System (ADS)

Judycka, U.; Jagiello, K.; Bober, L.; Błażejowski, J.; Puzyn, T.

2018-06-01

Chemometric tools were applied to investigate the biological behaviour of ampholytic substances in relation to their physicochemical and spectral properties. Results of the Principal Component Analysis suggest that size of molecules and their electronic and spectral characteristics are the key properties required to predict therapeutic relevance of the compounds examined. These properties were used for developing the structure-activity classification model. The classification model allows assessing the therapeutic behaviour of ampholytic substances on the basis of solely values of descriptors that can be obtained computationally. Thus, the prediction is possible without necessity of carrying out time-consuming and expensive laboratory tests, which is its main advantage.

At-line determination of pharmaceuticals small molecule's blending end point using chemometric modeling combined with Fourier transform near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish; Strong, Richard; Boulas, Pierre

2017-02-01

This article summarizes the development and validation of a Fourier transform near infrared spectroscopy (FT-NIR) method for the rapid at-line prediction of active pharmaceutical ingredient (API) in a powder blend to optimize small molecule formulations. The method was used to determine the blend uniformity end-point for a pharmaceutical solid dosage formulation containing a range of API concentrations. A set of calibration spectra from samples with concentrations ranging from 1% to 15% of API (w/w) were collected at-line from 4000 to 12,500 cm- 1. The ability of the FT-NIR method to predict API concentration in the blend samples was validated against a reference high performance liquid chromatography (HPLC) method. The prediction efficiency of four different types of multivariate data modeling methods such as partial least-squares 1 (PLS1), partial least-squares 2 (PLS2), principal component regression (PCR) and artificial neural network (ANN), were compared using relevant multivariate figures of merit. The prediction ability of the regression models were cross validated against results generated with the reference HPLC method. PLS1 and ANN showed excellent and superior prediction abilities when compared to PLS2 and PCR. Based upon these results and because of its decreased complexity compared to ANN, PLS1 was selected as the best chemometric method to predict blend uniformity at-line. The FT-NIR measurement and the associated chemometric analysis were implemented in the production environment for rapid at-line determination of the end-point of the small molecule blending operation. FIGURE 1: Correlation coefficient vs Rank plot FIGURE 2: FT-NIR spectra of different steps of Blend and final blend FIGURE 3: Predictions ability of PCR FIGURE 4: Blend uniformity predication ability of PLS2 FIGURE 5: Prediction efficiency of blend uniformity using ANN FIGURE 6: Comparison of prediction efficiency of chemometric models TABLE 1: Order of Addition for Blending Steps

Multi-Response Extraction Optimization Based on Anti-Oxidative Activity and Quality Evaluation by Main Indicator Ingredients Coupled with Chemometric Analysis on Thymus quinquecostatus Celak.

PubMed

Chang, Yan-Li; Shen, Meng; Ren, Xue-Yang; He, Ting; Wang, Le; Fan, Shu-Sheng; Wang, Xiu-Huan; Li, Xiao; Wang, Xiao-Ping; Chen, Xiao-Yi; Sui, Hong; She, Gai-Mei

2018-04-19

Thymus quinquecostatus Celak is a species of thyme in China and it used as condiment and herbal medicine for a long time. To set up the quality evaluation of T. quinquecostatus , the response surface methodology (RSM) based on its 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was introduced to optimize the extraction condition, and the main indicator components were found through an UPLC-LTQ-Orbitrap MS n method. The ethanol concentration, solid-liquid ratio, and extraction time on optimum conditions were 42.32%, 1:17.51, and 1.8 h, respectively. 35 components having 12 phenolic acids and 23 flavonoids were unambiguously or tentatively identified both positive and negative modes to employ for the comprehensive analysis in the optimum anti-oxidative part. A simple, reliable, and sensitive HPLC method was performed for the multi-component quantitative analysis of T. quinquecostatus using six characteristic and principal phenolic acids and flavonoids as reference compounds. Furthermore, the chemometrics methods (principal components analysis (PCA) and hierarchical clustering analysis (HCA)) appraised the growing areas and harvest time of this herb closely relative to the quality-controlled. This study provided full-scale qualitative and quantitative information for the quality evaluation of T. quinquecostatus , which would be a valuable reference for further study and development of this herb and related laid the foundation of further study on its pharmacological efficacy.

Chemometric classification of apple juices according to variety and geographical origin based on polyphenolic profiles.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong; Wang, Yutang

2013-07-17

To characterize and classify apple juices according to apple variety and geographical origin on the basis of their polyphenol composition, the polyphenolic profiles of 58 apple juice samples belonging to 5 apple varieties and from 6 regions in Shaanxi province of China were assessed. Fifty-one of the samples were from protected designation of origin (PDO) districts. Polyphenols were determined by high-performance liquid chromatography coupled to photodiode array detection (HPLC-PDA) and to a Q Exactive quadrupole-Orbitrap mass spectrometer. Chemometric techniques including principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were carried out on polyphenolic profiles of the samples to develop discrimination models. SLDA achieved satisfactory discriminations of apple juices according to variety and geographical origin, providing respectively 98.3 and 91.2% success rate in terms of prediction ability. This result demonstrated that polyphenols could served as characteristic indices to verify the variety and geographical origin of apple juices.

A comparative study of three tissue-cultured Dendrobium species and their wild correspondences by headspace gas chromatography-mass spectrometry combined with chemometric methods.

PubMed

Chen, Nai-Dong; You, Tao; Li, Jun; Bai, Li-Tao; Hao, Jing-Wen; Xu, Xiao-Yuan

2016-10-01

Plant tissue culture technique is widely used in the conservation and utilization of rare and endangered medicinal plants and it is crucial for tissue culture stocks to obtain the ability to produce similar bioactive components as their wild correspondences. In this paper, a headspace gas chromatography-mass spectrometry method combined with chemometric methods was applied to analyze and evaluate the volatile compounds in tissue-cultured and wild Dendrobium huoshanense Cheng and Tang, Dendrobium officinale Kimura et Migo and Dendrobium moniliforme (Linn.) Sw. In total, 63 volatile compounds were separated, with 53 being identified from the three Dendrobium spp. Different provenances of Dendrobiums had characteristic chemicals and showed remarkable quantity discrepancy of common compositions. The similarity evaluation disclosed that the accumulation of volatile compounds in Dendrobium samples might be affected by their provenance. Principal component analysis showed that the first three components explained 85.9% of data variance, demonstrating a good discrimination between samples. Gas chromatography-mass spectrometry techniques, combined with chemometrics, might be an effective strategy for identifying the species and their provenance, especially in the assessment of tissue-cultured Dendrobium quality for use in raw herbal medicines. Copyright © 2016. Published by Elsevier B.V.

Chemical Profiling of the Essential Oils of Syzygium aqueum, Syzygium samarangense and Eugenia uniflora and Their Discrimination Using Chemometric Analysis.

PubMed

Sobeh, Mansour; Braun, Markus Santhosh; Krstin, Sonja; Youssef, Fadia S; Ashour, Mohamed L; Wink, Michael

2016-11-01

The essential oil compositions of the leaves of three related Myrtaceae species, namely Syzygium aqueum, Syzygium samarangense and Eugenia uniflora, were investigated using GLC/MS and GLC/FID. Altogether, 125 compounds were identified: α-Selinene (13.85%), β-caryophyllene (12.72%) and β-selinene constitute the most abundant constituents in S. aqueum. Germacrene D (21.62%) represents the major compound in S. samarangense whereas in E. uniflora, spathulenol (15.80%) represents the predominant component. Multivariate chemometric analyses were used to discriminate the essential oils using hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on the chromatographic results. The antimicrobial activity of the popularly used E. uniflora essential oil was assessed using broth microdilution method against six Gram-positive, three Gram-negative bacteria and two fungi. The oil showed moderate antimicrobial activity against Bacillus licheniformis exhibiting MIC and MMC of 0.63 mg/ml. The cytotoxic activity of E. uniflora essential oil was investigated against Trypanosoma brucei brucei (T. b. brucei) and MCF-7 cancer cell line using MTT assay. It showed moderate activity against MCF-7 cells with an IC 50 value of 76.40 μg/ml. On the other hand, T. brucei was highly susceptible to E. uniflora essential oil with IC 50 of 11.20 μg/ml, and a selectivity index of 6.82. © 2016 Wiley-VHCA AG, Zurich, Switzerland.

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

PubMed

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

2018-04-01

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

Design and Optimization of a Chemometric-Assisted Spectrophotometric Determination of Telmisartan and Hydrochlorothiazide in Pharmaceutical Dosage Form

PubMed Central

Lakshmi, KS; Lakshmi, S

2010-01-01

Two chemometric methods were developed for the simultaneous determination of telmisartan and hydrochlorothiazide. The chemometric methods applied were principal component regression (PCR) and partial least square (PLS-1). These approaches were successfully applied to quantify the two drugs in the mixture using the information included in the UV absorption spectra of appropriate solutions in the range of 200-350 nm with the intervals Δλ = 1 nm. The calibration of PCR and PLS-1 models was evaluated by internal validation (prediction of compounds in its own designed training set of calibration) and by external validation over laboratory prepared mixtures and pharmaceutical preparations. The PCR and PLS-1 methods require neither any separation step, nor any prior graphical treatment of the overlapping spectra of the two drugs in a mixture. The results of PCR and PLS-1 methods were compared with each other and a good agreement was found. PMID:21331198

Design and optimization of a chemometric-assisted spectrophotometric determination of telmisartan and hydrochlorothiazide in pharmaceutical dosage form.

PubMed

Lakshmi, Ks; Lakshmi, S

2010-01-01

Two chemometric methods were developed for the simultaneous determination of telmisartan and hydrochlorothiazide. The chemometric methods applied were principal component regression (PCR) and partial least square (PLS-1). These approaches were successfully applied to quantify the two drugs in the mixture using the information included in the UV absorption spectra of appropriate solutions in the range of 200-350 nm with the intervals Δλ = 1 nm. The calibration of PCR and PLS-1 models was evaluated by internal validation (prediction of compounds in its own designed training set of calibration) and by external validation over laboratory prepared mixtures and pharmaceutical preparations. The PCR and PLS-1 methods require neither any separation step, nor any prior graphical treatment of the overlapping spectra of the two drugs in a mixture. The results of PCR and PLS-1 methods were compared with each other and a good agreement was found.

[Discrimination of donkey meat by NIR and chemometrics].

PubMed

Niu, Xiao-Ying; Shao, Li-Min; Dong, Fang; Zhao, Zhi-Lei; Zhu, Yan

2014-10-01

Donkey meat samples (n = 167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n = 47), pork (n = 51) and mutton (n = 32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4,000~12,500 cm(-1). The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98. 96% of prediction set. For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs, which gained an accuracy of 100% for calibration and 97.53% for prediction. For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction. And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs. And in these models, donkey meat samples were all correctly classified with 100% either in calibration or prediction. The results show that it is feasible that NIR with chemometrics methods is used to discriminate donkey meat from the else meat.

Chemometric formulation of bacterial consortium-AVS for improved decolorization of resonance-stabilized and heteropolyaromatic dyes.

PubMed

Kumar, Madhava Anil; Kumar, Vaidyanathan Vinoth; Premkumar, Manickam Periyaraman; Baskaralingam, Palanichamy; Thiruvengadaravi, Kadathur Varathachary; Dhanasekaran, Anuradha; Sivanesan, Subramanian

2012-11-01

A bacterial consortium-AVS, consisting of Pseudomonas desmolyticum NCIM 2112, Kocuria rosea MTCC 1532 and Micrococcus glutamicus NCIM 2168 was formulated chemometrically, using the mixture design matrix based on the design of experiments methodology. The formulated consortium-AVS decolorized acid blue 15 and methylene blue with a higher average decolorization rate, which is more rapid than that of the pure cultures. The UV-vis spectrophotometric, Fourier transform infra red spectrophotometric and high performance liquid chromatographic analysis confirm that the decolorization was due to biodegradation by oxido-reductive enzymes, produced by the consortium-AVS. The toxicological assessment of plant growth parameters and the chlorophyll pigment concentrations of Phaseolus mungo and Triticum aestivum seedlings revealed the reduced toxic nature of the biodegraded products. Copyright © 2012 Elsevier Ltd. All rights reserved.

Improved Discrimination for Brassica Vegetables Treated with Agricultural Fertilizers Using a Combined Chemometric Approach.

PubMed

Yuan, Yuwei; Hu, Guixian; Chen, Tianjin; Zhao, Ming; Zhang, Yongzhi; Li, Yong; Xu, Xiahong; Shao, Shengzhi; Zhu, Jiahong; Wang, Qiang; Rogers, Karyne M

2016-07-20

Multielement and stable isotope (δ(13)C, δ(15)N, δ(2)H, δ(18)O, (207)Pb/(206)Pb, and (208)Pb/(206)Pb) analyses were combined to provide a new chemometric approach to improve the discrimination between organic and conventional Brassica vegetable production. Different combinations of organic and conventional fertilizer treatments were used to demonstrate this authentication approach using Brassica chinensis planted in experimental test pots. Stable isotope analyses (δ(15)N and δ(13)C) of B. chinensis using elemental analyzer-isotope ratio mass spectrometry easily distinguished organic and chemical fertilizer treatments. However, for low-level application fertilizer treatments, this dual isotope approach became indistinguishable over time. Using a chemometric approach (combined isotope and elemental approach), organic and chemical fertilizer mixes and low-level applications of synthetic and organic fertilizers were detectable in B. chinensis and their associated soils, improving the detection limit beyond the capacity of individual isotopes or elemental characterization. LDA shows strong promise as an improved method to discriminate genuine organic Brassica vegetables from produce treated with chemical fertilizers and could be used as a robust test for organic produce authentication.

Modelling spatio-temporal heterogeneities in groundwater quality in Ghana: a multivariate chemometric approach.

PubMed

Armah, Frederick Ato; Paintsil, Arnold; Yawson, David Oscar; Adu, Michael Osei; Odoi, Justice O

2017-08-01

Chemometric techniques were applied to evaluate the spatial and temporal heterogeneities in groundwater quality data for approximately 740 goldmining and agriculture-intensive locations in Ghana. The strongest linear and monotonic relationships occurred between Mn and Fe. Sixty-nine per cent of total variance in the dataset was explained by four variance factors: physicochemical properties, bacteriological quality, natural geologic attributes and anthropogenic factors (artisanal goldmining). There was evidence of significant differences in means of all trace metals and physicochemical parameters (p < 0.001) between goldmining and non-goldmining locations. Arsenic and turbidity produced very high value F's demonstrating that 'physical properties and chalcophilic elements' was the function that most discriminated between non-goldmining and goldmining locations. Variations in Escherichia coli and total coliforms were observed between the dry and wet seasons. The overall predictive accuracy of the discriminant function showed that non-goldmining locations were classified with slightly better accuracy (89%) than goldmining areas (69.6%). There were significant differences between the underlying distributions of Cd, Mn and Pb in the wet and dry seasons. This study emphasizes the practicality of chemometrics in the assessment and elucidation of complex water quality datasets to promote effective management of groundwater resources for sustaining human health.

Experimental Design, Near-Infrared Spectroscopy, and Multivariate Calibration: An Advanced Project in a Chemometrics Course

ERIC Educational Resources Information Center

de Oliveira, Rodrigo R.; das Neves, Luiz S.; de Lima, Kassio M. G.

2012-01-01

A chemometrics course is offered to students in their fifth semester of the chemistry undergraduate program that includes an in-depth project. Students carry out the project over five weeks (three 8-h sessions per week) and conduct it in parallel to other courses or other practical work. The students conduct a literature search, carry out…

Chemometric assessment of enhanced bioremediation of oil contaminated soils.

PubMed

Soleimani, Mohsen; Farhoudi, Majid; Christensen, Jan H

2013-06-15

Bioremediation is a promising technique for reclamation of oil polluted soils. In this study, six methods for enhancing bioremediation were tested on oil contaminated soils from three refinery areas in Iran (Isfahan, Arak, and Tehran). The methods included bacterial enrichment, planting, and addition of nitrogen and phosphorous, molasses, hydrogen peroxide, and a surfactant (Tween 80). Total petroleum hydrocarbon (TPH) concentrations and CHEMometric analysis of Selected Ion Chromatograms (SIC) termed CHEMSIC method of petroleum biomarkers including terpanes, regular, diaromatic and triaromatic steranes were used for determining the level and type of hydrocarbon contamination. The same methods were used to study oil weathering of 2 to 6 ring polycyclic aromatic compounds (PACs). Results demonstrated that bacterial enrichment and addition of nutrients were most efficient with 50% to 62% removal of TPH. Furthermore, the CHEMSIC results demonstrated that the bacterial enrichment was more efficient in degradation of n-alkanes and low molecular weight PACs as well as alkylated PACs (e.g. C₃-C₄ naphthalenes, C₂ phenanthrenes and C₂-C₃ dibenzothiophenes), while nutrient addition led to a larger relative removal of isoprenoids (e.g. norpristane, pristane and phytane). It is concluded that the CHEMSIC method is a valuable tool for assessing bioremediation efficiency. Copyright © 2013 Elsevier B.V. All rights reserved.

¹H and ¹³C NMR-based sugar profiling with chemometric analysis and antioxidant activity of herbhoneys and honeys.

PubMed

Jamróz, Marta K; Paradowska, Katarzyna; Zawada, Katarzyna; Makarova, Katerina; Kaźmierski, Sławomir; Wawer, Iwona

2014-01-30

Herbhoneys, relatively new bee products, are expected to have interesting medicinal properties. However, there is still a lack of data concerning their composition and antioxidant properties. ¹H and ¹³C NMR spectroscopy coupled with chemometric analysis (PCA and PLS-DA) and antioxidant assays (DPPH-ESR and ORAC-FL) were used to study 25 samples of Polish herbhoneys and honeys. Antioxidant activity varied among the samples. The best properties were exhibited by cocoa and instant coffee herbhoneys. The contents of total polyphenols and total carotenoids in the studied samples were found to be 70-1340 mg GAE kg⁻¹ and 0-28.05 mg kg⁻¹ respectively. No significant differences between herbhoney and honey samples were found in their sugar profiles. The PCA of ¹³C NMR spectra of the samples in DMSO-d6 resulted in sample clustering due to sucrose content. Herbhoneys have similar antioxidant properties to traditional honeys, being therefore of equal nutritional value. There was a noticeable influence of the extract concentration on the observed antioxidant effect. For samples with high antioxidant activity, polyphenols were responsible for the observed effect. Sample clustering due to sucrose content in the NMR-PCA study allowed effortless detection of adulteration. © 2013 Society of Chemical Industry.

Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets

NASA Astrophysics Data System (ADS)

Wyche, K. P.; Monks, P. S.; Smallbone, K. L.; Hamilton, J. F.; Alfarra, M. R.; Rickard, A. R.; McFiggans, G. B.; Jenkin, M. E.; Bloss, W. J.; Ryan, A. C.; Hewitt, C. N.; MacKenzie, A. R.

2015-07-01

Highly non-linear dynamical systems, such as those found in atmospheric chemistry, necessitate hierarchical approaches to both experiment and modelling in order to ultimately identify and achieve fundamental process-understanding in the full open system. Atmospheric simulation chambers comprise an intermediate in complexity, between a classical laboratory experiment and the full, ambient system. As such, they can generate large volumes of difficult-to-interpret data. Here we describe and implement a chemometric dimension reduction methodology for the deconvolution and interpretation of complex gas- and particle-phase composition spectra. The methodology comprises principal component analysis (PCA), hierarchical cluster analysis (HCA) and positive least-squares discriminant analysis (PLS-DA). These methods are, for the first time, applied to simultaneous gas- and particle-phase composition data obtained from a comprehensive series of environmental simulation chamber experiments focused on biogenic volatile organic compound (BVOC) photooxidation and associated secondary organic aerosol (SOA) formation. We primarily investigated the biogenic SOA precursors isoprene, α-pinene, limonene, myrcene, linalool and β-caryophyllene. The chemometric analysis is used to classify the oxidation systems and resultant SOA according to the controlling chemistry and the products formed. Results show that "model" biogenic oxidative systems can be successfully separated and classified according to their oxidation products. Furthermore, a holistic view of results obtained across both the gas- and particle-phases shows the different SOA formation chemistry, initiating in the gas-phase, proceeding to govern the differences between the various BVOC SOA compositions. The results obtained are used to describe the particle composition in the context of the oxidised gas-phase matrix. An extension of the technique, which incorporates into the statistical models data from anthropogenic (i

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics.

PubMed

Song, Yuqiao; Liao, Jie; Dong, Junxing; Chen, Li

2015-09-01

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M-H](-) . Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time-consuming methods for grape seed fingerprinting. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics.

PubMed

Wang, Zhengfang; Jablonski, Joseph E

2016-01-01

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.

Least-Squares Regression and Spectral Residual Augmented Classical Least-Squares Chemometric Models for Stability-Indicating Analysis of Agomelatine and Its Degradation Products: A Comparative Study.

PubMed

Naguib, Ibrahim A; Abdelrahman, Maha M; El Ghobashy, Mohamed R; Ali, Nesma A

2016-01-01

Two accurate, sensitive, and selective stability-indicating methods are developed and validated for simultaneous quantitative determination of agomelatine (AGM) and its forced degradation products (Deg I and Deg II), whether in pure forms or in pharmaceutical formulations. Partial least-squares regression (PLSR) and spectral residual augmented classical least-squares (SRACLS) are two chemometric models that are being subjected to a comparative study through handling UV spectral data in range (215-350 nm). For proper analysis, a three-factor, four-level experimental design was established, resulting in a training set consisting of 16 mixtures containing different ratios of interfering species. An independent test set consisting of eight mixtures was used to validate the prediction ability of the suggested models. The results presented indicate the ability of mentioned multivariate calibration models to analyze AGM, Deg I, and Deg II with high selectivity and accuracy. The analysis results of the pharmaceutical formulations were statistically compared to the reference HPLC method, with no significant differences observed regarding accuracy and precision. The SRACLS model gives comparable results to the PLSR model; however, it keeps the qualitative spectral information of the classical least-squares algorithm for analyzed components.

Tracking thermal degradation on passion fruit juice through Nuclear Magnetic Resonance and chemometrics.

PubMed

Soares, Marcia Valeria L; Alves Filho, Elenilson G; Silva, Lorena Mara A; Novotny, Etelvino Henrique; Canuto, Kirley Marques; Wurlitzer, Nedio Jair; Narain, Narendra; de Brito, Edy Sousa

2017-03-15

Thermal food processing mainly aims to control microorganism in order to extend its shelf life. However, it may induce chemical and nutritional changes in foodstuff. The Nuclear Magnetic Resonance (NMR) coupled to multivariate analysis was used to evaluate the effect of different thermal processing conditions (85 and 140°C for 4; 15; 30; and 60s) on the passion fruit juice using an Armfield pasteurizer. Through this approach it was possible to identify the changes in the juice composition. The temperature and the time lead to a hydrolysis of the sucrose to glucose and fructose. Additionally, juice submitted to 140°C for 60s results in the degradation of the sucrose and the formation of 5-(hydroxymethyl)-2-furfural (HMF). Despite no novel chemical marker has been identified, the 1 H NMR chemometrics approach may contribute in the choice of the temperature and time to be employed in the juice processing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chinese vinegar classification via volatiles using long-optical-path infrared spectroscopy and chemometrics.

PubMed

Dong, D; Zheng, W; Jiao, L; Lang, Y; Zhao, X

2016-03-01

Different brands of Chinese vinegar are similar in appearance, color and aroma, making their discrimination difficult. The compositions and concentrations of the volatiles released from different vinegars vary by raw material and brewing process and thus offer a means to discriminate vinegars. In this study, we enhanced the detection sensitivity of the infrared spectrometer by extending its optical path. We measured the infrared spectra of the volatiles from 5 brands of Chinese vinegar and observed the spectral characteristics corresponding to alcohols, esters, acids, furfural, etc. Different brands of Chinese vinegar had obviously different infrared spectra and could be classified through chemometrics analysis. Furthermore, we established classification models and demonstrated their effectiveness for classifying different brands of vinegar. This study demonstrates that long-optical-path infrared spectroscopy has the ability to discriminate Chinese vinegars with the advantages that it is fast and non-destructive and eliminates the need for sampling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality control of the paracetamol drug by chemometrics and imaging spectroscopy in the near infrared region

NASA Astrophysics Data System (ADS)

Baptistao, Mariana; Rocha, Werickson Fortunato de Carvalho; Poppi, Ronei Jesus

2011-09-01

In this work, it was used imaging spectroscopy and chemometric tools for the development and analysis of paracetamol and excipients in pharmaceutical formulations. It was also built concentration maps to study the distribution of the drug in the tablets surface. Multivariate models based on PLS regression were developed for paracetamol and excipients concentrations prediction. For the construction of the models it was used 31 samples in the tablet form containing the active principle in a concentration range of 30.0-90.0% (w/w) and errors below to 5% were obtained for validation samples. Finally, the study of the distribution in the drug was performed through the distribution maps of concentration of active principle and excipients. The analysis of maps showed the complementarity between the active principle and excipients in the tablets. The region with a high concentration of a constituent must have, necessarily, absence or low concentration of the other one. Thus, an alternative method for the paracetamol drug quality monitoring is presented.

A new simplex chemometric approach to identify olive oil blends with potentially high traceability.

PubMed

Semmar, N; Laroussi-Mezghani, S; Grati-Kamoun, N; Hammami, M; Artaud, J

2016-10-01

Olive oil blends (OOBs) are complex matrices combining different cultivars at variable proportions. Although qualitative determinations of OOBs have been subjected to several chemometric works, quantitative evaluations of their contents remain poorly developed because of traceability difficulties concerning co-occurring cultivars. Around this question, we recently published an original simplex approach helping to develop predictive models of the proportions of co-occurring cultivars from chemical profiles of resulting blends (Semmar & Artaud, 2015). Beyond predictive model construction and validation, this paper presents an extension based on prediction errors' analysis to statistically define the blends with the highest predictability among all the possible ones that can be made by mixing cultivars at different proportions. This provides an interesting way to identify a priori labeled commercial products with potentially high traceability taking into account the natural chemical variability of different constitutive cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chemometric profile, antioxidant and tyrosinase inhibitory activity of Camel's foot creeper leaves (Bauhinia vahlii).

PubMed

Panda, Pritipadma; Dash, Priyanka; Ghosh, Goutam

2018-03-01

The present study is the first effort to a comprehensive evaluation of antityrosinase activity and chemometric analysis of Bauhinia vahlii. The experimental results revealed that the methanol extract of Bauhinia vahlii (BVM) possesses higher polyphenolic compounds and total antioxidant activity than those reported elsewhere for other more conventionally and geographically different varieties. The BVM contain saturated fatty acids such as hexadecanoic acid (10.15%), octadecanoic acid (1.97%), oleic acid (0.61%) and cis-vaccenic acid (2.43%) along with vitamin E (12.71%), α-amyrin (9.84%), methyl salicylate (2.39%) and β-sitosterol (17.35%), which were mainly responsible for antioxidant as well as tyrosinase inhibitory activity. Tyrosinase inhibitory activity of this extract was comparable to that of Kojic acid. These findings suggested that the B. vahlii leaves could be exploited as potential source of natural antioxidant and tyrosinase inhibitory agent, as well.

Detection of l-Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA.

PubMed

Cebi, Nur; Dogan, Canan Ekinci; Develioglu, Ayşen; Yayla, Mediha Esra Altuntop; Sagdic, Osman

2017-08-01

l-Cysteine is deliberately added to various flour types since l-Cysteine has enabled favorable baking conditions such as low viscosity, increased elasticity and rise during baking. In Turkey, usage of l-Cysteine as a food additive isn't allowed in wheat flour according to the Turkish Food Codex Regulation on food additives. There is an urgent need for effective methods to detect l-Cysteine in wheat flour. In this study, for the first time, a new, rapid, effective, non-destructive and cost-effective method was developed for detection of l-Cysteine in wheat flour using Raman microscopy. Detection of l-Cysteine in wheat flour was accomplished successfully using Raman microscopy combined chemometrics of PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis). In this work, 500-2000cm -1 spectral range (fingerprint region) was determined to perform PCA and HCA analysis. l-Cysteine and l-Cystine were determined with detection limit of 0.125% (w/w) in different wheat flour samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Chemometrics-based process analytical technology (PAT) tools: applications and adaptation in pharmaceutical and biopharmaceutical industries.

PubMed

Challa, Shruthi; Potumarthi, Ravichandra

2013-01-01

Process analytical technology (PAT) is used to monitor and control critical process parameters in raw materials and in-process products to maintain the critical quality attributes and build quality into the product. Process analytical technology can be successfully implemented in pharmaceutical and biopharmaceutical industries not only to impart quality into the products but also to prevent out-of-specifications and improve the productivity. PAT implementation eliminates the drawbacks of traditional methods which involves excessive sampling and facilitates rapid testing through direct sampling without any destruction of sample. However, to successfully adapt PAT tools into pharmaceutical and biopharmaceutical environment, thorough understanding of the process is needed along with mathematical and statistical tools to analyze large multidimensional spectral data generated by PAT tools. Chemometrics is a chemical discipline which incorporates both statistical and mathematical methods to obtain and analyze relevant information from PAT spectral tools. Applications of commonly used PAT tools in combination with appropriate chemometric method along with their advantages and working principle are discussed. Finally, systematic application of PAT tools in biopharmaceutical environment to control critical process parameters for achieving product quality is diagrammatically represented.

Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics

PubMed Central

Li, Xiaoli; Zhang, Yuying; He, Yong

2016-01-01

This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea. PMID:27468701

Rapid authentication of edible bird's nest by FTIR spectroscopy combined with chemometrics.

PubMed

Guo, Lili; Wu, Yajun; Liu, Mingchang; Ge, Yiqiang; Chen, Ying

2018-06-01

Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. For economic reasons, they are frequently subjected to adulteration with some cheaper substitutes, such as Tremella fungus, agar, fried pigskin, and egg white. As a kind of precious and functional product, it is necessary to establish a robust method for the rapid authentication of EBNs with small amounts of samples by simple processes. In this study, the Fourier transform infrared spectroscopy (FTIR) system was utilized and its feasibility for identification of EBNs was verified. FTIR spectra data of authentic and adulterated EBNs were analyzed by chemometrics analyses including principal component analysis, linear discriminant analysis (LDA), support vector machine (SVM) and one-class partial least squares (OCPLS). The results showed that the established LDA and SVM models performed well and had satisfactory classification ability, with the former 94.12% and the latter 100%. The OCPLS model was developed with prediction sensitivity of 0.937 and specificity of 0.886. Further detection of commercial EBN samples confirmed these results. FTIR is applicable in the scene of rapid authentication of EBNs, especially for quality supervision departments, entry-exit inspection and quarantine, and customs administration. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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.

Study of the aroma formation and transformation during the manufacturing process of oolong tea by solid-phase micro-extraction and gas chromatography-mass spectrometry combined with chemometrics.

PubMed

Ma, Chengying; Li, Junxing; Chen, Wei; Wang, Wenwen; Qi, Dandan; Pang, Shi; Miao, Aiqing

2018-06-01

Oolong tea is a typical semi-fermented tea and is famous for its unique aroma. The aim of this study was to compare the volatile compounds during manufacturing process to reveal the formation of aroma. In this paper, a method was developed based on head-space solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) combined with chemometrics to assess volatile profiles during manufacturing process (fresh leaves, sun-withered leaves, rocked leaves and leaves after de-enzyming). A total of 24 aroma compounds showing significant differences during manufacturing process were identified. Subsequently, according to these aroma compounds, principal component analysis and hierarchical cluster analysis showed that the four samples were clearly distinguished from each other, which suggested that the 24 identified volatile compounds can represent the changes of volatile compounds during the four steps. Additionally, sun-withering, rocking and de-enzyming can influence the variations of volatile compounds in different degree, and we found the changes of volatile compounds in withering step were less than other two manufacturing process, indicating that the characteristic volatile compounds of oolong tea might be mainly formed in rocking stage by biological reactions and de-enzyming stage through thermal chemical transformations rather than withering stage. This study suggested that HS-SPME/GC-MS combined with chemometrics methods is accurate, sensitive, fast and ideal for rapid routine analysis of the aroma compounds changes in oolong teas during manufacturing processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of chemometric methods for assessment and modelling of microbiological quality data concerning coastal bathing water in Greece.

PubMed

Papaioannou, Agelos; Rigas, George; Papastergiou, Panagiotis; Hadjichristodoulou, Christos

2014-12-02

Worldwide, the aim of managing water is to safeguard human health whilst maintaining sustainable aquatic and associated terrestrial, ecosystems. Because human enteric viruses are the most likely pathogens responsible for waterborne diseases from recreational water use, but detection methods are complex and costly for routine monitoring, it is of great interest to determine the quality of coastal bathing water with a minimum cost and maximum safety. This study handles the assessment and modelling of the microbiological quality data of 2149 seawater bathing areas in Greece over 10-year period (1997-2006) by chemometric methods. Cluster analysis results indicated that the studied bathing beaches are classified in accordance with the seasonality in three groups. Factor analysis was applied to investigate possible determining factors in the groups resulted from the cluster analysis, and also two new parameters were created in each group; VF1 includes E. coli, faecal coliforms and total coliforms and VF2 includes faecal streptococci/enterococci. By applying the cluster analysis in each seasonal group, three new groups of coasts were generated, group A (ultraclean), group B (clean) and group C (contaminated). The above analysis is confirmed by the application of discriminant analysis, and proves that chemometric methods are useful tools for assessment and modeling microbiological quality data of coastal bathing water on a large scale, and thus could attribute to effective and economical monitoring of the quality of coastal bathing water in a country with a big number of bathing coasts, like Greece. Significance for public healthThe microbiological protection of coastal bathing water quality is of great interest for the public health authorities as well as for the economy. The present study proves that this protection can be achieved by monitoring only two microbiological parameters, E. coli and faecal streptococci/enterococci instead four microbiological parameters (the

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."

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy.

PubMed

Ribeiro, J S; Ferreira, M M C; Salva, T J G

2011-02-15

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein. Copyright © 2010 Elsevier B.V. All rights reserved.

Quality evaluation of moluodan concentrated pill using high-performance liquid chromatography fingerprinting coupled with chemometrics.

PubMed

Tao, Lingyan; Zhang, Qing; Wu, Yongjiang; Liu, Xuesong

2016-12-01

In this study, a fast and effective high-performance liquid chromatography method was developed to obtain a fingerprint chromatogram and quantitative analysis simultaneously of four indexes including gallic acid, chlorogenic acid, albiflorin and paeoniflorin of the traditional Chinese medicine Moluodan Concentrated Pill. The method was performed by using a Waters X-bridge C 18 reversed phase column on an Agilent 1200S high-performance liquid chromatography system coupled with diode array detection. The mobile phase of the high-performance liquid chromatography method was composed of 20 mmol/L phosphate solution and acetonitrile with a 1 mL/min eluent velocity, under a detection temperature of 30°C and a UV detection wavelength of 254 nm. After the methodology validation, 16 batches of Moluodan Concentrated Pill were analyzed by this high-performance liquid chromatography method and both qualitative and quantitative evaluation results were achieved by similarity analysis, principal component analysis and hierarchical cluster analysis. The results of these three chemometrics were in good agreement and all indicated that batch 10 and batch 16 showed significant differences with the other 14 batches. This suggested that the developed high-performance liquid chromatography method could be applied in the quality evaluation of Moluodan Concentrated Pill. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography for simultaneous determination of seven β-blockers in their pharmaceutical products: A comparative study

NASA Astrophysics Data System (ADS)

Abdel Hameed, Eman A.; Abdel Salam, Randa A.; Hadad, Ghada M.

2015-04-01

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography (HPLC) were developed for the simultaneous determination of the seven most commonly prescribed β-blockers (atenolol, sotalol, metoprolol, bisoprolol, propranolol, carvedilol and nebivolol). Principal component regression PCR, partial least square PLS and PLS with previous wavelength selection by genetic algorithm (GA-PLS) were used for chemometric analysis of spectral data of these drugs. The compositions of the mixtures used in the calibration set were varied to cover the linearity ranges 0.7-10 μg ml-1 for AT, 1-15 μg ml-1 for ST, 1-15 μg ml-1 for MT, 0.3-5 μg ml-1 for BS, 0.1-3 μg ml-1 for PR, 0.1-3 μg ml-1 for CV and 0.7-5 μg ml-1 for NB. The analytical performances of these chemometric methods were characterized by relative prediction errors and were compared with each other. GA-PLS showed superiority over the other applied multivariate methods due to the wavelength selection. A new gradient HPLC method had been developed using statistical experimental design. Optimum conditions of separation were determined with the aid of central composite design. The developed HPLC method was found to be linear in the range of 0.2-20 μg ml-1 for AT, 0.2-20 μg ml-1 for ST, 0.1-15 μg ml-1 for MT, 0.1-15 μg ml-1 for BS, 0.1-13 μg ml-1 for PR, 0.1-13 μg ml-1 for CV and 0.4-20 μg ml-1 for NB. No significant difference between the results of the proposed GA-PLS and HPLC methods with respect to accuracy and precision. The proposed analytical methods did not show any interference of the excipients when applied to pharmaceutical products.

Classification and identification of Rhodobryum roseum Limpr. and its adulterants based on fourier-transform infrared spectroscopy (FTIR) and chemometrics.

PubMed

Cao, Zhen; Wang, Zhenjie; Shang, Zhonglin; Zhao, Jiancheng

2017-01-01

Fourier-transform infrared spectroscopy (FTIR) with the attenuated total reflectance technique was used to identify Rhodobryum roseum from its four adulterants. The FTIR spectra of six samples in the range from 4000 cm-1 to 600 cm-1 were obtained. The second-derivative transformation test was used to identify the small and nearby absorption peaks. A cluster analysis was performed to classify the spectra in a dendrogram based on the spectral similarity. Principal component analysis (PCA) was used to classify the species of six moss samples. A cluster analysis with PCA was used to identify different genera. However, some species of the same genus exhibited highly similar chemical components and FTIR spectra. Fourier self-deconvolution and discrete wavelet transform (DWT) were used to enhance the differences among the species with similar chemical components and FTIR spectra. Three scales were selected as the feature-extracting space in the DWT domain. The results show that FTIR spectroscopy with chemometrics is suitable for identifying Rhodobryum roseum and its adulterants.

Chemometric expertise of the quality of groundwater sources for domestic use.

PubMed

Spanos, Thomas; Ene, Antoaneta; Simeonova, Pavlina

2015-01-01

In the present study 49 representative sites have been selected for the collection of water samples from central water supplies with different geographical locations in the region of Kavala, Northern Greece. Ten physicochemical parameters (pH, electric conductivity, nitrate, chloride, sodium, potassium, total alkalinity, total hardness, bicarbonate and calcium) were analyzed monthly, in the period from January 2010 to December 2010. Chemometric methods were used for monitoring data mining and interpretation (cluster analysis, principal components analysis and source apportioning by principal components regression). The clustering of the chemical indicators delivers two major clusters related to the water hardness and the mineral components (impacted by sea, bedrock and acidity factors). The sampling locations are separated into three major clusters corresponding to the spatial distribution of the sites - coastal, lowland and semi-mountainous. The principal components analysis reveals two latent factors responsible for the data structures, which are also an indication for the sources determining the groundwater quality of the region (conditionally named "mineral" factor and "water hardness" factor). By the apportionment approach it is shown what the contribution is of each of the identified sources to the formation of the total concentration of each one of the chemical parameters. The mean values of the studied physicochemical parameters were found to be within the limits given in the 98/83/EC Directive. The water samples are appropriate for human consumption. The results of this study provide an overview of the hydrogeological profile of water supply system for the studied area.

Chemical Sensing in Process Analysis.

ERIC Educational Resources Information Center

Hirschfeld, T.; And Others

1984-01-01

Discusses: (1) rationale for chemical sensors in process analysis; (2) existing types of process chemical sensors; (3) sensor limitations, considering lessons of chemometrics; (4) trends in process control sensors; and (5) future prospects. (JN)

Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data--a review.

PubMed

Bosque-Sendra, Juan M; Cuadros-Rodríguez, Luis; Ruiz-Samblás, Cristina; de la Mata, A Paulina

2012-04-29

The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee. Copyright © 2012 Elsevier B.V. All rights reserved.

Combination of near infrared spectroscopy and chemometrics for authentication of taro flour from wheat and sago flour

NASA Astrophysics Data System (ADS)

Rachmawati; Rohaeti, E.; Rafi, M.

2017-05-01

Taro flour on the market is usually sold at higher price than wheat and sago flour. This situation could be a cause for adulteration of taro flour from wheat and sago flour. For this reason, we will need an identification and authentication. Combination of near infrared (NIR) spectrum with multivariate analysis was used in this study to identify and authenticate taro flour from wheat and sago flour. The authentication model of taro flour was developed by using a mixture of 5%, 25%, and 50% of adulterated taro flour from wheat and sago flour. Before subjected to multivariate analysis, an initial preprocessing signal was used namely normalization and standard normal variate to the NIR spectrum. We used principal component analysis followed by discriminant analysis to make an identification and authentication model of taro flour. From the result obtained, about 90.48% of the taro flour mixed with wheat flour and 85% of taro flour mixed with sago flour were successfully classified into their groups. So the combination of NIR spectrum with chemometrics could be used for identification and authentication of taro flour from wheat and sago flour.

Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph.

PubMed

Saidemberg, Daniel M; Baptista-Saidemberg, Nicoli B; Palma, Mario S

2011-09-01

When searching for prospective novel peptides, it is difficult to determine the biological activity of a peptide based only on its sequence. The "trial and error" approach is generally laborious, expensive and time consuming due to the large number of different experimental setups required to cover a reasonable number of biological assays. To simulate a virtual model for Hymenoptera insects, 166 peptides were selected from the venoms and hemolymphs of wasps, bees and ants and applied to a mathematical model of multivariate analysis, with nine different chemometric components: GRAVY, aliphaticity index, number of disulfide bonds, total residues, net charge, pI value, Boman index, percentage of alpha helix, and flexibility prediction. Principal component analysis (PCA) with non-linear iterative projections by alternating least-squares (NIPALS) algorithm was performed, without including any information about the biological activity of the peptides. This analysis permitted the grouping of peptides in a way that strongly correlated to the biological function of the peptides. Six different groupings were observed, which seemed to correspond to the following groups: chemotactic peptides, mastoparans, tachykinins, kinins, antibiotic peptides, and a group of long peptides with one or two disulfide bonds and with biological activities that are not yet clearly defined. The partial overlap between the mastoparans group and the chemotactic peptides, tachykinins, kinins and antibiotic peptides in the PCA score plot may be used to explain the frequent reports in the literature about the multifunctionality of some of these peptides. The mathematical model used in the present investigation can be used to predict the biological activities of novel peptides in this system, and it may also be easily applied to other biological systems. Copyright © 2011 Elsevier Inc. All rights reserved.

STATISTICAL SAMPLING AND DATA ANALYSIS

EPA Science Inventory

Research is being conducted to develop approaches to improve soil and sediment sampling techniques, measurement design and geostatistics, and data analysis via chemometric, environmetric, and robust statistical methods. Improvements in sampling contaminated soil and other hetero...

Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies.

PubMed

Tan, Jin; Li, Rong; Jiang, Zi-Tao

2015-10-01

We report an application of data fusion for chemometric classification of 135 canned samples of Chinese lager beers by manufacturer based on the combination of fluorescence, UV and visible spectroscopies. Right-angle synchronous fluorescence spectra (SFS) at three wavelength difference Δλ=30, 60 and 80 nm and visible spectra in the range 380-700 nm of undiluted beers were recorded. UV spectra in the range 240-400 nm of diluted beers were measured. A classification model was built using principal component analysis (PCA) and linear discriminant analysis (LDA). LDA with cross-validation showed that the data fusion could achieve 78.5-86.7% correct classification (sensitivity), while those rates using individual spectroscopies ranged from 42.2% to 70.4%. The results demonstrated that the fluorescence, UV and visible spectroscopies complemented each other, yielding higher synergic effect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality assessment of crude and processed Arecae semen based on colorimeter and HPLC combined with chemometrics methods.

PubMed

Sun, Meng; Yan, Donghui; Yang, Xiaolu; Xue, Xingyang; Zhou, Sujuan; Liang, Shengwang; Wang, Shumei; Meng, Jiang

2017-05-01

Raw Arecae Semen, the seed of Areca catechu L., as well as Arecae Semen Tostum and Arecae semen carbonisata are traditionally processed by stir-baking for subsequent use in a variety of clinical applications. These three Arecae semen types, important Chinese herbal drugs, have been used in China and other Asian countries for thousands of years. In this study, the sensory technologies of a colorimeter and sensitive validated high-performance liquid chromatography with diode array detection were employed to discriminate raw Arecae semen and its processed drugs. The color parameters of the samples were determined by a colorimeter instrument CR-410. Moreover, the fingerprints of the four alkaloids of arecaidine, guvacine, arecoline and guvacoline were surveyed by high-performance liquid chromatography. Subsequently, Student's t test, the analysis of variance, fingerprint similarity analysis, hierarchical cluster analysis, principal component analysis, factor analysis and Pearson's correlation test were performed for final data analysis. The results obtained demonstrated a significant color change characteristic for components in raw Arecae semen and its processed drugs. Crude and processed Arecae semen could be determined based on colorimetry and high-performance liquid chromatography with a diode array detector coupled with chemometrics methods for a comprehensive quality evaluation. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Assessment of sediment quality in the Mediterranean Sea-Boughrara lagoon exchange areas (southeastern Tunisia): GIS approach-based chemometric methods.

PubMed

Kharroubi, Adel; Gargouri, Dorra; Baati, Houda; Azri, Chafai

2012-06-01

Concentrations of selected heavy metals (Cd, Pb, Zn, Cu, Mn, and Fe) in surface sediments from 66 sites in both northern and eastern Mediterranean Sea-Boughrara lagoon exchange areas (southeastern Tunisia) were studied in order to understand current metal contamination due to the urbanization and economic development of nearby several coastal regions of the Gulf of Gabès. Multiple approaches were applied for the sediment quality assessment. These approaches were based on GIS coupled with chemometric methods (enrichment factors, geoaccumulation index, principal component analysis, and cluster analysis). Enrichment factors and principal component analysis revealed two distinct groups of metals. The first group corresponded to Fe and Mn derived from natural sources, and the second group contained Cd, Pb, Zn, and Cu originated from man-made sources. For these latter metals, cluster analysis showed two distinct distributions in the selected areas. They were attributed to temporal and spatial variations of contaminant sources input. The geoaccumulation index (I (geo)) values explained that only Cd, Pb, and Cu can be considered as moderate to extreme pollutants in the studied sediments.

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

PubMed

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

2014-03-01

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

Chemometric differentiation of crude oil families in the San Joaquin Basin, California

USGS Publications Warehouse

Peters, Kenneth E.; Coutrot, Delphine; Nouvelle, Xavier; Ramos, L. Scott; Rohrback, Brian G.; Magoon, Leslie B.; Zumberge, John E.

2013-01-01

Chemometric analyses of geochemical data for 165 crude oil samples from the San Joaquin Basin identify genetically distinct oil families and their inferred source rocks and provide insight into migration pathways, reservoir compartments, and filling histories. In the first part of the study, 17 source-related biomarker and stable carbon-isotope ratios were evaluated using a chemometric decision tree (CDT) to identify families. In the second part, ascendant hierarchical clustering was applied to terpane mass chromatograms for the samples to compare with the CDT results. The results from the two methods are remarkably similar despite differing data input and assumptions. Recognized source rocks for the oil families include the (1) Eocene Kreyenhagen Formation, (2) Eocene Tumey Formation, (3–4) upper and lower parts of the Miocene Monterey Formation (Buttonwillow depocenter), and (5–6) upper and lower parts of the Miocene Monterey Formation (Tejon depocenter). Ascendant hierarchical clustering identifies 22 oil families in the basin as corroborated by independent data, such as carbon-isotope ratios, sample location, reservoir unit, and thermal maturity maps from a three-dimensional basin and petroleum system model. Five families originated from the Eocene Kreyenhagen Formation source rock, and three families came from the overlying Eocene Tumey Formation. Fourteen families migrated from the upper and lower parts of the Miocene Monterey Formation source rocks within the Buttonwillow and Tejon depocenters north and south of the Bakersfield arch. The Eocene and Miocene families show little cross-stratigraphic migration because of seals within and between the source rocks. The data do not exclude the possibility that some families described as originating from the Monterey Formation actually came from source rock in the Temblor Formation.

Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China

NASA Astrophysics Data System (ADS)

Wu, Meilin; Wang, Youshao; Dong, Junde; Sun, Fulin; Wang, Yutu; Hong, Yiguo

2017-03-01

A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organizing Map (SOM) were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component (PC1) were related to NH4-N, PO4-P, NO2-N, NO3-N, TP, and Chlorophyll a while salinity, turbidity, and SiO3-Si in the second principal component (PC2). Similarly, the scores of stations in the bottom layers in PC1 were related to PO4-P, NO2-N, NO3-N, and TP, while salinity, Chlorophyll a, NH4-N, and SiO3-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.

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

PubMed

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

2017-09-01

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

Chemometric investigation of light-shade effects on essential oil yield and morphology of Moroccan Myrtus communis L.

PubMed

Fadil, Mouhcine; Farah, Abdellah; Ihssane, Bouchaib; Haloui, Taoufik; Lebrazi, Sara; Zghari, Badreddine; Rachiq, Saâd

2016-01-01

To investigate the effect of environmental factors such as light and shade on essential oil yield and morphological traits of Moroccan Myrtus communis, a chemometric study was conducted on 20 individuals growing under two contrasting light environments. The study of individual's parameters by principal component analysis has shown that essential oil yield, altitude, and leaves thickness were positively correlated between them and negatively correlated with plants height, leaves length and leaves width. Principal component analysis and hierarchical cluster analysis have also shown that the individuals of each sampling site were grouped separately. The one-way ANOVA test has confirmed the effect of light and shade on essential oil yield and morphological parameters by showing a statistically significant difference between them from the shaded side to the sunny one. Finally, the multiple linear model containing main, interaction and quadratic terms was chosen for the modeling of essential oil yield in terms of morphological parameters. Sun plants have a small height, small leaves length and width, but they are thicker and richer in essential oil than shade plants which have shown almost the opposite. The highlighted multiple linear model can be used to predict essential oil yield in the studied area.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

PubMed Central

Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China. PMID:29410795

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods.

PubMed

Guo, Xiuhan; Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber ( Apostichopus japonicus ) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

Fourier transform infrared spectroscopy combined with chemometrics for discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingiber cassumunar

NASA Astrophysics Data System (ADS)

Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi

2015-02-01

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm-1). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species.

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.

Chemometric study of Andalusian extra virgin olive oils Raman spectra: Qualitative and quantitative information.

PubMed

Sánchez-López, E; Sánchez-Rodríguez, M I; Marinas, A; Marinas, J M; Urbano, F J; Caridad, J M; Moalem, M

2016-08-15

Authentication of extra virgin olive oil (EVOO) is an important topic for olive oil industry. The fraudulent practices in this sector are a major problem affecting both producers and consumers. This study analyzes the capability of FT-Raman combined with chemometric treatments of prediction of the fatty acid contents (quantitative information), using gas chromatography as the reference technique, and classification of diverse EVOOs as a function of the harvest year, olive variety, geographical origin and Andalusian PDO (qualitative information). The optimal number of PLS components that summarizes the spectral information was introduced progressively. For the estimation of the fatty acid composition, the lowest error (both in fitting and prediction) corresponded to MUFA, followed by SAFA and PUFA though such errors were close to zero in all cases. As regards the qualitative variables, discriminant analysis allowed a correct classification of 94.3%, 84.0%, 89.0% and 86.6% of samples for harvest year, olive variety, geographical origin and PDO, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2016-02-01

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer.

Differentiation of Aurantii Fructus Immaturus from Poniciri Trifoliatae Fructus Immaturus using Flow- injection Mass spectrometric (FIMS) Metabolic Fingerprinting Method Combined with Chemometrics

PubMed Central

Zhao, Yang; Chang, Yuan-Shiun; Chen, Pei

2015-01-01

A flow-injection mass spectrometric metabolic fingerprinting method in combination with chemometrics was used to differentiate Aurantii Fructus Immaturus from its counterfeit Poniciri Trifoliatae Fructus Immaturus. Flow-injection mass spectrometric (FIMS) fingerprints of 9 Aurantii Fructus Immaturus samples and 12 Poniciri Trifoliatae Fructus Immaturus samples were acquired and analyzed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The authentic herbs were differentiated from their counterfeits easily. Eight characteristic components which were responsible for the difference between the samples were tentatively identified. Furthermore, three out of the eight components, naringin, hesperidin, and neohesperidin, were quantified. The results are useful to help identify the authenticity of Aurantii Fructus Immaturus. PMID:25622204

Pre-selection and assessment of green organic solvents by clustering chemometric tools.

PubMed

Tobiszewski, Marek; Nedyalkova, Miroslava; Madurga, Sergio; Pena-Pereira, Francisco; Namieśnik, Jacek; Simeonov, Vasil

2018-01-01

The study presents the result of the application of chemometric tools for selection of physicochemical parameters of solvents for predicting missing variables - bioconcentration factors, water-octanol and octanol-air partitioning constants. EPI Suite software was successfully applied to predict missing values for solvents commonly considered as "green". Values for logBCF, logK OW and logK OA were modelled for 43 rather nonpolar solvents and 69 polar ones. Application of multivariate statistics was also proved to be useful in the assessment of the obtained modelling results. The presented approach can be one of the first steps and support tools in the assessment of chemicals in terms of their greenness. Copyright © 2017 Elsevier Inc. All rights reserved.

Optimisation of the formulation of a bubble bath by a chemometric approach market segmentation and optimisation.

PubMed

Marengo, Emilio; Robotti, Elisa; Gennaro, Maria Carla; Bertetto, Mariella

2003-03-01

The optimisation of the formulation of a commercial bubble bath was performed by chemometric analysis of Panel Tests results. A first Panel Test was performed to choose the best essence, among four proposed to the consumers; the best essence chosen was used in the revised commercial bubble bath. Afterwards, the effect of changing the amount of four components (the amount of primary surfactant, the essence, the hydratant and the colouring agent) of the bubble bath was studied by a fractional factorial design. The segmentation of the bubble bath market was performed by a second Panel Test, in which the consumers were requested to evaluate the samples coming from the experimental design. The results were then treated by Principal Component Analysis. The market had two segments: people preferring a product with a rich formulation and people preferring a poor product. The final target, i.e. the optimisation of the formulation for each segment, was obtained by the calculation of regression models relating the subjective evaluations given by the Panel and the compositions of the samples. The regression models allowed to identify the best formulations for the two segments ofthe market.

Kinetics of Forming Aldehydes in Frying Oils and Their Distribution in French Fries Revealed by LC-MS-Based Chemometrics.

PubMed

Wang, Lei; Csallany, A Saari; Kerr, Brian J; Shurson, Gerald C; Chen, Chi

2016-05-18

In this study, the kinetics of aldehyde formation in heated frying oils was characterized by 2-hydrazinoquinoline derivatization, liquid chromatography-mass spectrometry (LC-MS) analysis, principal component analysis (PCA), and hierarchical cluster analysis (HCA). The aldehydes contributing to time-dependent separation of heated soybean oil (HSO) in a PCA model were grouped by the HCA into three clusters (A1, A2, and B) on the basis of their kinetics and fatty acid precursors. The increases of 4-hydroxynonenal (4-HNE) and the A2-to-B ratio in HSO were well-correlated with the duration of thermal stress. Chemometric and quantitative analysis of three frying oils (soybean, corn, and canola oils) and French fry extracts further supported the associations between aldehyde profiles and fatty acid precursors and also revealed that the concentrations of pentanal, hexanal, acrolein, and the A2-to-B ratio in French fry extracts were more comparable to their values in the frying oils than other unsaturated aldehydes. All of these results suggest the roles of specific aldehydes or aldehyde clusters as novel markers of the lipid oxidation status for frying oils or fried foods.

Binding assessment of two arachidonic-based synthetic derivatives of adrenalin with β-lactoglobulin: Molecular modeling and chemometrics approach.

PubMed

Gholami, S; Bordbar, A K; Akvan, N; Parastar, H; Fani, N; Gretskaya, N M; Bezuglov, V V; Haertlé, T

2015-12-01

A computational approach to predict the main binding modes of two adrenalin derivatives, arachidonoyl adrenalin (AA-AD) and arachidonoyl noradrenalin (AA-NOR) with the β-lactoglubuline (BLG) as a nano-milk protein carrier is presented and assessed by comparison to the UV-Vis absorption spectroscopic data using chemometric analysis. Analysis of the spectral data matrices by using the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm led to the pure concentration calculation and spectral profiles resolution of the chemical constituents and the apparent equilibrium constants computation. The negative values of entropy and enthalpy changes for both compound indicated the essential role of hydrogen bonding and van der Waals interactions as main driving forces in stabilizing protein-ligand complex. Computational studies predicted that both derivatives are situated in the calyx pose and remained in that pose during the whole time of simulation with no any significant protein structural changes which pointed that the BLG could be considered as a suitable carrier for these catecholamine compounds. Copyright © 2015 Elsevier B.V. All rights reserved.

A chemometrics as a powerful tool in the elucidation of the role of metals in the biosynthesis of volatile organic compounds in Hungarian thyme samples.

PubMed

Arsenijević, Jelena; Marković, Jelena; Soštarić, Ivan; Ražić, Slavica

2013-10-01

The volatile fraction of the leaves of Thymus pannonicus All. (Lamiaceae) was analyzed by headspace extraction followed by GC-FID and GC-MS analysis. The different headspace profiles were recognized, with citral and with monoterpene hydrocarbons as dominant compounds. In addition, the determination of Cr, Co, Ni, Mo, Cu, Zn, Mn, Fe, Mg, Ca, K and Na was conducted by spectroscopic techniques (FAAS, GFAAS and ICP-OES). In order to evaluate the relationship between volatile organic compounds and metals, a chemometrics approach was applied. The data obtained by analysis of the headspace and elemental content were subjected to correlation analysis, factor analysis, principal component analysis and cluster analysis. A number of significant correlations of metals with plant volatiles were found. Correlation of Zn with citral, Mn with oxygenated monoterpenes and Mg with β-bourbonene, could be explained by involvement of metals in the biosynthesis of volatile organic compounds. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Source identification of petroleum hydrocarbons in soil and sediments from Iguaçu River Watershed, Paraná, Brazil using the CHEMSIC method (CHEMometric analysis of Selected Ion Chromatograms).

PubMed

Gallotta, Fabiana D C; Christensen, Jan H

2012-04-27

A chemometric method based on principal component analysis (PCA) of pre-processed and combined sections of selected ion chromatograms (SICs) is used to characterise the hydrocarbon profiles in soil and sediment from Araucária, Guajuvira, General Lúcio and Balsa Nova Municipalities (Iguaçu River Watershed, Paraná, Brazil) and to indicate the main sources of hydrocarbon pollution. The study includes 38 SICs of polycyclic aromatic compounds (PACs) and four of petroleum biomarkers in two separate analyses. The most contaminated samples are inside the Presidente Getúlio Vargas Refinery area. These samples represent a petrogenic pattern and different weathering degrees. Samples from outside the refinery area are either less or not contaminated, or contain mixtures of diagenetic, pyrogenic and petrogenic inputs where different proportions predominate. The locations farthest away from industrial activity (Balsa Nova) contains the lowest levels of PAC contamination. There are no evidences to conclude positive matches between the samples from outside the refinery area and the Cusiana spilled oil. Copyright © 2012 Elsevier B.V. All rights reserved.

Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis.

PubMed

Takamura, Ayari; Watanabe, Ken; Akutsu, Tomoko; Ozawa, Takeaki

2018-05-31

Body fluid (BF) identification is a critical part of a criminal investigation because of its ability to suggest how the crime was committed and to provide reliable origins of DNA. In contrast to current methods using serological and biochemical techniques, vibrational spectroscopic approaches provide alternative advantages for forensic BF identification, such as non-destructivity and versatility for various BF types and analytical interests. However, unexplored issues remain for its practical application to forensics; for example, a specific BF needs to be discriminated from all other suspicious materials as well as other BFs, and the method should be applicable even to aged BF samples. Herein, we describe an innovative modeling method for discriminating the ATR FT-IR spectra of various BFs, including peripheral blood, saliva, semen, urine and sweat, to meet the practical demands described above. Spectra from unexpected non-BF samples were efficiently excluded as outliers by adopting the Q-statistics technique. The robustness of the models against aged BFs was significantly improved by using the discrimination scheme of a dichotomous classification tree with hierarchical clustering. The present study advances the use of vibrational spectroscopy and a chemometric strategy for forensic BF identification.

Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics

NASA Astrophysics Data System (ADS)

Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya

2014-09-01

Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application

NASA Astrophysics Data System (ADS)

Kumar, Raj; Sharma, Vishal

2017-03-01

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application.

PubMed

Kumar, Raj; Sharma, Vishal

2017-03-15

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%). Copyright © 2016 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.

Chemometric strategy for modeling metabolic biological space along the gastrointestinal tract and assessing microbial influences.

PubMed

Martin, François-Pierre J; Montoliu, Ivan; Kochhar, Sunil; Rezzi, Serge

2010-12-01

Over the past decade, the analysis of metabolic data with advanced chemometric techniques has offered the potential to explore functional relationships among biological compartments in relation to the structure and function of the intestine. However, the employed methodologies, generally based on regression modeling techniques, have given emphasis to region-specific metabolic patterns, while providing only limited insights into the spatiotemporal metabolic features of the complex gastrointestinal system. Hence, novel approaches are needed to analyze metabolic data to reconstruct the metabolic biological space associated with the evolving structures and functions of an organ such as the gastrointestinal tract. Here, we report the application of multivariate curve resolution (MCR) methodology to model metabolic relationships along the gastrointestinal compartments in relation to its structure and function using data from our previous metabonomic analysis. The method simultaneously summarizes metabolite occurrence and contribution to continuous metabolic signatures of the different biological compartments of the gut tract. This methodology sheds new light onto the complex web of metabolic interactions with gut symbionts that modulate host cell metabolism in surrounding gut tissues. In the future, such an approach will be key to provide new insights into the dynamic onset of metabolic deregulations involved in region-specific gastrointestinal disorders, such as Crohn's disease or ulcerative colitis.

A Comprehensive Two-Dimensional Retention Time Alignment Algorithm To Enhance Chemometric Analysis of Comprehensive Two-Dimensional Separation Data

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

Pierce, Karisa M.; Wood, Lianna F.; Wright, Bob W.

2005-12-01

of trilinearity to the data, since trilinearity benefits chemometric analysis. By applying comprehensive 2D retention time alignment to all three data sets (control mixtures, gasoline samples, and diesel samples), classification by principal component analysis (PCA) substantially improved, resulting in 100% accurate scores clustering.« less

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Triglyceride dependent differentiation of obesity in adipose tissues by FTIR spectroscopy coupled with chemometrics.

PubMed

Kucuk Baloglu, Fatma; Baloglu, Onur; Heise, Sebastian; Brockmann, Gudrun; Severcan, Feride

2017-10-01

The excess deposition of triglycerides in adipose tissue is the main reason of obesity and causes excess release of fatty acids to the circulatory system resulting in obesity and insulin resistance. Body mass index and waist circumference are not precise measure of obesity and obesity related metabolic diseases. Therefore, in the current study, it was aimed to propose triglyceride bands located at 1770-1720 cm -1 spectral region as a more sensitive obesity related biomarker using the diagnostic potential of Fourier Transform Infrared (FTIR) spectroscopy in subcutaneous (SCAT) and visceral (VAT) adipose tissues. The adipose tissue samples were obtained from 10 weeks old male control (DBA/2J) (n = 6) and four different obese BFMI mice lines (n = 6 per group). FTIR spectroscopy coupled with hierarchical cluster analysis (HCA) and principal component analysis (PCA) was applied to the spectra of triglyceride bands as a diagnostic tool in the discrimination of the samples. Successful discrimination of the obese, obesity related insulin resistant and control groups were achieved with high sensitivity and specificity. The results revealed the power of FTIR spectroscopy coupled with chemometric approaches in internal diagnosis of abdominal obesity based on the spectral differences in the triglyceride region that can be used as a spectral marker. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fourier transform infrared spectroscopy combined with chemometrics for discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingiber cassumunar.

PubMed

Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi

2015-02-25

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm(-1)). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species. Copyright © 2014 Elsevier B.V. All rights reserved.

Fingerprint analysis and quality consistency evaluation of flavonoid compounds for fermented Guava leaf by combining high-performance liquid chromatography time-of-flight electrospray ionization mass spectrometry and chemometric methods.

PubMed

Wang, Lu; Tian, Xiaofei; Wei, Wenhao; Chen, Gong; Wu, Zhenqiang

2016-10-01

Guava leaves are used in traditional herbal teas as antidiabetic therapies. Flavonoids are the main active of Guava leaves and have many physiological functions. However, the flavonoid compositions and activities of Guava leaves could change due to microbial fermentation. A high-performance liquid chromatography time-of-flight electrospray ionization mass spectrometry method was applied to identify the varieties of the flavonoids in Guava leaves before and after fermentation. High-performance liquid chromatography, hierarchical cluster analysis and principal component analysis were used to quantitatively determine the changes in flavonoid compositions and evaluate the consistency and quality of Guava leaves. Monascus anka Saccharomyces cerevisiae fermented Guava leaves contained 2.32- and 4.06-fold more total flavonoids and quercetin, respectively, than natural Guava leaves. The flavonoid compounds of the natural Guava leaves had similarities ranging from 0.837 to 0.927. The flavonoid compounds from the Monascus anka S. cerevisiae fermented Guava leaves had similarities higher than 0.993. This indicated that the quality consistency of the fermented Guava leaves was better than that of the natural Guava leaves. High-performance liquid chromatography fingerprinting and chemometric analysis are promising methods for evaluating the degree of fermentation of Guava leaves based on quality consistency, which could be used in assessing flavonoid compounds for the production of fermented Guava leaves. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Influence of genotype and crop year in the chemometrics of almond and pistachio oils.

PubMed

Rabadán, Adrián; Álvarez-Ortí, Manuel; Gómez, Ricardo; de Miguel, Concepción; Pardo, José E

2018-04-01

Almond and pistachio oils can be considered as interesting products to produce and commercialize owing to their health-promoting properties. However, these properties are not consistent because of the differences that appear in oils as a result of the genotype and the crop year. The analysis of these variations and their origin is decisive in ensuring the commercial future prospects of these nut oils. Although significant variability has been reported in almond and pistachio oils as a result of the crop year and the interaction between crop year and genotype, the genotype itself remains the main factor determining oil chemometrics. Oil fatty acid profile has been mainly determined by the genotype, with the exception of palmitic fatty acid in pistachio oil. However, the crop year affects the concentration of some minor components of crucial nutritional interest as total polyphenols and phytosterols. Regarding reported differences in oil, some almond and pistachio genotypes should be prioritized for oil extraction. Breeding programmes focused on the improvement of specific characteristics of almond and pistachio oils should focus on chemical parameters mainly determined by the genotype. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Synthesis-identification integration: One-pot hydrothermal preparation of fluorescent nitrogen-doped carbon nanodots for differentiating nucleobases with the aid of multivariate chemometrics analysis.

PubMed

Zhuang, Qianfen; Cao, Wei; Ni, Yongnian; Wang, Yong

2018-08-01

Most of the conventional multidimensional differential sensors currently need at least two-step fabrication, namely synthesis of probe(s) and identification of multiple analytes by mixing of analytes with probe(s), and were conducted using multiple sensing elements or several devices. In the study, we chose five different nucleobases (adenine, cytosine, guanine, thymine, and uracil) as model analytes, and found that under hydrothermal conditions, sodium citrate could react directly with various nucleobases to yield different nitrogen-doped carbon nanodots (CDs). The CDs synthesized from different nucleobases exhibited different fluorescent properties, leading to their respective characteristic fluorescence spectra. Hence, we combined the fluorescence spectra of the CDs with advanced chemometrics like principle component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA), to present a conceptually novel "synthesis-identification integration" strategy to construct a multidimensional differential sensor for nucleobase discrimination. Single-wavelength excitation fluorescence spectral data, single-wavelength emission fluorescence spectral data, and fluorescence Excitation-Emission Matrices (EEMs) of the CDs were respectively used as input data of the differential sensor. The results showed that the discrimination ability of the multidimensional differential sensor with EEM data set as input data was superior to those with single-wavelength excitation/emission fluorescence data set, suggesting that increasing the number of the data input could improve the discrimination power. Two supervised pattern recognition methods, namely KNN and SIMCA, correctly identified the five nucleobases with a classification accuracy of 100%. The proposed "synthesis-identification integration" strategy together with a multidimensional array of experimental data holds great promise in the construction of

A solid-phase extraction procedure coupled to 1H NMR, with chemometric analysis, to seek reliable markers of the botanical origin of honey.

PubMed

Beretta, Giangiacomo; Caneva, Enrico; Regazzoni, Luca; Bakhtyari, Nazanin Golbamaki; Maffei Facino, Roberto

2008-07-14

The aim of this work was to establish an analytical method for identifying the botanical origin of honey, as an alternative to conventional melissopalynological, organoleptic and instrumental methods (gas-chromatography coupled to mass spectrometry (GC-MS), high-performance liquid chromatography HPLC). The procedure is based on the (1)H nuclear magnetic resonance (NMR) profile coupled, when necessary, with electrospray ionisation-mass spectrometry (ESI-MS) and two-dimensional NMR analyses of solid-phase extraction (SPE)-purified honey samples, followed by chemometric analyses. Extracts of 44 commercial Italian honeys from 20 different botanical sources were analyzed. Honeydew, chestnut and linden honeys showed constant, specific, well-resolved resonances, suitable for use as markers of origin. Honeydew honey contained the typical resonances of an aliphatic component, very likely deriving from the plant phloem sap or excreted into it by sap-sucking aphids. Chestnut honey contained the typical signals of kynurenic acid and some structurally related metabolite. In linden honey the (1)H NMR profile gave strong signals attributable to the mono-terpene derivative cyclohexa-1,3-diene-1-carboxylic acid (CDCA) and to its 1-O-beta-gentiobiosyl ester (CDCA-GBE). These markers were not detectable in the other honeys, except for the less common nectar honey from rosa mosqueta. We compared and analyzed the data by multivariate techniques. Principal component analysis found different clusters of honeys based on the presence of these specific markers. The results, although obviously only preliminary, suggest that the (1)H NMR profile (with HPLC-MS analysis when necessary) can be used as a reference framework for identifying the botanical origin of honey.

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products.

PubMed

Darwish, Hany W; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A

2016-02-05

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer. Copyright © 2015 Elsevier B.V. All rights reserved.

Chemometrics and chromatographic fingerprints to classify plant food supplements according to the content of regulated plants.

PubMed

Deconinck, E; Sokeng Djiogo, C A; Courselle, P

2017-09-05

Plant food supplements are gaining popularity, resulting in a broader spectrum of available products and an increased consumption. Next to the problem of adulteration of these products with synthetic drugs the presence of regulated or toxic plants is an important issue, especially when the products are purchased from irregular sources. This paper focusses on this problem by using specific chromatographic fingerprints for five targeted plants and chemometric classification techniques in order to extract the important information from the fingerprints and determine the presence of the targeted plants in plant food supplements in an objective way. Two approaches were followed: (1) a multiclass model, (2) 2-class model for each of the targeted plants separately. For both approaches good classification models were obtained, especially when using SIMCA and PLS-DA. For each model, misclassification rates for the external test set of maximum one sample could be obtained. The models were applied to five real samples resulting in the identification of the correct plants, confirmed by mass spectrometry. Therefore chromatographic fingerprinting combined with chemometric modelling can be considered interesting to make a more objective decision on whether a regulated plant is present in a plant food supplement or not, especially when no mass spectrometry equipment is available. The results suggest also that the use of a battery of 2-class models to screen for several plants is the approach to be preferred. Copyright © 2017 Elsevier B.V. All rights reserved.

Chemometric Deconvolution of Continuous Electrokinetic Injection Micellar Electrokinetic Chromatography Data for the Quantitation of Trinitrotoluene in Mixtures of Other Nitroaromatic Compounds

DTIC Science & Technology

2014-02-24

Suite 600 Washington, DC 20036 NRL/MR/ 6110 --14-9521 Approved for public release; distribution is unlimited. 1Science & Engineering Apprenticeship...Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/ 6110 --14-9521 Chemometric Deconvolution of Continuous Electrokinetic Injection Micellar... Engineering Apprenticeship Program American Society for Engineering Education Washington, DC Kevin Johnson Navy Technology Center for Safety and

Investigation of Drug–Polymer Compatibility Using Chemometric-Assisted UV-Spectrophotometry

PubMed Central

Mohamed, Amir Ibrahim; Abd-Motagaly, Amr Mohamed Elsayed; Ahmed, Osama A. A.; Amin, Suzan; Mohamed Ali, Alaa Ibrahim

2017-01-01

A simple chemometric-assisted UV-spectrophotometric method was used to study the compatibility of clindamycin hydrochloride (HC1) with two commonly used natural controlled-release polymers, alginate (Ag) and chitosan (Ch). Standard mixtures containing 1:1, 1:2, and 1:0.5 w/w drug–polymer ratios were prepared and UV scanned. A calibration model was developed with partial least square (PLS) regression analysis for each polymer separately. Then, test mixtures containing 1:1 w/w drug–polymer ratios with different sets of drug concentrations were prepared. These were UV scanned initially and after three and seven days of storage at 25 °C. Using the calibration model, the drug recovery percent was estimated and a decrease in concentration of 10% or more from initial concentration was considered to indicate instability. PLS models with PC3 (for Ag) and PC2 (for Ch) showed a good correlation between actual and found values with root mean square error of cross validation (RMSECV) of 0.00284 and 0.01228, and calibration coefficient (R2) values of 0.996 and 0.942, respectively. The average drug recovery percent after three and seven days was 98.1 ± 2.9 and 95.4 ± 4.0 (for Ag), and 97.3 ± 2.1 and 91.4 ± 3.8 (for Ch), which suggests more drug compatibility with an Ag than a Ch polymer. Conventional techniques including DSC, XRD, FTIR, and in vitro minimum inhibitory concentration (MIC) for (1:1) drug–polymer mixtures were also performed to confirm clindamycin compatibility with Ag and Ch polymers. PMID:28275214

Determination of cannabinoids in hemp nut products in Taiwan by HPLC-MS/MS coupled with chemometric analysis: quality evaluation and a pilot human study.

PubMed

Chang, Chih-Wei; Tung, Chun-Wei; Tsai, Chin-Chuan; Wu, Yu-Tse; Hsu, Mei-Chich

2017-06-01

Hemp nuts are mature cannabis seeds obtained after shelling and that are commonly used in traditional Chinese medicine for treating functional constipation. In this work, we screened hemp nut products, classified them, and verified the legality of consuming them. A total of 18 products were purchased from Taiwan, China, and Canada. Validated high-performance liquid chromatography with tandem mass spectrometry methods were developed for analyzing the cannabinoid (i.e., Δ 9 -tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabinol) content of the products and the concentration of urinary 11-nor-9-carboxy-THC. Chemometric techniques, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), were applied for rapidly classifying 11 concentrated powder products in Taiwan. A pilot human study comprising single and multiple administrations of a product with 1.5 µg/g of THC was conducted to examine the urinary 11-nor-9-carboxy-THC concentration. Through optimization of 3 2 full factorial design, using 60% isopropanol as the extraction solvent exhibited the highest yield of cannabinoids and was applied as the optimal condition in further analysis. The results of HCA and PCA on quality evaluation were in good agreement; however, the tested products possessed distinct CBD-to-THC ratios which ranged widely from 0.1:1 to 46.8:1. Particularly, the products with CBD-to-THC ratios higher than 1:1 were the majority in Taiwan. Our data suggested that all the tested hemp nut products met the Taiwan restriction criterion of 10 µg/g of THC. We propose a usual consumption amount of hemp nut products in Taiwan would unlikely to violate the cut-off point of 15 ng/mL of urinary 11-nor-9-carboxy-THC. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A chemometric method to identify enzymatic reactions leading to the transition from glycolytic oscillations to waves

NASA Astrophysics Data System (ADS)

Zimányi, László; Khoroshyy, Petro; Mair, Thomas

2010-06-01

In the present work we demonstrate that FTIR-spectroscopy is a powerful tool for the time resolved and noninvasive measurement of multi-substrate/product interactions in complex metabolic networks as exemplified by the oscillating glycolysis in a yeast extract. Based on a spectral library constructed from the pure glycolytic intermediates, chemometric analysis of the complex spectra allowed us the identification of many of these intermediates. Singular value decomposition and multiple level wavelet decomposition were used to separate drifting substances from oscillating ones. This enabled us to identify slow and fast variables of glycolytic oscillations. Most importantly, we can attribute a qualitative change in the positive feedback regulation of the autocatalytic reaction to the transition from homogeneous oscillations to travelling waves. During the oscillatory phase the enzyme phosphofructokinase is mainly activated by its own product ADP, whereas the transition to waves is accompanied with a shift of the positive feedback from ADP to AMP. This indicates that the overall energetic state of the yeast extract determines the transition between spatially homogeneous oscillations and travelling waves.

Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example.

PubMed

Ni, Yongnian; Lai, Yanhua; Brandes, Sarina; Kokot, Serge

2009-08-11

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.

Pre-analytical method for NMR-based grape metabolic fingerprinting and chemometrics.

PubMed

Ali, Kashif; Maltese, Federica; Fortes, Ana Margarida; Pais, Maria Salomé; Verpoorte, Robert; Choi, Young Hae

2011-10-10

Although metabolomics aims at profiling all the metabolites in organisms, data quality is quite dependent on the pre-analytical methods employed. In order to evaluate current methods, different pre-analytical methods were compared and used for the metabolic profiling of grapevine as a model plant. Five grape cultivars from Portugal in combination with chemometrics were analyzed in this study. A common extraction method with deuterated water and methanol was found effective in the case of amino acids, organic acids, and sugars. For secondary metabolites like phenolics, solid phase extraction with C-18 cartridges showed good results. Principal component analysis, in combination with NMR spectroscopy, was applied and showed clear distinction among the cultivars. Primary metabolites such as choline, sucrose, and leucine were found discriminating for 'Alvarinho', while elevated levels of alanine, valine, and acetate were found in 'Arinto' (white varieties). Among the red cultivars, higher signals for citrate and GABA in 'Touriga Nacional', succinate and fumarate in 'Aragonês', and malate, ascorbate, fructose and glucose in 'Trincadeira', were observed. Based on the phenolic profile, 'Arinto' was found with higher levels of phenolics as compared to 'Alvarinho'. 'Trincadeira' showed lowest phenolics content while higher levels of flavonoids and phenylpropanoids were found in 'Aragonês' and 'Touriga Nacional', respectively. It is shown that the metabolite composition of the extract is highly affected by the extraction procedure and this consideration has to be taken in account for metabolomics studies. Copyright © 2011 Elsevier B.V. All rights reserved.

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

NASA Astrophysics Data System (ADS)

Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun

2018-01-01

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

A Chemometrics Approach for Nuclear Magnetic Resonance Data to Characterize the Partial Metabolome Banana Peels from Southern Brazil.

PubMed

Cardoso, Sara; Maraschin, Marcelo; Peruch, Luiz Augusto Martins; Rocha, Miguel; Pereira, Aline

2017-12-13

Banana peels are well recognized as a source of important bioactive compounds, such as phenolics, carotenoids, biogenic amines, among others. As such, they have recently started to be used for industrial purposes. However, its composition seems to be strongly affected by biotic or abiotic ecological factors. Thus, this study aimed to investigate banana peels chemical composition, not only to get insights on eventual metabolic changes caused by the seasons, in southern Brazil, but also to identify the most relevant metabolites for these processes. To achieve this, a Nuclear magnetic resonance (NMR)-based metabolic profiling strategy was adopted, followed by chemometrics analysis, using the specmine package for the R environment, and metabolite identification. The results showed that the metabolomic approach adopted allowed identifying a series of primary and secondary metabolites in the aqueous extracts investigated. Besides, over the seasons the metabolic profiles of the banana peels showed to contain biologically active compounds relevant to the skin wound healing process, indicating the biotechnological potential of that raw material.

Differentiation of Body Fluid Stains on Fabrics Using External Reflection Fourier Transform Infrared Spectroscopy (FT-IR) and Chemometrics.

PubMed

Zapata, Félix; de la Ossa, Ma Ángeles Fernández; García-Ruiz, Carmen

2016-04-01

Body fluids are evidence of great forensic interest due to the DNA extracted from them, which allows genetic identification of people. This study focuses on the discrimination among semen, vaginal fluid, and urine stains (main fluids in sexual crimes) placed on different colored cotton fabrics by external reflection Fourier transform infrared spectroscopy (FT-IR) combined with chemometrics. Semen-vaginal fluid mixtures and potential false positive substances commonly found in daily life such as soaps, milk, juices, and lotions were also studied. Results demonstrated that the IR spectral signature obtained for each body fluid allowed its identification and the correct classification of unknown stains by means of principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). Interestingly, results proved that these IR spectra did not show any bands due to the color of the fabric and no substance of those present in daily life which were analyzed, provided a false positive. © The Author(s) 2016.

Chemometrics-assisted spectrophotometric green method for correcting interferences in biowaiver studies: Application to assay and dissolution profiling study of donepezil hydrochloride tablets

NASA Astrophysics Data System (ADS)

Korany, Mohamed A.; Mahgoub, Hoda; Haggag, Rim S.; Ragab, Marwa A. A.; Elmallah, Osama A.

2018-06-01

A green, simple and cost effective chemometric UV-Vis spectrophotometric method has been developed and validated for correcting interferences that arise during conducting biowaiver studies. Chemometric manipulation has been done for enhancing the results of direct absorbance, resulting from very low concentrations (high incidence of background noise interference) of earlier points in the dissolution timing in case of dissolution profile using first and second derivative (D1 & D2) methods and their corresponding Fourier function convoluted methods (D1/FF& D2/FF). The method applied for biowaiver study of Donepezil Hydrochloride (DH) as a representative model was done by comparing two different dosage forms containing 5 mg DH per tablet as an application of a developed chemometric method for correcting interferences as well as for the assay and dissolution testing in its tablet dosage form. The results showed that first derivative technique can be used for enhancement of the data in case of low concentration range of DH (1-8 μg mL-1) in the three different pH dissolution media which were used to estimate the low drug concentrations dissolved at the early points in the biowaiver study. Furthermore, the results showed similarity in phosphate buffer pH 6.8 and dissimilarity in the other 2 pH media. The method was validated according to ICH guidelines and USP monograph for both assays (HCl of pH 1.2) and dissolution study in 3 pH media (HCl of pH 1.2, acetate buffer of pH 4.5 and phosphate buffer of pH 6.8). Finally, the assessment of the method greenness was done using two different assessment techniques: National Environmental Method Index label and Eco scale methods. Both techniques ascertained the greenness of the proposed method.

Chemometrics-assisted spectrophotometric green method for correcting interferences in biowaiver studies: Application to assay and dissolution profiling study of donepezil hydrochloride tablets.

PubMed

Korany, Mohamed A; Mahgoub, Hoda; Haggag, Rim S; Ragab, Marwa A A; Elmallah, Osama A

2018-06-15

A green, simple and cost effective chemometric UV-Vis spectrophotometric method has been developed and validated for correcting interferences that arise during conducting biowaiver studies. Chemometric manipulation has been done for enhancing the results of direct absorbance, resulting from very low concentrations (high incidence of background noise interference) of earlier points in the dissolution timing in case of dissolution profile using first and second derivative (D1 & D2) methods and their corresponding Fourier function convoluted methods (D1/FF& D2/FF). The method applied for biowaiver study of Donepezil Hydrochloride (DH) as a representative model was done by comparing two different dosage forms containing 5mg DH per tablet as an application of a developed chemometric method for correcting interferences as well as for the assay and dissolution testing in its tablet dosage form. The results showed that first derivative technique can be used for enhancement of the data in case of low concentration range of DH (1-8μgmL -1 ) in the three different pH dissolution media which were used to estimate the low drug concentrations dissolved at the early points in the biowaiver study. Furthermore, the results showed similarity in phosphate buffer pH6.8 and dissimilarity in the other 2pH media. The method was validated according to ICH guidelines and USP monograph for both assays (HCl of pH1.2) and dissolution study in 3pH media (HCl of pH1.2, acetate buffer of pH4.5 and phosphate buffer of pH6.8). Finally, the assessment of the method greenness was done using two different assessment techniques: National Environmental Method Index label and Eco scale methods. Both techniques ascertained the greenness of the proposed method. Copyright © 2018 Elsevier B.V. All rights reserved.

HPLC-PDA Combined with Chemometrics for Quantitation of Active Components and Quality Assessment of Raw and Processed Fruits of Xanthium strumarium L.

PubMed

Jiang, Hai; Yang, Liu; Xing, Xudong; Yan, Meiling; Guo, Xinyue; Yang, Bingyou; Wang, Qiuhong; Kuang, Haixue

2018-01-25

As a valuable herbal medicine, the fruits of Xanthium strumarium L. (Xanthii Fructus) have been widely used in raw and processed forms to achieve different therapeutic effects in practice. In this study, a comprehensive strategy was proposed for evaluating the active components in 30 batches of raw and processed Xanthii Fructus (RXF and PXF) samples, based on high-performance liquid chromatography coupled with photodiode array detection (HPLC-PDA). Twelve common peaks were detected and eight compounds of caffeoylquinic acids were simultaneously quantified in RXF and PXF. All the analytes were detected with satisfactory linearity (R² > 0.9991) over wide concentration ranges. Simultaneously, the chemically latent information was revealed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results suggest that there were significant differences between RXF and PXF from different regions in terms of the content of eight caffeoylquinic acids. Potential chemical markers for XF were found during processing by chemometrics.

Development and validation of simple spectrophotometric and chemometric methods for simultaneous determination of empagliflozin and metformin: Applied to recently approved pharmaceutical formulation

NASA Astrophysics Data System (ADS)

Ayoub, Bassam M.

2016-11-01

New univariate spectrophotometric method and multivariate chemometric approach were developed and compared for simultaneous determination of empagliflozin and metformin manipulating their zero order absorption spectra with application on their pharmaceutical preparation. Sample enrichment technique was used to increase concentration of empagliflozin after extraction from tablets to allow its simultaneous determination with metformin without prior separation. Validation parameters according to ICH guidelines were satisfactory over the concentration range of 2-12 μg mL- 1 for both drugs using simultaneous equation with LOD values equal to 0.20 μg mL- 1 and 0.19 μg mL- 1, LOQ values equal to 0.59 μg mL- 1 and 0.58 μg mL- 1 for empagliflozin and metformin, respectively. While the optimum results for the chemometric approach using partial least squares method (PLS-2) were obtained using concentration range of 2-10 μg mL- 1. The optimized validated methods are suitable for quality control laboratories enable fast and economic determination of the recently approved pharmaceutical combination Synjardy® tablets.

High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics

PubMed Central

Johnson, Helen E.; Broadhurst, David; Kell, Douglas B.; Theodorou, Michael K.; Merry, Roger J.; Griffith, Gareth W.

2004-01-01

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm−1) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins. PMID:15006782

Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Gómez-Romero, María; Ajal, El Amine; Bagur-González, María Gracia; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2017-01-15

High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using "Low-level" and "Mid-Level" data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination "chromatographic dataset+chemometric technique" allowing the most accurate classification for each monovarietal extra-VOO was highlighted. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of juices and fermented beverages made from unripe, ripe and senescent apples based on the aromatic profile using chemometrics.

PubMed

Braga, Cíntia Maia; Zielinski, Acácio Antonio Ferreira; Silva, Karolline Marques da; de Souza, Frederico Koch Fernandes; Pietrowski, Giovana de Arruda Moura; Couto, Marcelo; Granato, Daniel; Wosiacki, Gilvan; Nogueira, Alessandro

2013-11-15

The aim of this study was to assess differences between apple juices and fermented apple beverages elaborated with fruits from different varieties and at different ripening stages in the aroma profile by using chemometrics. Ripening influenced the aroma composition of the apple juice and fermented apple. For all varieties, senescent fruits provided more aromatic fermented apple beverages. However, no significant difference was noticed in samples made of senescent or ripe fruits of the Lisgala variety. Regarding the juices, ripe Gala apple had the highest total aroma concentration. Ethanal was the major compound identified in all the samples, with values between 11.83mg/L (unripe Lisgala juice) and 81.05mg/L (ripe Gala juice). 3-Methyl-1-butanol was the major compound identified in the fermented juices. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied and classified the juices and fermented juices based on physicochemical and aroma profile, demonstrating their applicability as tools to monitor the quality of apple-based products. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.

PubMed

Gomes, Clêrton L; de Lima, Ari Clecius A; Loiola, Adonay R; da Silva, Abel B R; Cândido, Manuela C L; Nascimento, Ronaldo F

2016-07-01

The increased marketing of fake perfumes has encouraged us to investigate how to identify such products by their chemical characteristics and multivariate analysis. The aim of this study was to present an alternative approach to distinguish original from fake perfumes by means of the investigation of sodium, potassium, chloride ions, and ethanol contents by chemometric tools. For this, 50 perfumes were used (25 original and 25 counterfeit) for the analysis of ions (ion chromatography) and ethanol (gas chromatography). The results demonstrated that the fake perfume had low levels of ethanol and high levels of chloride compared to the original product. The data were treated by chemometric tools such as principal component analysis and linear discriminant analysis. This study proved that the analysis of ethanol is an effective method of distinguishing original from the fake products, and it may potentially be used to assist legal authorities in such cases. © 2016 American Academy of Forensic Sciences.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

PubMed

Xu, Lu; Shi, Peng-Tao; Ye, Zi-Hong; Yan, Si-Min; Yu, Xiao-Ping

2013-12-01

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market. Copyright © 2013 Elsevier Ltd. All rights reserved.

Characterization of semi-solid Self-Emulsifying Drug Delivery Systems (SEDDS) of atorvastatin calcium by Raman image spectroscopy and chemometrics.

PubMed

Breitkreitz, Márcia C; Sabin, Guilherme P; Polla, Griselda; Poppi, Ronei J

2013-01-25

A methodology based on Raman image spectroscopy and chemometrics for homogeneity evaluation of formulations containing atorvastatin calcium in Gelucire(®) 44/14 is presented. In the first part of the work, formulations with high amounts of Gelucire(®) 44/14 (80%) and solvents of different polarities (diethylene glycol monoethyl ether, propyleneglycol, propylene glycol monocaprylate and glyceryl mono/dicaprylate/caprate) were prepared for miscibility screening evaluation by classical least squares (CLS). It was observed that Gelucire(®) 44/14 presented higher affinity for the lipophilic solvents glyceryl mono/dicaprylate/caprate and propylene glycol monocaprylate, whose samples were observed to be homogeneous, and lower affinity for the hydrophilic solvents diethylene glycol monoethyl ether and propyleneglycol, whose samples were heterogeneous. In the second part of the work, the ratio of glyceryl mono/dicaprylate/caprate and Gelucire(®) 44/14 was determined based on studies in water and allowed the selection of the proportions of these two excipients in the preconcentrate that provided supersaturation of atorvastatin upon dilution. The preconcentrate was then evaluated for homogeneity by partial least squares (PLS) and an excellent miscibility was observed in this proportion as well. Therefore, it was possible to select a formulation that presented simultaneously homogeneous preconcentrate and solubility enhancement in water by Raman image spectroscopy and chemometrics. Copyright © 2012 Elsevier B.V. All rights reserved.

Traceability of Opuntia ficus-indica L. Miller by ICP-MS multi-element profile and chemometric approach.

PubMed

Mottese, Antonio Francesco; Naccari, Clara; Vadalà, Rossella; Bua, Giuseppe Daniel; Bartolomeo, Giovanni; Rando, Rossana; Cicero, Nicola; Dugo, Giacomo

2018-01-01

Opuntia ficus-indica L. Miller fruits, particularly 'Ficodindia dell'Etna' of Biancavilla (POD), 'Fico d'india tradizionale di Roccapalumba' with protected brand and samples from an experimental field in Pezzolo (Sicily) were analyzed by inductively coupled plasma mass spectrometry in order to determine the multi-element profile. A multivariate chemometric approach, specifically principal component analysis (PCA), was applied to individuate how mineral elements may represent a marker of geographic origin, which would be useful for traceability. PCA has allowed us to verify that the geographical origin of prickly pear fruits is significantly influenced by trace element content, and the results found in Biancavilla PDO samples were linked to the geological composition of this volcanic areas. It was observed that two principal components accounted for 72.03% of the total variance in the data and, in more detail, PC1 explains 45.51% and PC2 26.52%, respectively. This study demonstrated that PCA is an integrated tool for the traceability of food products and, at the same time, a useful method of authentication of typical local fruits such as prickly pear. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Evaluation of thermal and non-thermal processing effect on non-prebiotic and prebiotic acerola juices using 1H qNMR and GC-MS coupled to chemometrics.

PubMed

Alves Filho, Elenilson G; Silva, Lorena Mara A; de Brito, Edy S; Wurlitzer, Nedio Jair; Fernandes, Fabiano A N; Rabelo, Maria Cristiane; Fonteles, Thatyane V; Rodrigues, Sueli

2018-11-01

The effects of thermal (pasteurization and sterilization) and non-thermal (ultrasound and plasma) processing on the composition of prebiotic and non-prebiotic acerola juices were evaluated using NMR and GC-MS coupled to chemometrics. The increase in the amount of Vitamin C was the main feature observed after thermal processing, followed by malic acid, choline, trigonelline, and acetaldehyde. On the other hand, thermal processing increased the amount of 2-furoic acid, a degradation product from ascorbic acid, as well as influenced the decrease in the amount of esters and alcohols. In general, the non-thermal processing did not present relevant effect on juices composition. The addition of prebiotics (inulin and gluco-oligosaccharides) decreased the effect of processing on juices composition, which suggested a protective effect by microencapsulation. Therefore, chemometric evaluation of the 1 H qNMR and GC-MS dataset was suitable to follow changes in acerola juice under different processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

Assessment of repeatability of composition of perfumed waters by high-performance liquid chromatography combined with numerical data analysis based on cluster analysis (HPLC UV/VIS - CA).

PubMed

Ruzik, L; Obarski, N; Papierz, A; Mojski, M

2015-06-01

High-performance liquid chromatography (HPLC) with UV/VIS spectrophotometric detection combined with the chemometric method of cluster analysis (CA) was used for the assessment of repeatability of composition of nine types of perfumed waters. In addition, the chromatographic method of separating components of the perfume waters under analysis was subjected to an optimization procedure. The chromatograms thus obtained were used as sources of data for the chemometric method of cluster analysis (CA). The result was a classification of a set comprising 39 perfumed water samples with a similar composition at a specified level of probability (level of agglomeration). A comparison of the classification with the manufacturer's declarations reveals a good degree of consistency and demonstrates similarity between samples in different classes. A combination of the chromatographic method with cluster analysis (HPLC UV/VIS - CA) makes it possible to quickly assess the repeatability of composition of perfumed waters at selected levels of probability. © 2014 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Application of multivariate chemometric techniques for simultaneous determination of five parameters of cottonseed oil by single bounce attenuated total reflectance Fourier transform infrared spectroscopy.

PubMed

Talpur, M Younis; Kara, Huseyin; Sherazi, S T H; Ayyildiz, H Filiz; Topkafa, Mustafa; Arslan, Fatma Nur; Naz, Saba; Durmaz, Fatih; Sirajuddin

2014-11-01

Single bounce attenuated total reflectance (SB-ATR) Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics was used for accurate determination of free fatty acid (FFA), peroxide value (PV), iodine value (IV), conjugated diene (CD) and conjugated triene (CT) of cottonseed oil (CSO) during potato chips frying. Partial least square (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR) and simple Beer׳s law (SBL) were applied to develop the calibrations for simultaneous evaluation of five stated parameters of cottonseed oil (CSO) during frying of French frozen potato chips at 170°C. Good regression coefficients (R(2)) were achieved for FFA, PV, IV, CD and CT with value of >0.992 by PLS, SMLR, PCR, and SBL. Root mean square error of prediction (RMSEP) was found to be less than 1.95% for all determinations. Result of the study indicated that SB-ATR FTIR in combination with multivariate chemometrics could be used for accurate and simultaneous determination of different parameters during the frying process without using any toxic organic solvent. Copyright © 2014 Elsevier B.V. All rights reserved.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

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.

Rapid differentiation of Listeria monocytogenes epidemic clones III and IV and their intact compared with heat-killed populations using Fourier transform infrared spectroscopy and chemometrics.

PubMed

Nyarko, Esmond B; Puzey, Kenneth A; Donnelly, Catherine W

2014-06-01

The objectives of this study were to determine if Fourier transform infrared (FT-IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat-killed populations. FT-IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1-101 and R2-499; ECIV: J1-129 and J1-220), and also from intact and heat-killed cell populations of each EC strain using 250 scans at a resolution of 4 cm(-1) in the mid-infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm(-1) separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat-killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT-IR spectral wavenumbers 1650 to 1390 cm(-1) were used to separate heat-killed and intact populations of L. monocytogenes. The FT-IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT-IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism. Fourier transform infrared (FT-IR) spectroscopy and multivariate statistical analysis can be used for L. monocytogenes source tracking and for clinical case isolate comparison during epidemiological investigations since the method is capable of differentiating epidemic clones and it uses a library of well-characterized strains. The FT-IR method is potentially less expensive and more rapid compared to genetic

Early detection of Zygosaccharomyces rouxii--spawned spoilage in apple juice by electronic nose combined with chemometrics.

PubMed

Wang, Huxuan; Hu, Zhongqiu; Long, Fangyu; Guo, Chunfeng; Yuan, Yahong; Yue, Tianli

2016-01-18

Spoilage spawned by Zygosaccharomyces rouxii can cause sensory defect in apple juice, which could hardly be perceived in the early stage and therefore would lead to the serious economic loss. Thus, it is essential to detect the contamination in early stage to avoid costly waste of products or recalls. In this work the performance of an electronic nose (e-nose) coupled with chemometric analysis was evaluated for diagnosis of the contamination in apple juice, using test panel evaluation as reference. The feasibility of using e-nose responses to predict the spoilage level of apple juice was also evaluated. Coupled with linear discriminant analysis (LDA), detection of the contamination was achieved after 12h, corresponding to the cell concentration of less than 2.0 log 10 CFU/mL, the level at which the test panelists could not yet identify the contamination, indicating that the signals of e-nose could be utilized as early indicators for the onset of contamination. Loading analysis indicated that sensors 2, 6, 7 and 8 were the most important in the detection of Z. rouxii-contaminated apple juice. Moreover, Z. rouxii counts in unknown samples could be well predicted by the established models using partial least squares (PLS) algorithm with high correlation coefficient (R) of 0.98 (Z. rouxii strain ATCC 2623 and ATCC 8383) and 0.97 (Z. rouxii strain B-WHX-12-53). Based on these results, e-nose appears to be promising for rapid analysis of the odor in apple juice during processing or on the shelf to realize the early detection of potential contamination caused by Z. rouxii strains. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

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

2010-06-01

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

Detection of goat body fat adulteration in pure ghee using ATR-FTIR spectroscopy coupled with chemometric strategy.

PubMed

Upadhyay, Neelam; Jaiswal, Pranita; Jha, Shyam Narayan

2016-10-01

Ghee forms an important component of the diet of human beings due to its rich flavor and high nutritive value. This high priced fat is prone to adulteration with cheaper fats. ATR-FTIR spectroscopy coupled with chemometrics was applied for determining the presence of goat body fat in ghee (@1, 3, 5, 10, 15 and 20% level in the laboratory made/spiked samples). The spectra of pure (ghee and goat body fat) and spiked samples were taken in the wavenumber range of 4000-500 cm -1 . Separated clusters of pure ghee and spiked samples were obtained on applying principal component analysis at 5% level of significance in the selected wavenumber range (1786-1680, 1490-919 and 1260-1040 cm -1 ). SIMCA was applied for classification of samples and pure ghee showed 100% classification efficiency. The value of R 2 was found to be >0.99 for calibration and validation sets using partial least square method at all the selected wavenumber range which indicate that the model was well developed. The study revealed that the spiked samples of goat body fat could be detected even at 1% level in ghee.

A spectroscopic study of mechanochemically activated kaolinite with the aid of chemometrics.

PubMed

Carmody, Onuma; Kristóf, János; Frost, Ray L; Makó, Eva; Kloprogge, J Theo; Kokot, Serge

2005-07-01

The study of kaolinite surfaces is of industrial importance. In this work we report the application of chemometrics to the study of modified kaolinite surfaces. DRIFT spectra of mechanochemically activated kaolinites (Kiralyhegy, Zettlitz, Szeg, and Birdwood) were analyzed using principal component analysis (PCA) and multicriteria decision making (MCDM) methods, PROMETHEE and GAIA. The clear discrimination of the Kiralyhegy spectral objects on the two PC scores plots (400-800 and 800-2030 cm(-1)) indicated the dominance of quartz. Importantly, no ordering of any spectral objects appeared to be related to grinding time in the PC plots of these spectral regions. Thus, neither the kaolinite nor the quartz, are systematically responsive to grinding time according to the spectral criteria investigated. The third spectral region (2600-3800 cm(-1)OH vibrations), showed apparent systematic ordering of the Kiralyhegy and, to a lesser extent, Zettlitz spectral objects with grinding time. This was attributed to the effect of the natural quartz on the delamination of kaolinite and the accompanying phenomena (i.e., formation of kaolinite spheres and water). With the MCDM methods, it was shown that useful information on the basis of chemical composition, physical properties and grinding time can be obtained. For example, the effects of the minor chemical components (e.g., MgO, K(2)O, etc.) indicated that the Birdwood kaolinite is arguably the most pure one analyzed. In another MCDM experiment, some support was obtained for the apparent trend with grinding time noted in the PC plot of the OH spectral region.

Tracking the degradation of fresh orange juice and discrimination of orange varieties: an example of NMR in coordination with chemometrics analyses.

PubMed

de Oliveira, Clayton R; Carneiro, Renato L; Ferreira, Antonio G

2014-12-01

Brazil is currently the largest exporter of concentrated orange juice and, unlike the other exporter countries, the domestic consumption is mainly based on the fresh orange juice. The quality control by evaluating the major chemical constituents under the influence of the most important factors, such as temperature and storage time of the product, is very important in this context. Therefore, the objective of this study was to evaluate the influence of temperature and time on the degradation of fresh orange juice for 24h, by using (1)H NMR technique and chemometric tools for data mining. The storage conditions at 24h led to the production of the formic, fumaric and acetic acids; and an increase of succinic and lactic acids and ethanol, which were observed at low concentration at the initial time. Furthermore, analysis by PCA has successfully distinguished the juice of different species/varieties as well as the metabolites responsible for their separation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fingerprints for main varieties of argentinean wines: terroir differentiation by inorganic, organic, and stable isotopic analyses coupled to chemometrics.

PubMed

Di Paola-Naranjo, Romina D; Baroni, Maria V; Podio, Natalia S; Rubinstein, Hector R; Fabani, Maria P; Badini, Raul G; Inga, Marcela; Ostera, Hector A; Cagnoni, Mariana; Gallegos, Ernesto; Gautier, Eduardo; Peral-Garcia, Pilar; Hoogewerff, Jurian; Wunderlin, Daniel A

2011-07-27

Our main goal was to investigate if robust chemical fingerprints could be developed for three Argentinean red wines based on organic, inorganic, and isotopic patterns, in relation to the regional soil composition. Soils and wines from three regions (Mendoza, San Juan, and Córdoba) and three varieties (Cabernet Sauvignon, Malbec, and Syrah) were collected. The phenolic profile was determined by HPLC-MS/MS and multielemental composition by ICP-MS; (87)Sr/(86)Sr and δ(13)C were determined by TIMS and IRMS, respectively. Chemometrics allowed robust differentiation between regions, wine varieties, and the same variety from different regions. Among phenolic compounds, resveratrol concentration was the most useful marker for wine differentiation, whereas Mg, K/Rb, Ca/Sr, and (87)Sr/(86)Sr were the main inorganic and isotopic parameters selected. Generalized Procrustes analysis (GPA) using two studied matrices (wine and soil) shows consensus between them and clear differences between studied areas. Finally, we applied a canonical correlation analysis, demonstrating significant correlation (r = 0.99; p < 0.001) between soil and wine composition. To our knowledge this is the first report combining independent variables, constructing a fingerprint including elemental composition, isotopic, and polyphenol patterns to differentiate wines, matching part of this fingerprint with the soil provenance.

Effect of the addition of chia's by-product on the composition of fatty acids in hamburgers through chemometric methods.

PubMed

Souza, Aloisio H P; Gohara, Aline K; Rotta, Eliza M; Chaves, Marcia A; Silva, Claudia M; Dias, Lucia F; Gomes, Sandra T M; Souza, Nilson E; Matsushita, Makoto

2015-03-30

Hamburger is a meat-based food that is easy to prepare and is widely consumed. It can be enriched using different ingredients, such as chia's by-product, which is rich in omega-3. Chemometrics is a very interesting tool to assess the influence of ingredients in the composition of foods. A complete factorial design 2(2) (two factors in two levels) with duplicate was performed to investigate the influence of the factors (1) concentration of textured soy proteins (TSP) and (2) concentration of chia flour partially defatted (CFPD) as a partial replacement for the bovine meat and porcine fat mix in hamburgers. The results of proximal composition, lipid oxidation, fatty acids sums, ratios, and nutritional indexes were used to propose statistical models. The factors TSP and CFPD were significant, and the increased values contributed to improve the composition in fatty acids, crude protein, and ash. Principal components analysis distinguished the samples with a higher content of chia. In desirability analysis, the highest level of TSP and CFPD was described as the optimal region, and it was not necessary to make another experimental point. The addition of chia's by-product is an alternative to increase the α-linolenic contents and to obtain nutritionally balanced food. © 2014 Society of Chemical Industry.

In-line and Real-time Monitoring of Resonant Acoustic Mixing by Near-infrared Spectroscopy Combined with Chemometric Technology for Process Analytical Technology Applications in Pharmaceutical Powder Blending Systems.

PubMed

Tanaka, Ryoma; Takahashi, Naoyuki; Nakamura, Yasuaki; Hattori, Yusuke; Ashizawa, Kazuhide; Otsuka, Makoto

2017-01-01

Resonant acoustic ® mixing (RAM) technology is a system that performs high-speed mixing by vibration through the control of acceleration and frequency. In recent years, real-time process monitoring and prediction has become of increasing interest, and process analytical technology (PAT) systems will be increasingly introduced into actual manufacturing processes. This study examined the application of PAT with the combination of RAM, near-infrared spectroscopy, and chemometric technology as a set of PAT tools for introduction into actual pharmaceutical powder blending processes. Content uniformity was based on a robust partial least squares regression (PLSR) model constructed to manage the RAM configuration parameters and the changing concentration of the components. As a result, real-time monitoring may be possible and could be successfully demonstrated for in-line real-time prediction of active pharmaceutical ingredients and other additives using chemometric technology. This system is expected to be applicable to the RAM method for the risk management of quality.

Main differences between volatiles of sparkling and base wines accessed through comprehensive two dimensional gas chromatography with time-of-flight mass spectrometric detection and chemometric tools.

PubMed

Welke, Juliane Elisa; Zanus, Mauro; Lazzarotto, Marcelo; Pulgati, Fernando Hepp; Zini, Cláudia Alcaraz

2014-12-01

The main changes in the volatile profile of base wines and their corresponding sparkling wines produced by traditional method were evaluated and investigated for the first time using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection (GC×GC/TOFMS) and chemometric tools. Fisher ratios helped to find the 119 analytes that were responsible for the main differences between base and sparkling wines and principal component analysis explained 93.1% of the total variance related to the selected 78 compounds. It was also possible to observe five subclusters in base wines and four subclusters in sparkling wines samples through hierarchical cluster analysis, which seemed to have an organised distribution according to the regions where the wines came from. Twenty of the most important volatile compounds co-eluted with other components and separation of some of them was possible due to GC×GC/TOFMS performance. Copyright © 2014. Published by Elsevier Ltd.

The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

PubMed

Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

2015-02-15

Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.

Conditions and chemometrics for the determination of heavy metals in natural and waste waters by stripping voltammetry with UV irradiation

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

Volkova, V.N.; Zakharova, E.A.; Khustenko, L.A.

The number of supporting electrolytes for stripping voltammetry with photochemical oxygen deactivation was broadened. The following agents are recommended: formic, lactic, tartaric, citric, and malonic acids at pH 2-4; salts of lactic, tartaric, and citric acids at pH 6-7; and salts of lactic, tartaric, citric, and glutaric acids at pH 12-14. A rapid method was developed for simultaneously determining Zn, Cd, Pb, and Cu in a 0.5 M formic acid supporting electrolyte. The method is chemometrically sound and cost-effective.

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).

Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods.

PubMed

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

2018-04-01

Boletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. Compared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. The results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

PubMed

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Quality assessment of the saffron samples using second-order spectrophotometric data assisted by three-way chemometric methods via quantitative analysis of synthetic colorants in adulterated saffron

NASA Astrophysics Data System (ADS)

Masoum, Saeed; Gholami, Ali; Hemmesi, Marjan; Abbasi, Saleheh

2015-09-01

Saffron is a valuable culinary spice that can be used not only for dyes and cooking, but also for many medical purposes. Due to its high price and restriction of its production, various fraud manners in its production have been growing. Addition of synthetic colorants to saffron is the most common way for adulteration. In this work, chemometric methods are proposed to resolve the three-dimensional absorbance spectra-pH data for simultaneous determination of the two colorants Tartrazin and Sunset yellow, in adulterated saffron. The rank deficiency in the concentration mode impaired the system. Therefore, to extirpate the ambiguity, which results from rank deficiency, three-way variation array V was generated by subtracting the first pH spectrum from each spectrum at each pH. This allows the extraction of extent reaction profile and mixture reaction spectral profiles, as well as the relative concentrations of the analytes.

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.

[Quality evaluation of Artemisiae Argyi Folium based on fingerprint analysis and quantitative analysis of multicomponents].

PubMed

Guo, Long; Jiao, Qian; Zhang, Dan; Liu, Ai-Peng; Wang, Qian; Zheng, Yu-Guang

2018-03-01

Artemisiae Argyi Folium, the dried leaves of Artemisia argyi, has been widely used in traditional Chinese and folk medicines for treatment of hemorrhage, pain, and skin itch. Phytochemical studies indicated that volatile oil, organic acid and flavonoids were the main bioactive components in Artemisiae Argyi Folium. Compared to the volatile compounds, the research of nonvolatile compounds in Artemisiae Argyi Folium are limited. In the present study, an accurate and reliable fingerprint approach was developed using HPLC for quality control of Artemisiae Argyi Folium. A total of 10 common peaks were marked，and the similarity of all the Artemisiae Argyi Folium samples was above 0.940. The established fingerprint method could be used for quality control of Artemisiae Argyi Folium. Furthermore, an HPLC method was applied for simultaneous determination of seven bioactive compounds including five organic acids and two flavonoids in Artemisiae Argyi Folium and Artemisiae Lavandulaefoliae Folium samples. Moreover, chemometrics methods such as hierarchical clustering analysis and principal component analysis were performed to compare and discriminate the Artemisiae Argyi Folium and Artemisiae Lavandulaefoliae Folium based on the quantitative data of analytes. The results indicated that simultaneous quantification of multicomponents coupled with chemometrics analysis could be a well-acceptable strategy to identify and evaluate the quality of Artemisiae Argyi Folium. Copyright© by the Chinese Pharmaceutical Association.

A chemometric approach for optimizing protein covalent immobilization on magnetic core-shell nanoparticles in view of an alternative immunoassay.

PubMed

Teste, Bruno; Vial, Jérôme; Descroix, Stéphanie; Georgelin, Thomas; Siaugue, Jean-Michel; Petr, Jan; Varenne, Anne; Hennion, Marie-Claire

2010-06-15

A chemometric approach was developed to optimize the grafting of a bovine milk allergen: alpha-Lactalbumin (alpha-Lac) on colloidal functionalized magnetic core-shell nanoparticles (MCSNP). Such nanoparticles, functionalized with polyethyleneglycol and amino groups, exhibit a 30nm physical diameter and behave as a quasi-homogeneous system. The alpha-Lac immobilization was achieved through the covalent binding between MCSNP amino groups and alpha-Lac carboxylic moieties using the well-known tandem carbodiimide (EDC) and hydroxysulfosuccinimide (NHS). In this study, a chemometric approach was employed to highlight the parameters influencing the number of grafted proteins on the MCSNP. Three factors were evaluated: the ratio in concentration between EDC and alpha-Lac, between NHS and EDC and the concentration of alpha-Lac. After a first full factorial design to delimit the region of the space where the optimum could be located, a central composite design was then carried out to predict the best grafting conditions. It was established and experimentally confirmed that the optimum parameters are [EDC]/[alpha-Lac]=25; [NHS]/[EDC]=1.55 and alpha-Lac=24.85nmolmL(-1). In these optimal conditions, MCSNP surface was successfully saturated with alpha-Lac (34 alpha-Lac/MCSNP) with a high reproducibility (RSD=2%). The colloidal stability of MCSNP grafted with alpha-Lac as well as the immunological interactions using anti alpha-Lac antibody were then investigated in different buffers. The results emphasized that a 50mM MES buffer (pH 6) allows an efficient immune capture and a satisfying colloidal stability which provide an immunological interaction in homogeneous liquid phase.

Near infrared spectroscopy combined with chemometrics for growth stage classification of cannabis cultivated in a greenhouse from seized seeds.

PubMed

Borille, Bruna Tassi; Marcelo, Marcelo Caetano Alexandre; Ortiz, Rafael Scorsatto; Mariotti, Kristiane de Cássia; Ferrão, Marco Flôres; Limberger, Renata Pereira

2017-02-15

Cannabis sativa L. (cannabis, Cannabaceae), popularly called marijuana, is one of the oldest plants known to man and it is the illicit drug most used worldwide. It also has been the subject of increasing discussions from the scientific and political points of view due to its medicinal properties. In recent years in Brazil, the form of cannabis drug trafficking has been changing and the Brazilian Federal Police has exponentially increased the number of seizures of cannabis seeds sent by the mail. This new form of trafficking encouraged the study of cannabis seeds seized germinated in a greenhouse through NIR spectroscopy combined with chemometrics. The plants were cultivated in a homemade greenhouse under controlled conditions. In three different growth periods (5.5weeks, 7.5weeks and 10weeks), they were harvested, dried, ground and directly analyzed. The iPCA was used to select the best NIR spectral range (4000-4375cm -1 ) in order to develop unsupervised and supervised methods. The PCA and HCA showed a good separation between the three groups of cannabis samples at different growth stages. The PLS-DA and SVM-DA classified the samples with good results in terms of sensitivity and specificity. The sensitivity and specificity for SVM-DA classification were equal to unity. This separation may be due to the correlation of cannabinoids and volatile compounds concentration during the growth of the cannabis plant. Therefore, the growth stage of cannabis can be predicted by NIR spectroscopy and chemometric tools in the early stages of indoor cannabis cultivation. Copyright © 2016 Elsevier B.V. All rights reserved.

Near infrared spectroscopy combined with chemometrics for growth stage classification of cannabis cultivated in a greenhouse from seized seeds

NASA Astrophysics Data System (ADS)

Borille, Bruna Tassi; Marcelo, Marcelo Caetano Alexandre; Ortiz, Rafael Scorsatto; Mariotti, Kristiane de Cássia; Ferrão, Marco Flôres; Limberger, Renata Pereira

2017-02-01

Cannabis sativa L. (cannabis, Cannabaceae), popularly called marijuana, is one of the oldest plants known to man and it is the illicit drug most used worldwide. It also has been the subject of increasing discussions from the scientific and political points of view due to its medicinal properties. In recent years in Brazil, the form of cannabis drug trafficking has been changing and the Brazilian Federal Police has exponentially increased the number of seizures of cannabis seeds sent by the mail. This new form of trafficking encouraged the study of cannabis seeds seized germinated in a greenhouse through NIR spectroscopy combined with chemometrics. The plants were cultivated in a homemade greenhouse under controlled conditions. In three different growth periods (5.5 weeks, 7.5 weeks and 10 weeks), they were harvested, dried, ground and directly analyzed. The iPCA was used to select the best NIR spectral range (4000-4375 cm- 1) in order to develop unsupervised and supervised methods. The PCA and HCA showed a good separation between the three groups of cannabis samples at different growth stages. The PLS-DA and SVM-DA classified the samples with good results in terms of sensitivity and specificity. The sensitivity and specificity for SVM-DA classification were equal to unity. This separation may be due to the correlation of cannabinoids and volatile compounds concentration during the growth of the cannabis plant. Therefore, the growth stage of cannabis can be predicted by NIR spectroscopy and chemometric tools in the early stages of indoor cannabis cultivation.

Learning Principal Component Analysis by Using Data from Air Quality Networks

ERIC Educational Resources Information Center

Perez-Arribas, Luis Vicente; Leon-González, María Eugenia; Rosales-Conrado, Noelia

2017-01-01

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…

Can odors of TCM be captured by electronic nose? The novel quality control method for musk by electronic nose coupled with chemometrics.

PubMed

Ye, Tao; Jin, Cheng; Zhou, Jian; Li, Xingfeng; Wang, Haitao; Deng, Pingye; Yang, Ying; Wu, Yanwen; Xiao, Xiaohe

2011-07-15

Musk is a precious and wide applied material in traditional Chinese medicine, also, an important material for the perfume industry all over the world. To establish a rapid, cost-effective and relatively objective assessment for the quality of musk, different musk samples, including authentic, fake and adulterate, were collected. A oxide sensor based electronic nose (E-nose) was employed to measure the musk samples, the E-nose generated data were analyzed by principal component analysis (PCA), the responses of 18 sensors of E-nose were evaluated by loading analysis. Results showed that a rapid evaluation of complex response of the samples could be obtained, in combination with PCA and the perception level of the E-nose was given better results in the recognition values of the musk aroma. The authentic, fake and adulterate musk could be distinguished by E-nose coupled with PCA, sensor 2, 3, 5, 12, 15 and 17 were found to be able to better discriminate between musk samples, confirming the potential application of an electronic instrument coupled with chemometrics for a rapid and on-line quality control for the traditional medicines. Copyright © 2011 Elsevier B.V. All rights reserved.

Simulated aging of lubricant oils by chemometric treatment of infrared spectra: potential antioxidant properties of sulfur structures.

PubMed

Amat, Sandrine; Braham, Zeineb; Le Dréau, Yveline; Kister, Jacky; Dupuy, Nathalie

2013-03-30

Lubricant oils are complex mixtures of base oils and additives. The evolution of their performance over time strongly depends on its resistance to thermal oxidation. Sulfur compounds revealed interesting antioxidant properties. This study presents a method to evaluate the lubricant oil oxidation. Two samples, a synthetic and a paraffinic base oils, were tested pure and supplemented with seven different sulfur compounds. An aging cell adapted to a Fourier Transform InfraRed (FT-IR) spectrometer allows the continuous and direct analysis of the oxidative aging of base oils. Two approaches were applied to study the oxidation/anti-oxidation phenomena. The first one leads to define a new oxidative spectroscopic index based on a reduced spectral range where the modifications have been noticed (from 3050 to 2750 cm(-1)). The second method is based on chemometric treatments of whole spectra (from 4000 to 400 cm(-1)) to extract underlying information. A SIMPLe-to-use Interactive Self Modeling Analysis (SIMPLISMA) method has been used to identify more precisely the chemical species produced or degraded during the thermal treatment and to follow their evolution. Pure spectra of different species present in oil were obtained without prior information of their existence. The interest of this tool is to supply relative quantitative information reflecting evolution of the relative abundance of the different products over thermal aging. Results obtained by these two ways have been compared to estimate their concordance. Copyright © 2013 Elsevier B.V. All rights reserved.

Distribution and mobility of metals in contaminated sites. chemometric investigation of pollutant profiles.

PubMed

Abollino, Ornella; Aceto, Maurizio; Malandrino, Mery; Mentasti, Edoardo; Sarzanini, Corrado; Barberis, Renzo

2002-01-01

The distribution and mobility of heavy metals in the soils of two contaminated sites in Piedmont (Italy) was investigated, evaluating the horizontal and vertical profiles of 15 metals, namely Al, Cd, Cu, Cr, Fe. La, Mn, Ni, Pb, Sc, Ti, V, Y, Zn and Zr. The concentrations in the most polluted areas of the sites were higher than the acceptable limits reported in Italian and Dutch legislations for soil reclamation. Chemometric elaboration of the results by pattern recognition techniques allowed us to identify groups of samples with similar characteristics and to find correlations among the variables. The pollutant mobility was studied by extraction with water, dilute acetic acid and EDTA and by applying Tessier's procedure. The fraction of mobile species, which potentially is the most harmful for the environment, was found to be higher than the one normally present in unpolluted soils, where heavy metals are, to a higher extent, strongly bound to the matrix.

Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics.

PubMed

Ribeiro, J S; Augusto, F; Salva, T J G; Ferreira, M M C

2012-11-15

In this work, soft modeling based on chemometric analyses of coffee beverage sensory data and the chromatographic profiles of volatile roasted coffee compounds is proposed to predict the scores of acidity, bitterness, flavor, cleanliness, body, and overall quality of the coffee beverage. A partial least squares (PLS) regression method was used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the compounds for the regression model of each sensory attribute in order to take only significant chromatographic peaks into account. The prediction errors of these models, using 4 or 5 latent variables, were equal to 0.28, 0.33, 0.35, 0.33, 0.34 and 0.41, for each of the attributes and compatible with the errors of the mean scores of the experts. Thus, the results proved the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of Brazilian Arabica coffee. Copyright © 2012 Elsevier B.V. All rights reserved.

Enantiomeric analysis of overlapped chromatographic profiles in the presence of interferences. Determination of ibuprofen in a pharmaceutical formulation containing homatropine.

PubMed

Padró, J M; Osorio-Grisales, J; Arancibia, J A; Olivieri, A C; Castells, C B

2016-10-07

In this work, we studied the combination of chemometric methods with chromatographic separations as a strategy applied to the analysis of enantiomers when complete enantioseparation is difficult or requires long analysis times and, in addition, the target signals have interference from the matrix. We present the determination of ibuprofen enantiomers in pharmaceutical formulations containing homatropine as interference by chiral HPLC-DAD detection in combination with partial least-squares algorithms. The method has been applied to samples containing enantiomeric ratios from 95:5 to 99.5:0.5 and coelution of interferents. The results were validated using univariate calibration and without homatropine. Relative error of the method was less than 4.0%, for both enantiomers. Limits of detection (LOD) and quantification (LOQ) for (S)-(+)-ibuprofen were 4.96×10 -10 and 1.50×10 -9 mol, respectively. LOD and LOQ for the R-(-)-ibuprofen were LOD=1.60×10 -11 mol and LOQ=4.85×10 -11 mol, respectively. Finally, the chemometric method was applied to the determination of enantiomeric purity of commercial pharmaceuticals. The ultimate goal of this research was the development of rapid, reliable, and robust methods for assessing enantiomeric purity by conventional diode array detector assisted by chemometric tools. Copyright © 2016 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of ash, potassium and magnesium in gluten free flours.

PubMed

Markiewicz-Keszycka, Maria; Casado-Gavalda, Maria P; Cama-Moncunill, Xavier; Cama-Moncunill, Raquel; Dixit, Yash; Cullen, Patrick J; Sullivan, Carl

2018-04-01

Gluten free (GF) diets are prone to mineral deficiency, thus effective monitoring of the elemental composition of GF products is important to ensure a balanced micronutrient diet. The objective of this study was to test the potential of laser-induced breakdown spectroscopy (LIBS) analysis combined with chemometrics for at-line monitoring of ash, potassium and magnesium content of GF flours: tapioca, potato, maize, buckwheat, brown rice and a GF flour mixture. Concentrations of ash, potassium and magnesium were determined with reference methods and LIBS. PCA analysis was performed and presented the potential for discrimination of the six GF flours. For the quantification analysis PLSR models were developed; R 2 cal were 0.99 for magnesium and potassium and 0.97 for ash. The study revealed that LIBS combined with chemometrics is a convenient method to quantify concentrations of ash, potassium and magnesium and present the potential to classify different types of flours. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fast and simultaneous determination of 12 polyphenols in apple peel and pulp by using chemometrics-assisted high-performance liquid chromatography with diode array detection.

PubMed

Wang, Tong; Wu, Hai-Long; Xie, Li-Xia; Zhu, Li; Liu, Zhi; Sun, Xiao-Dong; Xiao, Rong; Yu, Ru-Qin

2017-04-01

In this work, a smart chemometrics-enhanced strategy, high-performance liquid chromatography, and diode array detection coupled with second-order calibration method based on alternating trilinear decomposition algorithm was proposed to simultaneously quantify 12 polyphenols in different kinds of apple peel and pulp samples. The proposed strategy proved to be a powerful tool to solve the problems of coelution, unknown interferences, and chromatographic shifts in the process of high-performance liquid chromatography analysis, making it possible for the determination of 12 polyphenols in complex apple matrices within 10 min under simple conditions of elution. The average recoveries with standard deviations, and figures of merit including sensitivity, selectivity, limit of detection, and limit of quantitation were calculated to validate the accuracy of the proposed method. Compared to the quantitative analysis results from the classic high-performance liquid chromatography method, the statistical and graphical analysis showed that our proposed strategy obtained more reliable results. All results indicated that our proposed method used in the quantitative analysis of apple polyphenols was an accurate, fast, universal, simple, and green one, and it was expected to be developed as an attractive alternative method for simultaneous determination of multitargeted analytes in complex matrices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization and geographical discrimination of commercial Citrus spp. honeys produced in different Mediterranean countries based on minerals, volatile compounds and physicochemical parameters, using chemometrics.

PubMed

Karabagias, Ioannis K; Louppis, Artemis P; Karabournioti, Sofia; Kontakos, Stavros; Papastephanou, Chara; Kontominas, Michael G

2017-02-15

The objective of the present study was: i) to characterize Mediterranean citrus honeys based on conventional physicochemical parameter values, volatile compounds, and mineral content ii) to investigate the potential of above parameters to differentiate citrus honeys according to geographical origin using chemometrics. Thus, 37 citrus honey samples were collected during harvesting periods 2013 and 2014 from Greece, Egypt, Morocco, and Spain. Conventional physicochemical and CIELAB colour parameters were determined using official methods of analysis and the Commission Internationale de l' Eclairage recommendations, respectively. Minerals were determined using ICP-OES and volatiles using SPME-GC/MS. Results showed that honey samples analyzed, met the standard quality criteria set by the EU and were successfully classified according to geographical origin. Correct classification rates were 97.3% using 8 physicochemical parameter values, 86.5% using 15 volatile compound data and 83.8% using 13 minerals. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chemometrics Optimized Extraction Procedures, Phytosynergistic Blending and in vitro Screening of Natural Enzyme Inhibitors Amongst Leaves of Tulsi, Banyan and Jamun.

PubMed

De, Baishakhi; Bhandari, Koushik; Singla, Rajeev K; Katakam, Prakash; Samanta, Tanmoy; Kushwaha, Dilip Kumar; Gundamaraju, Rohit; Mitra, Analava

2015-10-01

Tulsi, Banyan, and Jamun are popular Indian medicinal plants with notable hypoglycemic potentials. Now the work reports chemo-profiling of the three species with in-vitro screening approach for natural enzyme inhibitors (NEIs) against enzymes pathogenic for type 2 diabetes. Further along with the chemometrics optimized extraction process technology, phyto-synergistic studies of the composite polyherbal blends have also been reported. Chemometrically optimized extraction procedures, ratios of polyherbal composites to achieve phyto-synergistic actions, and in-vitro screening of NEIs amongst leaves of Tulsi, Banyan, and Jamun. The extraction process parameters of the leaves of three plant species (Ficus benghalensis, Syzigium cumini and Ocimum sanctum) were optimized by rotatable central composite design of chemometrics so as to get maximal yield of bio-actives. Phyto-blends of three species were prepared so as to achieve synergistic antidiabetic and antioxidant potentials and the ratios were optimized by chemometrics. Next, for in vitro screening of natural enzyme inhibitors the individual leaf extracts as well as composite blends were subjected to assay procedures to see their inhibitory potentials against the enzymes pathogenic in type 2 diabetes. The antioxidant potentials were also estimated by DPPH radical scavenging, ABTS, FRAP and Dot Blot assay. Considering response surface methodology studies and from the solutions obtained using desirability function, it was found that hydro-ethanolic or methanolic solvent ratio of 52.46 ± 1.6 and at a temperature of 20.17 ± 0.6 gave an optimum yield of polyphenols with minimal chlorophyll leaching. The species also showed the presence of glycosides, alkaloids, and saponins. Composites in the ratios of 1:1:1 and 1:1:2 gave synergistic effects in terms of polyphenol yield and anti-oxidant potentials. All composites (1:1:1, 1:2:1, 2:1:1, 1:1:2) showed synergistic anti-oxidant actions. Inhibitory activities against the

Chemometrics Optimized Extraction Procedures, Phytosynergistic Blending and in vitro Screening of Natural Enzyme Inhibitors Amongst Leaves of Tulsi, Banyan and Jamun

PubMed Central

De, Baishakhi; Bhandari, Koushik; Singla, Rajeev K.; Katakam, Prakash; Samanta, Tanmoy; Kushwaha, Dilip Kumar; Gundamaraju, Rohit; Mitra, Analava

2015-01-01

Background: Tulsi, Banyan, and Jamun are popular Indian medicinal plants with notable hypoglycemic potentials. Now the work reports chemo-profiling of the three species with in-vitro screening approach for natural enzyme inhibitors (NEIs) against enzymes pathogenic for type 2 diabetes. Further along with the chemometrics optimized extraction process technology, phyto-synergistic studies of the composite polyherbal blends have also been reported. Objective: Chemometrically optimized extraction procedures, ratios of polyherbal composites to achieve phyto-synergistic actions, and in-vitro screening of NEIs amongst leaves of Tulsi, Banyan, and Jamun. Materials and Methods: The extraction process parameters of the leaves of three plant species (Ficus benghalensis, Syzigium cumini and Ocimum sanctum) were optimized by rotatable central composite design of chemometrics so as to get maximal yield of bio-actives. Phyto-blends of three species were prepared so as to achieve synergistic antidiabetic and antioxidant potentials and the ratios were optimized by chemometrics. Next, for in vitro screening of natural enzyme inhibitors the individual leaf extracts as well as composite blends were subjected to assay procedures to see their inhibitory potentials against the enzymes pathogenic in type 2 diabetes. The antioxidant potentials were also estimated by DPPH radical scavenging, ABTS, FRAP and Dot Blot assay. Results: Considering response surface methodology studies and from the solutions obtained using desirability function, it was found that hydro-ethanolic or methanolic solvent ratio of 52.46 ± 1.6 and at a temperature of 20.17 ± 0.6 gave an optimum yield of polyphenols with minimal chlorophyll leaching. The species also showed the presence of glycosides, alkaloids, and saponins. Composites in the ratios of 1:1:1 and 1:1:2 gave synergistic effects in terms of polyphenol yield and anti-oxidant potentials. All composites (1:1:1, 1:2:1, 2:1:1, 1:1:2) showed synergistic anti

Combination of Analytical and Chemometric Methods as a Useful Tool for the Characterization of Extra Virgin Argan Oil and Other Edible Virgin Oils. Role of Polyphenols and Tocopherols.

PubMed

Rueda, Ascensión; Samaniego-Sánchez, Cristina; Olalla, Manuel; Giménez, Rafael; Cabrera-Vique, Carmen; Seiquer, Isabel; Lara, Luis

2016-01-01

Analysis of phenolic profile and tocopherol fractions in conjunction with chemometrics techniques were used for the accurate characterization of extra virgin argan oil and eight other edible vegetable virgin oils (olive, soybean, wheat germ, walnut, almond, sesame, avocado, and linseed) and to establish similarities among them. Phenolic profile and tocopherols were determined by HPLC coupled with diode-array and fluorescence detectors, respectively. Multivariate factor analysis (MFA) and linear correlations were applied. Significant negative correlations were found between tocopherols and some of the polyphenols identified, but more intensely (P < 0.001) between the γ-tocopherol and oleuropein, pinoresinol, and luteolin. MFA revealed that tocopherols, especially γ-fraction, most strongly influenced the oil characterization. Among the phenolic compounds, syringic acid, dihydroxybenzoic acid, oleuropein, pinoresinol, and luteolin also contributed to the discrimination of the oils. According to the variables analyzed in the present study, argan oil presented the greatest similarity with walnut oil, followed by sesame and linseed oils. Olive, avocado, and almond oils showed close similarities.

Combining vibrational biomolecular spectroscopy with chemometric techniques for the study of response and sensitivity of molecular structures/functional groups mainly related to lipid biopolymer to various processing applications.

PubMed

Yu, Gloria Qingyu; Yu, Peiqiang

2015-09-01

The objectives of this project were to (1) combine vibrational spectroscopy with chemometric multivariate techniques to determine the effect of processing applications on molecular structural changes of lipid biopolymer that mainly related to functional groups in green- and yellow-type Crop Development Centre (CDC) pea varieties [CDC strike (green-type) vs. CDC meadow (yellow-type)] that occurred during various processing applications; (2) relatively quantify the effect of processing applications on the antisymmetric CH3 ("CH3as") and CH2 ("CH2as") (ca. 2960 and 2923 cm(-1), respectively), symmetric CH3 ("CH3s") and CH2 ("CH2s") (ca. 2873 and 2954 cm(-1), respectively) functional groups and carbonyl C=O ester (ca. 1745 cm(-1)) spectral intensities as well as their ratios of antisymmetric CH3 to antisymmetric CH2 (ratio of CH3as to CH2as), ratios of symmetric CH3 to symmetric CH2 (ratio of CH3s to CH2s), and ratios of carbonyl C=O ester peak area to total CH peak area (ratio of C=O ester to CH); and (3) illustrate non-invasive techniques to detect the sensitivity of individual molecular functional group to the various processing applications in the recently developed different types of pea varieties. The hypothesis of this research was that processing applications modified the molecular structure profiles in the processed products as opposed to original unprocessed pea seeds. The results showed that the different processing methods had different impacts on lipid molecular functional groups. Different lipid functional groups had different sensitivity to various heat processing applications. These changes were detected by advanced molecular spectroscopy with chemometric techniques which may be highly related to lipid utilization and availability. The multivariate molecular spectral analyses, cluster analysis, and principal component analysis of original spectra (without spectral parameterization) are unable to fully distinguish the structural differences in the

Quality assessment of Penthorum chinense Pursh through multicomponent qualification and fingerprint, chemometric, and antihepatocarcinoma analyses.

PubMed

Sun, Zong-Liang; Zhang, Yu-Zhen; Zhang, Feng; Zhang, Jia-Wei; Zheng, Guo-Can; Tan, Ling; Wang, Chong-Zhi; Zhou, Lian-Di; Zhang, Qi-Hui; Yuan, Chun-Su

2018-06-22

An efficient method combined with fingerprint and chemometric analyses was developed to evaluate the quality of the traditional Chinese medicine plant Penthorum chinense Pursh. Nine samples were collected from different regions during different harvest periods, and 17 components in the form of extracts were simultaneously examined to assess quality by using high-performance liquid chromatography. The hepatoprotective effects of components were investigated by assessing the inhibition of SMMC-7721 cell growth. The results indicated that the quality control method was accurate, stable, and reliable, and the hierarchical heat-map cluster and the principle component analyses confirmed that the classification of all nine samples was consistent. Quercetin and ellagitannins including pinocembrin-7-O-[3''-O-galloyl-4'',6''-hexahydroxydiphenoyl]-β-glucose (PGHG), thonningianin A, thonningianin B, and other flavonoids were abundant in the extracts, and significantly contributed to the hepatoprotective effects.

Chemometric-assisted QuEChERS extraction method for post-harvest pesticide determination in fruits and vegetables

NASA Astrophysics Data System (ADS)

Li, Minmin; Dai, Chao; Wang, Fengzhong; Kong, Zhiqiang; He, Yan; Huang, Ya Tao; Fan, Bei

2017-02-01

An effective analysis method was developed based on a chemometric tool for the simultaneous quantification of five different post-harvest pesticides (2,4-dichlorophenoxyacetic acid (2,4-D), carbendazim, thiabendazole, iprodione, and prochloraz) in fruits and vegetables. In the modified QuEChERS (quick, easy, cheap, effective, rugged and safe) method, the factors and responses for optimization of the extraction and cleanup analyses were compared using the Plackett-Burman (P-B) screening design. Furthermore, the significant factors (toluene percentage, hydrochloric acid (HCl) percentage, and graphitized carbon black (GCB) amount) were optimized using a central composite design (CCD) combined with Derringer’s desirability function (DF). The limits of quantification (LOQs) were estimated to be 1.0 μg/kg for 2,4-D, carbendazim, thiabendazole, and prochloraz, and 1.5 μg/kg for iprodione in food matrices. The mean recoveries were in the range of 70.4-113.9% with relative standard deviations (RSDs) of less than 16.9% at three spiking levels. The measurement uncertainty of the analytical method was determined using the bottom-up approach, which yielded an average value of 7.6%. Carbendazim was most frequently found in real samples analyzed using the developed method. Consequently, the analytical method can serve as an advantageous and rapid tool for determination of five preservative pesticides in fruits and vegetables.

Chemometric-assisted QuEChERS extraction method for post-harvest pesticide determination in fruits and vegetables

PubMed Central

Li, Minmin; Dai, Chao; Wang, Fengzhong; Kong, Zhiqiang; He, Yan; Huang, Ya Tao; Fan, Bei

2017-01-01

An effective analysis method was developed based on a chemometric tool for the simultaneous quantification of five different post-harvest pesticides (2,4-dichlorophenoxyacetic acid (2,4-D), carbendazim, thiabendazole, iprodione, and prochloraz) in fruits and vegetables. In the modified QuEChERS (quick, easy, cheap, effective, rugged and safe) method, the factors and responses for optimization of the extraction and cleanup analyses were compared using the Plackett–Burman (P–B) screening design. Furthermore, the significant factors (toluene percentage, hydrochloric acid (HCl) percentage, and graphitized carbon black (GCB) amount) were optimized using a central composite design (CCD) combined with Derringer’s desirability function (DF). The limits of quantification (LOQs) were estimated to be 1.0 μg/kg for 2,4-D, carbendazim, thiabendazole, and prochloraz, and 1.5 μg/kg for iprodione in food matrices. The mean recoveries were in the range of 70.4–113.9% with relative standard deviations (RSDs) of less than 16.9% at three spiking levels. The measurement uncertainty of the analytical method was determined using the bottom-up approach, which yielded an average value of 7.6%. Carbendazim was most frequently found in real samples analyzed using the developed method. Consequently, the analytical method can serve as an advantageous and rapid tool for determination of five preservative pesticides in fruits and vegetables. PMID:28225030

Chemometric-assisted QuEChERS extraction method for post-harvest pesticide determination in fruits and vegetables.

PubMed

Li, Minmin; Dai, Chao; Wang, Fengzhong; Kong, Zhiqiang; He, Yan; Huang, Ya Tao; Fan, Bei

2017-02-22

An effective analysis method was developed based on a chemometric tool for the simultaneous quantification of five different post-harvest pesticides (2,4-dichlorophenoxyacetic acid (2,4-D), carbendazim, thiabendazole, iprodione, and prochloraz) in fruits and vegetables. In the modified QuEChERS (quick, easy, cheap, effective, rugged and safe) method, the factors and responses for optimization of the extraction and cleanup analyses were compared using the Plackett-Burman (P-B) screening design. Furthermore, the significant factors (toluene percentage, hydrochloric acid (HCl) percentage, and graphitized carbon black (GCB) amount) were optimized using a central composite design (CCD) combined with Derringer's desirability function (DF). The limits of quantification (LOQs) were estimated to be 1.0 μg/kg for 2,4-D, carbendazim, thiabendazole, and prochloraz, and 1.5 μg/kg for iprodione in food matrices. The mean recoveries were in the range of 70.4-113.9% with relative standard deviations (RSDs) of less than 16.9% at three spiking levels. The measurement uncertainty of the analytical method was determined using the bottom-up approach, which yielded an average value of 7.6%. Carbendazim was most frequently found in real samples analyzed using the developed method. Consequently, the analytical method can serve as an advantageous and rapid tool for determination of five preservative pesticides in fruits and vegetables.

Chemometric analysis of minerals in gluten-free products.

PubMed

Gliszczyńska-Świgło, Anna; Klimczak, Inga; Rybicka, Iga

2018-06-01

Numerous studies indicate mineral deficiencies in people on a gluten-free (GF) diet. These deficiencies may indicate that GF products are a less valuable source of minerals than gluten-containing products. In the study, the nutritional quality of 50 GF products is discussed taking into account the nutritional requirements for minerals expressed as percentage of recommended daily allowance (%RDA) or percentage of adequate intake (%AI) for a model celiac patient. Elements analyzed were calcium, potassium, magnesium, sodium, copper, iron, manganese, and zinc. Analysis of %RDA or %AI was performed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Using PCA, the differentiation between products based on rice, corn, potato, GF wheat starch and based on buckwheat, chickpea, millet, oats, amaranth, teff, quinoa, chestnut, and acorn was possible. In the HCA, four clusters were created. The main criterion determining the adherence of the sample to the cluster was the content of all minerals included to HCA (K, Mg, Cu, Fe, Mn); however, only the Mn content differentiated four formed groups. GF products made of buckwheat, chickpea, millet, oats, amaranth, teff, quinoa, chestnut, and acorn are better source of minerals than based on other GF raw materials, what was confirmed by PCA and HCA. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

"Turn-off" fluorescent data array sensor based on double quantum dots coupled with chemometrics for highly sensitive and selective detection of multicomponent pesticides.

PubMed

Fan, Yao; Liu, Li; Sun, Donglei; Lan, Hanyue; Fu, Haiyan; Yang, Tianming; She, Yuanbin; Ni, Chuang

2016-04-15

As a popular detection model, the fluorescence "turn-off" sensor based on quantum dots (QDs) has already been successfully employed in the detections of many materials, especially in the researches on the interactions between pesticides. However, the previous studies are mainly focused on simple single track or the comparison based on similar concentration of drugs. In this work, a new detection method based on the fluorescence "turn-off" model with water-soluble ZnCdSe and CdSe QDs simultaneously as the fluorescent probes is established to detect various pesticides. The fluorescence of the two QDs can be quenched by different pesticides with varying degrees, which leads to the differences in positions and intensities of two peaks. By combining with chemometrics methods, all the pesticides can be qualitative and quantitative respectively even in real samples with the limit of detection was 2 × 10(-8) mol L(-1) and a recognition rate of 100%. This work is, to the best of our knowledge, the first report on the detection of pesticides based on the fluorescence quenching phenomenon of double quantum dots combined with chemometrics methods. What's more, the excellent selectivity of the system has been verified in different mediums such as mixed ion disruption, waste water, tea and water extraction liquid drugs. Copyright © 2016 Elsevier B.V. All rights reserved.

FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: Classification of samples for the presence of lactose and fraud detection by addition of maltodextrin.

PubMed

Rodrigues Júnior, Paulo Henrique; de Sá Oliveira, Kamila; de Almeida, Carlos Eduardo Rocha; De Oliveira, Luiz Fernando Cappa; Stephani, Rodrigo; Pinto, Michele da Silva; de Carvalho, Antônio Fernandes; Perrone, Ítalo Tuler

2016-04-01

FT-Raman spectroscopy has been explored as a quick screening method to evaluate the presence of lactose and identify milk powder samples adulterated with maltodextrin (2.5-50% w/w). Raman measurements can easily differentiate samples of milk powder, without the need for sample preparation, while traditional quality control methods, including high performance liquid chromatography, are cumbersome and slow. FT-Raman spectra were obtained from samples of whole lactose and low-lactose milk powder, both without and with addition of maltodextrin. Differences were observed between the spectra involved in identifying samples with low lactose content, as well as adulterated samples. Exploratory data analysis using Raman spectroscopy and multivariate analysis was also developed to classify samples with PCA and PLS-DA. The PLS-DA models obtained allowed to correctly classify all samples. These results demonstrate the utility of FT-Raman spectroscopy in combination with chemometrics to infer about the quality of milk powder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development and validation of NIR-chemometric methods for chemical and pharmaceutical characterization of meloxicam tablets.

PubMed

Tomuta, Ioan; Iovanov, Rares; Bodoki, Ede; Vonica, Loredana

2014-04-01

Near-Infrared (NIR) spectroscopy is an important component of a Process Analytical Technology (PAT) toolbox and is a key technology for enabling the rapid analysis of pharmaceutical tablets. The aim of this research work was to develop and validate NIR-chemometric methods not only for the determination of active pharmaceutical ingredients content but also pharmaceutical properties (crushing strength, disintegration time) of meloxicam tablets. The development of the method for active content assay was performed on samples corresponding to 80%, 90%, 100%, 110% and 120% of meloxicam content and the development of the methods for pharmaceutical characterization was performed on samples prepared at seven different compression forces (ranging from 7 to 45 kN) using NIR transmission spectra of intact tablets and PLS as a regression method. The results show that the developed methods have good trueness, precision and accuracy and are appropriate for direct active content assay in tablets (ranging from 12 to 18 mg/tablet) and also for predicting crushing strength and disintegration time of intact meloxicam tablets. The comparative data show that the proposed methods are in good agreement with the reference methods currently used for the characterization of meloxicam tablets (HPLC-UV methods for the assay and European Pharmacopeia methods for determining the crushing strength and disintegration time). The results show the possibility to predict both chemical properties (active content) and physical/pharmaceutical properties (crushing strength and disintegration time) directly, without any sample preparation, from the same NIR transmission spectrum of meloxicam tablets.

Exploring hyperspectral imaging data sets with topological data analysis.

PubMed

Duponchel, Ludovic

2018-02-13

Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of growing location on the production of main active components and antioxidant activity of Dasiphora fruticosa (L.) Rydb. by chemometric methods.

PubMed

Liu, Wei; Wang, Dongmei; Hou, Xiaogai; Yang, Yueqin; Xue, Xian; Jia, Qishi; Zhang, Lixia; Zhao, Wei; Yin, Dongxue

2018-05-17

Traditional Chinese medicine (TCM) plays a very important role in the health system of China. The content and activity of active component are main indexes that evaluate the quality of TCM, however they may vary with environmental factors in their growing locations. In this study, effects of environmental factors on the contents of active components and antioxidant activity of Dasiphora fruticosa from the five main production areas of China were investigated. The contents of tannin, total flavonoid and rutin were determined and varied within the range of 7.65-10.69%, 2.30-5.39% and 0.18-0.81%, respectively. Antioxidant activity was determined by DPPH assay, with the DPPH IC 50 values ranged from 8.791 to 32.534μg mL -1 . In order to further explore the cause of these significant geographical variations, the chemometric methods including correlation analysis, principal component analysis, gray correlation analysis, and path analysis were conducted. The results showed environmental factors had significant effect on the active component contents and antioxidant activity. Rapidly available phosphorus (RAP) and rapidly available nitrogen (RAN) were common dominant factors, and a significant positive correlation was observed between RAP and active components and antioxidant activity (P<0.05). Contributed by their high active components and strong antioxidant activity, Bange in Tibet and Geermu in Qinghai Province was selected as a favorable growing location, respectively. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Qualitative and quantitative control of pediatric syrups using Nuclear Magnetic Resonance and chemometrics.

PubMed

Alves Filho, Elenilson G; Silva, Lorena Mara A; Araújo, Nathália V P; Alves, Elenilson G; Lião, Luciano M; Alcantara, Glaucia B

2018-05-10

Several flavoring and sweetening agents added to excipient of pediatric syrups are not declared in the package leaflet. Therefore, the aim of this study was to develop a non-target, simple, and precise method for qualitative and quantitative evaluation of pediatric syrups using NMR spectroscopy combined with chemometrics. This approach allowed the identification of several added compounds as citric acid, cyclamate, ethanol, glycerol, propylene glycol, saccharin, sorbitol, fructose, glucose, and sucrose. Among the sugared syrups, sucrose was the main carbohydrate with approximately 59.1%, and for sweetened syrups, glycerol with 25.5%. The ethanol was found with highest concentration of 4.0%, approximately. In addition, some syrups presented both sugar and sweetener, which is inconsistent according to the purpose of the addition. Consequently, institutional structures of countries as Brazil that are in charge of public health should put additional compliance pressure on pharmaceutical companies to clearly declare in package leaflet the presence and exact amount of the main compounds (at least) existent in the pediatric excipients. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a Direct Spectrophotometric and Chemometric Method for Determining Food Dye Concentrations.

PubMed

Arroz, Erin; Jordan, Michael; Dumancas, Gerard G

2017-07-01

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n = 64) was generated using a full factorial design and its accuracy was tested in a test set (n = 13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.79 × 10 -7 for blue, 4.59 × 10 -7 for red, and 1.13 × 10 -6 for yellow dyes. The relatively small RMSE suggests only a small difference between predicted versus measured concentrations, demonstrating the accuracy of our model. The relative error of prediction (REP) for the test set were 11.73%, 19.52%, 19.38%, for blue, red, and yellow dyes, respectively. A comparable overlay between the actual candy samples and their replicated synthetic spectra were also obtained indicating the model as a potentially accurate method for determining concentrations of dyes in food samples.

Chemometric evaluation of the volatile profile of probiotic melon and probiotic cashew juice.

PubMed

de Godoy Alves Filho, Elenilson; Rodrigues, Tigressa Helena Soares; Fernandes, Fabiano André Narciso; Pereira, Ana Lucia Fernandes; Narain, Narendra; de Brito, Edy Sousa; Rodrigues, Sueli

2017-09-01

The aim of this study was to evaluate the influence of the lactic acid fermentation on volatile compounds of melon and cashew apple juices. The effect of the fermentation processing on the volatile profile of probiotic juices was assessed by HS-SPME/GC-MS coupled to chemometrics with 67.9% and 81.0% of the variance in the first principal component for melon and cashew juices, respectively. The Lactobacillus casei fermentation imparted a reduction of ethyl butanoate, ethyl-2-methylbutirate, and ethyl hexanoate for melon juice; and of ethyl acetate, ethyl-2-methyl butanoate, ethyl crotonate, ethyl isovalerate, benzaldehyde, and ethyl hexanoate for cashew juice. Measurements of the stability of these compounds and the formation of the component 3-methyl-2-butenyl in melon juice may be used as a volatile marker to follow the juice fermentation. These findings suggested that even though it is not a dairy product the lactic acid fermentation of fruits developed a volatile profile combining the fruit and lactic acid fermentation volatiles with mildly formation or degradation of aroma compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2017-05-01

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

Chemometric aided NIR portable instrument for rapid assessment of medicine quality.

PubMed

Zontov, Y V; Balyklova, K S; Titova, A V; Rodionova, O Ye; Pomerantsev, A L

2016-11-30

The progress in instrumentation technology has led to miniaturization of NIR instruments. Fast systems that contain no moving parts were developed to be used in the field, warehouses, drugstores, etc. At the same time, in general these portable/handheld spectrometers have a lower spectral resolution and a narrower spectral region than stationary ones. Vendors of portable instruments supply their equipment with special software for spectra processing, which aims at simplifying the analyst's work to the highest degree possible. Often such software is not fully capable of solving complex problems. In application to a real-world problem of counterfeit drug detection we demonstrate that even impaired spectral data do carry information sufficient for drug authentication. The chemometrics aided approach helps to extract this information and thus to extend the applicability of miniaturized NIR instruments. MicroPhazir-RX NIR spectrometer is used as an example of a portable instrument. The data driven soft independent modeling of class analogy (DD-SIMCA) method is employed for data processing. A representative set of tablets of a calcium channel blocker from 6 different manufacturers is used to illustrate the proposed approach. It is shown that the DD-SIMCA approach yields a better result than the basic method provided by the instrument vendor. Copyright © 2016 Elsevier B.V. All rights reserved.

Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy

NASA Astrophysics Data System (ADS)

Xu, Zhang; Reilley, Michael; Li, Run; Xu, Min

2017-06-01

We report chemometric wide-field fluorescence microscopy for imaging the spatial distribution and concentration of endogenous fluorophores in thin tissue sections. Nonnegative factorization aided by spatial diversity is used to learn both the spectral signature and the spatial distribution of endogenous fluorophores from microscopic fluorescence color images obtained under broadband excitation and detection. The absolute concentration map of individual fluorophores is derived by comparing the fluorescence from "pure" fluorophores under the identical imaging condition following the identification of the fluorescence species by its spectral signature. This method is then demonstrated by characterizing the concentration map of endogenous fluorophores (including tryptophan, elastin, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide) for lung tissue specimens. The absolute concentrations of these fluorophores are all found to decrease significantly from normal, perilesional, to cancerous (squamous cell carcinoma) tissue. Discriminating tissue types using the absolute fluorophore concentration is found to be significantly more accurate than that achievable with the relative fluorescence strength. Quantification of fluorophores in terms of the absolute concentration map is also advantageous in eliminating the uncertainties due to system responses or measurement details, yielding more biologically relevant data, and simplifying the assessment of competing imaging approaches.

Speciation of adsorbates on surface of solids by infrared spectroscopy and chemometrics.

PubMed

Vilmin, Franck; Bazin, Philippe; Thibault-Starzyk, Frédéric; Travert, Arnaud

2015-09-03

Speciation, i.e. identification and quantification, of surface species on heterogeneous surfaces by infrared spectroscopy is important in many fields but remains a challenging task when facing strongly overlapped spectra of multiple adspecies. Here, we propose a new methodology, combining state of the art instrumental developments for quantitative infrared spectroscopy of adspecies and chemometrics tools, mainly a novel data processing algorithm, called SORB-MCR (SOft modeling by Recursive Based-Multivariate Curve Resolution) and multivariate calibration. After formal transposition of the general linear mixture model to adsorption spectral data, the main issues, i.e. validity of Beer-Lambert law and rank deficiency problems, are theoretically discussed. Then, the methodology is exposed through application to two case studies, each of them characterized by a specific type of rank deficiency: (i) speciation of physisorbed water species over a hydrated silica surface, and (ii) speciation (chemisorption and physisorption) of a silane probe molecule over a dehydrated silica surface. In both cases, we demonstrate the relevance of this approach which leads to a thorough surface speciation based on comprehensive and fully interpretable multivariate quantitative models. Limitations and drawbacks of the methodology are also underlined. Copyright © 2015 Elsevier B.V. All rights reserved.

Application of class-modelling techniques to infrared spectra for analysis of pork adulteration in beef jerkys.

PubMed

Kuswandi, Bambang; Putri, Fitra Karima; Gani, Agus Abdul; Ahmad, Musa

2015-12-01

The use of chemometrics to analyse infrared spectra to predict pork adulteration in the beef jerky (dendeng) was explored. In the first step, the analysis of pork in the beef jerky formulation was conducted by blending the beef jerky with pork at 5-80 % levels. Then, they were powdered and classified into training set and test set. The second step, the spectra of the two sets was recorded by Fourier Transform Infrared (FTIR) spectroscopy using atenuated total reflection (ATR) cell on the basis of spectral data at frequency region 4000-700 cm(-1). The spectra was categorised into four data sets, i.e. (a) spectra in the whole region as data set 1; (b) spectra in the fingerprint region (1500-600 cm(-1)) as data set 2; (c) spectra in the whole region with treatment as data set 3; and (d) spectra in the fingerprint region with treatment as data set 4. The third step, the chemometric analysis were employed using three class-modelling techniques (i.e. LDA, SIMCA, and SVM) toward the data sets. Finally, the best result of the models towards the data sets on the adulteration analysis of the samples were selected and the best model was compared with the ELISA method. From the chemometric results, the LDA model on the data set 1 was found to be the best model, since it could classify and predict 100 % accuracy of the sample tested. The LDA model was applied toward the real samples of the beef jerky marketed in Jember, and the results showed that the LDA model developed was in good agreement with the ELISA method.

Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics.

PubMed

Yan, Hui; Guo, Cheng; Shao, Yang; Ouyang, Zhen

2017-01-01

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient ( R ) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx . The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx . Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx . Abbreviations used: 1 st der

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape.

PubMed

Longobardi, Francesco; Casiello, Grazia; Centonze, Valentina; Catucci, Lucia; Agostiano, Angela

2017-08-01

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. 2 H/ 1 H, 13 C/ 12 C, 15 N/ 14 N and 18 O/ 16 O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape. In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ 13 C and δ 18 O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively. The present findings suggest that stable isotopes (i.e. δ 18 O, δ 2 H and δ 13 C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

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

PubMed

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

2014-12-01

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

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

Molecularly imprinted polymer for determination of lumefantrine in human plasma through chemometric-assisted solid-phase extraction and liquid chromatography.

PubMed

da Silva, Pedro Henrique Reis; Diniz, Melina Luiza Vieira; Pianetti, Gerson Antônio; da Costa César, Isabela; Ribeiro E Silva, Maria Elisa Scarpelli; de Souza Freitas, Roberto Fernando; de Sousa, Ricardo Geraldo; Fernandes, Christian

2018-07-01

Lumefantrine is the first-choice treatment of Falciparum uncomplicated malaria. Recent findings of resistance to lumefantrine has brought attention for the importance of therapeutic monitoring, since exposure to subtherapeutic doses of antimalarials after administration is a major cause of selection of resistant parasites. Therefore, this study focused on the development of innovative, selective, less expensive and stable molecularly imprinted polymers (MIPs) for solid-phase extraction (SPE) of lumefantrine from human plasma to be used in drug monitoring. Polymers were synthesized by precipitation polymerization and chemometric tools (Box-Behnken design and surface response methodology) were employed for rational optimization of synthetic parameters. Optimum conditions were achieved with 2-vinylpyridine as monomer, ethylene glycol dimethacrylate as crosslinker and toluene as porogen, at molar ratio of 1:6:30 of template/monomer/crosslinker and azo-bisisobutyronitrile as initiator at 65 °C. The MIP obtained was characterized and exhibited high thermal stability, adequate surface morphology and porosity characteristics and high binding properties, with high affinity (adsorption capacity of 977.83 μg g -1 ) and selectivity (imprinting factor of 2.44; and selectivity factor of 1.48 and selectivity constant of 1.44 compared with halofantrine). Doehlert matrix and fractional designs were satisfactorily used for development and optimization of a MISPE-HPLC-UV method for determination of lumefantrine. The method fulfilled all validation parameters, with recoveries ranging from 83.68% to 85.42%, and was applied for quantitation of the drug in plasma from two healthy volunteers, with results of 1407.89 and 1271.35 ng mL -1 , respectively. Therefore, the MISPE-HPLC-UV method optimized through chemometrics provided a rapid, highly selective, less expensive and reproducible approach for lumefantrine drug monitoring. Copyright © 2018 Elsevier B.V. All rights

Chemometric modeling of 5-Phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents.

PubMed

Adhikari, Nilanjan; Jana, Dhritiman; Halder, Amit K; Mondal, Chanchal; Maiti, Milan K; Jha, Tarun

2012-09-01

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted

Fatty acid composition of wild mushroom species of order Agaricales--examination by gas chromatography-mass spectrometry and chemometrics.

PubMed

Marekov, Ilko; Momchilova, Svetlana; Grung, Bjørn; Nikolova-Damyanova, Boryana

2012-12-01

Applying gas chromatography-mass spectrometry of 4,4-dimethyloxazoline fatty acid derivatives, the fatty acid composition of 15 mushroom species belonging to 9 genera and 5 families of order Agaricales growing in Bulgaria is determined. The structure of 31 fatty acids (not all present in each species) is unambiguously elucidated, with linoleic, oleic and palmitic acids being the main components (ranging between 70.9% (Marasmius oreades) and 91.2% (Endoptychum agaricoides)). A group of three hexadecenoic positionally isomeric fatty acids, 6-, 9- and 11-16:1, appeared to be characteristic components of the examined species. By applying chemometrics it was possible to show that the fatty acid composition closely reflects the classification of the species. Copyright © 2012 Elsevier B.V. All rights reserved.

MDAS: an integrated system for metabonomic data analysis.

PubMed

Liu, Juan; Li, Bo; Xiong, Jiang-Hui

2009-03-01

Metabonomics, the latest 'omics' research field, shows great promise as a tool in biomarker discovery, drug efficacy and toxicity analysis, disease diagnosis and prognosis. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system, e.g., the mechanism of diseases. Traditional methods employed in metabonomic data analysis use multivariate analysis methods developed independently in chemometrics research. Additionally, with the development of machine learning approaches, some methods such as SVMs also show promise for use in metabonomic data analysis. Aside from the application of general multivariate analysis and machine learning methods to this problem, there is also a need for an integrated tool customized for metabonomic data analysis which can be easily used by biologists to reveal interesting patterns in metabonomic data.In this paper, we present a novel software tool MDAS (Metabonomic Data Analysis System) for metabonomic data analysis which integrates traditional chemometrics methods and newly introduced machine learning approaches. MDAS contains a suite of functional models for metabonomic data analysis and optimizes the flow of data analysis. Several file formats can be accepted as input. The input data can be optionally preprocessed and can then be processed with operations such as feature analysis and dimensionality reduction. The data with reduced dimensionalities can be used for training or testing through machine learning models. The system supplies proper visualization for data preprocessing, feature analysis, and classification which can be a powerful function for users to extract knowledge from the data. MDAS is an integrated platform for metabonomic data analysis, which transforms a complex analysis procedure into a more formalized and simplified one. The software package can be obtained from the authors.

Modification of kaolinite surfaces through mechanochemical activation with quartz: A diffuse reflectance infrared fourier transform and chemometrics study.

PubMed

Carmody, Onuma; Frost, Ray L; Kristóf, János; Kokot, Serge; Kloprogge, J Theo; Makó, Eva

2006-12-01

Studies of kaolinite surfaces are of industrial importance. One useful method for studying the changes in kaolinite surface properties is to apply chemometric analyses to the kaolinite surface infrared spectra. A comparison is made between the mechanochemical activation of Kiralyhegy kaolinites with significant amounts of natural quartz and the mechanochemical activation of Zettlitz kaolinite with added quartz. Diffuse reflectance infrared Fourier transform (DRIFT) spectra were analyzed using principal component analysis (PCA) and multi-criteria decision making (MCDM) methods, the preference ranking organization method for enrichment evaluations (PROMETHEE) and geometrical analysis for interactive assistance (GAIA). The clear discrimination of the Kiralyhegy spectral objects on the two PC scores plots (400-800 and 800-2030 cm(-1)) indicated the dominance of quartz. Importantly, no ordering of any spectral objects appeared to be related to grinding time in the PC plots of these spectral regions. Thus, neither the kaolinite nor the quartz are systematically responsive to grinding time according to the spectral criteria investigated. The third spectral region (2600-3800 cm(-1), OH vibrations), showed apparent systematic ordering of the Kiralyhegy and, to a lesser extent, Zettlitz spectral objects with grinding time. This was attributed to the effect of the natural quartz on the delamination of kaolinite and the accompanying phenomena (i.e., formation of kaolinite spheres and water). The mechanochemical activation of kaolinite and quartz, through dry grinding, results in changes to the surface structure. Different grinding times were adopted to study the rate of destruction of the kaolinite and quartz structures. This relationship (i.e., grinding time) was classified using PROMETHEE and GAIA methodology.

Chemometrics-based Approach in Analysis of Arnicae flos

PubMed Central

Zheleva-Dimitrova, Dimitrina Zh.; Balabanova, Vessela; Gevrenova, Reneta; Doichinova, Irini; Vitkova, Antonina

2015-01-01

Introduction: Arnica montana flowers have a long history as herbal medicines for external use on injuries and rheumatic complaints. Objective: To investigate Arnicae flos of cultivated accessions from Bulgaria, Poland, Germany, Finland, and Pharmacy store for phenolic derivatives and sesquiterpene lactones (STLs). Materials and Methods: Samples of Arnica from nine origins were prepared by ultrasound-assisted extraction with 80% methanol for phenolic compounds analysis. Subsequent reverse-phase high-performance liquid chromatography (HPLC) separation of the analytes was performed using gradient elution and ultraviolet detection at 280 and 310 nm (phenolic acids), and 360 nm (flavonoids). Total STLs were determined in chloroform extracts by solid-phase extraction-HPLC at 225 nm. The HPLC generated chromatographic data were analyzed using principal component analysis (PCA) and hierarchical clustering (HC). Results: The highest total amount of phenolic acids was found in the sample from Botanical Garden at Joensuu University, Finland (2.36 mg/g dw). Astragalin, isoquercitrin, and isorhamnetin 3-glucoside were the main flavonol glycosides being present up to 3.37 mg/g (astragalin). Three well-defined clusters were distinguished by PCA and HC. Cluster C1 comprised of the German and Finnish accessions characterized by the highest content of flavonols. Cluster C2 included the Bulgarian and Polish samples presenting a low content of flavonoids. Cluster C3 consisted only of one sample from a pharmacy store. Conclusion: A validated HPLC method for simultaneous determination of phenolic acids, flavonoid glycosides, and aglycones in A. montana flowers was developed. The PCA loading plot showed that quercetin, kaempferol, and isorhamnetin can be used to distinguish different Arnica accessions. SUMMARY A principal component analysis (PCA) on 13 phenolic compounds and total amount of sesquiterpene lactones in Arnicae flos collection tended to cluster the studied 9 accessions into

Chemometric and trace element profiling methodologies for authenticating, crossmatching and constraining the provenance of illicit tobacco products.

PubMed

Stephens, William Edryd

2016-09-01

Illicit tobacco products have a disproportionately negative effect on public health. Counterfeits and cheap whites as well as legal brands smuggled from countries not adopting track and trace technologies will require novel forensic tools to aid the disruption of their supply chains. Data sets of trace element concentrations in tobacco were obtained using X-ray fluorescence spectrometry on samples of legal and illicit products mainly from Europe. Authentic and counterfeit products were discriminated by identifying outliers from data sets of legal products using Mahalanobis distance and graphical profiling methods. Identical and closely similar counterfeits were picked out using Euclidean distance, and counterfeit provenance was addressed using chemometric methods to identify geographical affinities. Taking Marlboro as an exemplar, the major brands are shown to be remarkably consistent in composition, in marked contrast to counterfeits bearing the same brand name. Analysis of 35 illicit products seized in the European Union (EU) indicates that 18 are indistinguishable or closely similar to Marlboro legally sold in the EU, while 17 are sufficiently different to be deemed counterfeit, among them being 2 counterfeits so closely similar that their tobaccos are likely to come from the same source. The tobacco in the large majority of counterfeits in this data set appears to originate in Asia. Multivariate and graphical analysis of trace elements in tobacco can effectively authenticate brands, crossmatch illicit products across jurisdictions and may identify their geographical sources. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Interaction between quinoline yellow and human serum albumin: Spectroscopic, chemometrics and molecular docking studies.

PubMed

Wang, Rui; Hu, Xing; Pan, Junhui; Gong, Deming; Zhang, Guowen

2018-05-23

Quinoline yellow (QY), a widely used synthetic colorant in food industry, has caused extensive concern due to its potential harm to human health. In the present work, the interaction between food colourant quinoline yellow (QY) and human serum albumin (HSA) was characterized by multispectroscopic methods, chemometrics algorithm and molecular modeling studies. The concentration profiles and the pure spectra for the components (QY, HSA and QY-HSA complex) obtained through analyzing the expanded UV-vis absorption data matrix by multivariate curve resolution-alternating least squares confirmed the QY-HSA interaction process. QY quenched the fluorescence of HSA due to the formation of QY-HSA complex and moderate affinity stabilized the complex. Hydrophobic forces and hydrogen bonding played major roles in the binding of QY to HSA. Site-specific marker-induced displacement results suggested that QY bound to the subdomain IIA of HSA which was corroborated by the molecular docking results. Decreases of HSA surface hydrophobicity and free sulfhydryl groups content indicated that QY caused a contraction of the peptide strand in HSA and hided the hydrophobic patches of the protein. The analysis of UV-vis absorption, circular dichroism and three-dimensional fluorescence spectroscopy found that QY led to the microenvironmental perturbations around the fluorophores and secondary structure changes of HSA. This work showed that QY could bind to HSA and affect the structural and functional properties of this protein, which provided new insights into the binding mechanism of QY with HSA and comprehensive understanding for the toxicity of QY in biological process. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Data Treatment for LC-MS Untargeted Analysis.

PubMed

Riccadonna, Samantha; Franceschi, Pietro

2018-01-01

Liquid chromatography-mass spectrometry (LC-MS) untargeted experiments require complex chemometrics strategies to extract information from the experimental data. Here we discuss "data preprocessing", the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.

Screening soy hydrolysates for the production of a recombinant therapeutic protein in commercial cell line by combined approach of near-infrared spectroscopy and chemometrics.

PubMed

Li, Guiyang; Wen, Zai-Qing

2013-03-01

Soy hydrolysates are widely used as the major nutrient sources for cell culture processes for industrial manufacturing of therapeutic recombinant proteins. The primary goal of this study was to develop a spectroscopy based chemometric method, a partial least squares (PLS), to screen soy hydrolysates for better yield of protein production (titers) in cell culture medium. Harvest titer values of 29 soy hydrolysate lots with production yield between 490 and 1,350 mg/L were obtained from shake flask models or from manufacture engineering runs. The soy hydrolysate samples were measured by near-infrared (NIR) in reflectance mode using an infrared fiber optic probe. The fiber optic probe could easily enable in situ measurement of the soy hydrolysates for convenient raw material screening. The best PLS calibration has a determination coefficient of R (2) = 0.887 utilizing no spectral preprocessing, the two spectral ranges of 10,000-5,376 cm(-1) and 4,980-4,484 cm(-1), and a rank of 6 factors. The cross-validation of the model resulted in a determination coefficient of R (2) = 0.741 between the predicted and actual titer values with an average standard deviation of 72 mg/L. Compared with the resource demanding shake flask model, the combination of NIR and chemometric modeling provides a convenient method for soy hydrolysate screening with the advantage of fast speed, low cost and non-destructive.

PG-Metrics: A chemometric-based approach for classifying bacterial peptidoglycan data sets and uncovering their subjacent chemical variability

PubMed Central

Kumar, Keshav; Espaillat, Akbar; Cava, Felipe

2017-01-01

Bacteria cells are protected from osmotic and environmental stresses by an exoskeleton-like polymeric structure called peptidoglycan (PG) or murein sacculus. This structure is fundamental for bacteria’s viability and thus, the mechanisms underlying cell wall assembly and how it is modulated serve as targets for many of our most successful antibiotics. Therefore, it is now more important than ever to understand the genetics and structural chemistry of the bacterial cell walls in order to find new and effective methods of blocking it for the treatment of disease. In the last decades, liquid chromatography and mass spectrometry have been demonstrated to provide the required resolution and sensitivity to characterize the fine chemical structure of PG. However, the large volume of data sets that can be produced by these instruments today are difficult to handle without a proper data analysis workflow. Here, we present PG-metrics, a chemometric based pipeline that allows fast and easy classification of bacteria according to their muropeptide chromatographic profiles and identification of the subjacent PG chemical variability between e.g. bacterial species, growth conditions and, mutant libraries. The pipeline is successfully validated here using PG samples from different bacterial species and mutants in cell wall proteins. The obtained results clearly demonstrated that PG-metrics pipeline is a valuable bioanalytical tool that can lead us to cell wall classification and biomarker discovery. PMID:29040278

Compositional variations for alpha-galactosides in different species of leguminosae, brassicaceae, and barley: a chemotaxonomic study based on chemometrics and high-performance capillary electrophoresis.

PubMed

Andersen, Keld Ejdrup; Bjergegaard, Charlotte; Møller, Peter; Sørensen, Jens Christian; Sørensen, Hilmer

2005-07-13

The contents of raffinose family oligosaccharides (RFO) and sucrose in Brassica, Lupinus, Pisum, and Hordeum species were investigated by chemometric principal component analysis (PCA). Hordeum samples contained sucrose and raffinose, and Brassica samples all contained sucrose, raffinose, and stachyose. In addition to these, the Pisum samples contained verbascose and the Lupinus samples also contained ajugose. High stachyose and low ajugose contents were found in Lupinus albus in contrast to Lupinus angustifolius, having low stachyose and high ajugose contents. Lupinus luteus had average stachyose and ajugose contents, whereas large amounts of verbascose were accumulated in these seeds. Lupinus mutabilis had high stachyose and low ajugose contents, similar to the composition in L. albus but showing higher raffinose content. The Brassica samples also showed compositional RFO variations within the species, and subgroup formations were discovered within the investigated Brassica napus varieties. PCA results indicated compositional variations between the investigated genera and within the various species of value as chemotaxonomic defined parameters and as tools in evaluations of authenticity/falsifications when RFO-containing plants are used as, for example, feed and food additives.

Quality evaluation of terpinen-4-ol-type Australian tea tree oils and commercial products: an integrated approach using conventional and chiral GC/MS combined with chemometrics.

PubMed

Wang, Mei; Zhao, Jianping; Avula, Bharathi; Wang, Yan-Hong; Chittiboyina, Amar G; Parcher, Jon F; Khan, Ikhlas A

2015-03-18

GC/MS, chiral GC/MS, and chemometric techniques were used to evaluate a large set (n=104) of tea tree oils (TTO) and commercial products purported to contain TTO. Twenty terpenoids were determined in each sample and compared with the standards specified by ISO-4730-2004. Several of the oil samples that were ISO compliant when distilled did not meet the ISO standards in this study primarily due to the presence of excessive p-cymene and/or depletion of terpinenes. Forty-nine percent of the commercial products did not meet the ISO specifications. Four terpenes, viz., α-pinene, limonene, terpinen-4-ol, and α-terpineol, present in TTOs with the (+)-isomer predominant were measured by chiral GC/MS. The results clearly indicated that 28 commercial products contained excessive (+)-isomer or contained the (+)-isomer in concentrations below the norm. Of the 28 outliers, 7 met the ISO standards. There was a substantial subset of commercial products that met ISO standards but displayed unusual enantiomeric+/-ratios. A class predictive model based on the oils that met ISO standards was constructed. The outliers identified by the class predictive model coincided with the samples that displayed an abnormal chiral ratio. Thus, chiral and chemometric analyses could be used to confirm the identification of abnormal commercial products including those that met all of the ISO standards.

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.

Time-Domain Nuclear Magnetic Resonance (TD-NMR) and Chemometrics for Determination of Fat Content in Commercial Products of Milk Powder.

PubMed

Nascimento, Paloma Andrade Martins; Barsanelli, Paulo Lopes; Rebellato, Ana Paula; Pallone, Juliana Azevedo Lima; Colnago, Luiz Alberto; Pereira, Fabíola Manhas Verbi

2017-03-01

This study shows the use of time-domain (TD)-NMR transverse relaxation (T2) data and chemometrics in the nondestructive determination of fat content for powdered food samples such as commercial dried milk products. Most proposed NMR spectroscopy methods for measuring fat content correlate free induction decay or echo intensities with the sample's mass. The need for the sample's mass limits the analytical frequency of NMR determination, because weighing the samples is an additional step in this procedure. Therefore, the method proposed here is based on a multivariate model of T2 decay, measured with Carr-Purcell-Meiboom-Gill pulse sequence and reference values of fat content. The TD-NMR spectroscopy method shows high correlation (r = 0.95) with the lipid content, determined by the standard extraction method of Bligh and Dyer. For comparison, fat content determination was also performed using a multivariate model with near-IR (NIR) spectroscopy, which is also a nondestructive method. The advantages of the proposed TD-NMR method are that it (1) minimizes toxic residue generation, (2) performs measurements with high analytical frequency (a few seconds per analysis), and (3) does not require sample preparation (such as pelleting, needed for NIR spectroscopy analyses) or weighing the samples.

Elimination of interference from water in KBr disk FT-IR spectra of solid biomaterials by chemometrics solved with kinetic modeling.

PubMed

Gordon, Sherald H; Harry-O'kuru, Rogers E; Mohamed, Abdellatif A

2017-11-01

Infrared analysis of proteins and polysaccharides by the well known KBr disk technique is notoriously frustrated and defeated by absorbed water interference in the important amide and hydroxyl regions of spectra. This interference has too often been overlooked or ignored even when the resulting distortion is critical or even fatal, as in quantitative analyses of protein secondary structure, because the water has been impossible to measure or eliminate. Therefore, a new chemometric method was devised that corrects spectra of materials in KBr disks by mathematically eliminating the water interference. A new concept termed the Beer-Lambert law absorbance ratio (R-matrix) model was augmented with water concentration ratios computed via an exponential decay kinetic model of the water absorption process in KBr, which rendered the otherwise indeterminate system of linear equations determinate and thus possible to solve in a formal analytic manner. Consequently, the heretofore baffling KBr water elimination problem is now solved once and for all. Using the new formal solution, efforts to eliminate water interference from KBr disks in research will be defeated no longer. Resulting spectra of protein were much more accurate than attenuated total reflection (ATR) spectra corrected using the well-accepted Advanced ATR Correction Algorithm. Published by Elsevier B.V.

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils.

PubMed

Jiménez-Sotelo, Paola; Hernández-Martínez, Maylet; Osorio-Revilla, Guillermo; Meza-Márquez, Ofelia Gabriela; García-Ochoa, Felipe; Gallardo-Velázquez, Tzayhrí

2016-07-01

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R(2) > 0.9961, standard errors of calibration (SEC) in the range of 0.3963-0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.

Simultaneous Determination of Octinoxate, Oxybenzone, and Octocrylene in a Sunscreen Formulation Using Validated Spectrophotometric and Chemometric Methods.

PubMed

Abdel-Ghany, Maha F; Abdel-Aziz, Omar; Ayad, Miriam F; Mikawy, Neven N

2015-01-01

Accurate, reliable, and sensitive spectrophotometric and chemometric methods were developed for simultaneous determination of octinoxate (OMC), oxybenzone (OXY), and octocrylene (OCR) in a sunscreen formulation without prior separation steps, including derivative ratio spectra zero crossing (DRSZ), double divisor ratio spectra derivative (DDRD), mean centering ratio spectra (MCR), and partial least squares (PLS-2). With the DRSZ technique, the UV filters could be determined in the ranges of 0.5-13.0, 0.3-9.0, and 0.5-9.0 μg/mL at 265.2, 246.6, and 261.8 nm, respectively. By utilizing the DDRD technique, UV filters could be determined in the above ranges at 237.8, 241.0, and 254.2 nm, respectively. With the MCR technique, the UV filters could be determined in the above ranges at 381.7, 383.2, and 355.6 nm, respectively. The PLS-2 technique successfully quantified the examined UV filters in the ranges of 0.5-9.3, 0.3-7.1, and 0.5-6.9 μg/mL, respectively. All the methods were validated according to the International Conference on Harmonization guidelines and successfully applied to determine the UV filters in pure form, laboratory-prepared mixtures, and a sunscreen formulation. The obtained results were statistically compared with reference and reported methods of analysis for OXY, OMC, and OCR, and there were no significant differences with respect to accuracy and precision of the adopted techniques.

Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics

PubMed Central

Trunfio, Nicholas; Lee, Haewoo; Starkey, Jason; Agarabi, Cyrus; Liu, Jay

2017-01-01

Two of the primary issues with characterizing the variability of raw materials used in mammalian cell culture, such as wheat hydrolysate, is that the analyses of these materials can be time consuming, and the results of the analyses are not straightforward to interpret. To solve these issues, spectroscopy can be combined with chemometrics to provide a quick, robust and easy to understand methodology for the characterization of raw materials; which will improve cell culture performance by providing an assessment of the impact that a given raw material will have on final product quality. In this study, four spectroscopic technologies: near infrared spectroscopy, middle infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy were used in conjunction with principal component analysis to characterize the variability of wheat hydrolysates, and to provide evidence that the classification of good and bad lots of raw material is possible. Then, the same spectroscopic platforms are combined with partial least squares regressions to quantitatively predict two cell culture critical quality attributes (CQA): integrated viable cell density and IgG titer. The results showed that near infrared (NIR) spectroscopy and fluorescence spectroscopy are capable of characterizing the wheat hydrolysate's chemical structure, with NIR performing slightly better; and that they can be used to estimate the raw materials’ impact on the CQAs. These results were justified by demonstrating that of all the components present in the wheat hydrolysates, six amino acids: arginine, glycine, phenylalanine, tyrosine, isoleucine and threonine; and five trace elements: copper, phosphorus, molybdenum, arsenic and aluminum, had a large, statistically significant effect on the CQAs, and that NIR and fluorescence spectroscopy performed the best for characterizing the important amino acids. It was also found that the trace elements of interest were not characterized well by any of the spectral

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

PubMed Central

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

2016-01-01

Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354

In-Depth Two-Year Study of Phenolic Profile Variability among Olive Oils from Autochthonous and Mediterranean Varieties in Morocco, as Revealed by a LC-MS Chemometric Profiling Approach.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Olmo-García, Lucía; Ajal, El Amine; Monasterio, Romina P; Hanine, Hafida; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2016-12-28

Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars ("Picholine Marocaine", "Dahbia", "Haouzia" and "Menara"), and nine Mediterranean varieties recently introduced in Morocco ("Arbequina", "Arbosana", "Cornicabra", "Frantoio", "Hojiblanca", "Koroneiki", "Manzanilla", "Picholine de Languedoc" and "Picual"), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils.

Confocal Raman imaging and chemometrics applied to solve forensic document examination involving crossed lines and obliteration cases by a depth profiling study.

PubMed

Borba, Flávia de Souza Lins; Jawhari, Tariq; Saldanha Honorato, Ricardo; de Juan, Anna

2017-03-27

This article describes a non-destructive analytical method developed to solve forensic document examination problems involving crossed lines and obliteration. Different strategies combining confocal Raman imaging and multivariate curve resolution-alternating least squares (MCR-ALS) are presented. Multilayer images were acquired at subsequent depth layers into the samples. It is the first time that MCR-ALS is applied to multilayer images for forensic purposes. In this context, this method provides a single set of pure spectral ink signatures and related distribution maps for all layers examined from the sole information in the raw measurement. Four cases were investigated, namely, two concerning crossed lines with different degrees of ink similarity and two related to obliteration, where previous or no knowledge about the identity of the obliterated ink was available. In the crossing line scenario, MCR-ALS analysis revealed the ink nature and the chronological order in which strokes were drawn. For obliteration cases, results making active use of information about the identity of the obliterated ink in the chemometric analysis were of similar quality as those where the identity of the obliterated ink was unknown. In all obliteration scenarios, the identity of inks and the obliterated text were satisfactorily recovered. The analytical methodology proposed is of general use for analytical forensic document examination problems, and considers different degrees of complexity and prior available information. Besides, the strategies of data analysis proposed can be applicable to any other kind of problem in which multilayer Raman images from multicomponent systems have to be interpreted.

Process monitored spectrophotometric titration coupled with chemometrics for simultaneous determination of mixtures of weak acids.

PubMed

Liao, Lifu; Yang, Jing; Yuan, Jintao

2007-05-15

A new spectrophotometric titration method coupled with chemometrics for the simultaneous determination of mixtures of weak acids has been developed. In this method, the titrant is a mixture of sodium hydroxide and an acid-base indicator, and the indicator is used to monitor the titration process. In a process of titration, both the added volume of titrant and the solution acidity at each titration point can be obtained simultaneously from an absorption spectrum by least square algorithm, and then the concentration of each component in the mixture can be obtained from the titration curves by principal component regression. The method only needs the information of absorbance spectra to obtain the analytical results, and is free of volumetric measurements. The analyses are independent of titration end point and do not need the accurate values of dissociation constants of the indicator and the acids. The method has been applied to the simultaneous determination of the mixtures of benzoic acid and salicylic acid, and the mixtures of phenol, o-chlorophenol and p-chlorophenol with satisfactory results.

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics.

PubMed

Qi, Luming; Liu, Honggao; Li, Jieqing; Li, Tao; Wang, Yuanzhong

2018-01-15

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 192 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms.

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics

PubMed Central

Qi, Luming; Liu, Honggao; Li, Jieqing; Li, Tao

2018-01-01

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 184 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms. PMID:29342969

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

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

Simultaneous determination of hydrocarbon renewable diesel, biodiesel and petroleum diesel contents in diesel fuel blends using near infrared (NIR) spectroscopy and chemometrics.

PubMed

Alves, Julio Cesar Laurentino; Poppi, Ronei Jesus

2013-11-07

Highly polluting fuels based on non-renewable resources such as fossil fuels need to be replaced with potentially less polluting renewable fuels derived from vegetable or animal biomass, these so-called biofuels, are a reality nowadays and many countries have started the challenge of increasing the use of different types of biofuels, such as ethanol and biodiesel (fatty acid alkyl esters), often mixed with petroleum derivatives, such as gasoline and diesel, respectively. The quantitative determination of these fuel blends using simple, fast and low cost methods based on near infrared (NIR) spectroscopy combined with chemometric methods has been reported. However, advanced biofuels based on a mixture of hydrocarbons or a single hydrocarbon molecule, such as farnesane (2,6,10-trimethyldodecane), a hydrocarbon renewable diesel, can also be used in mixtures with biodiesel and petroleum diesel fuel and the use of NIR spectroscopy for the quantitative determination of a ternary fuel blend of these two hydrocarbon-based fuels and biodiesel can be a useful tool for quality control. This work presents a development of an analytical method for the quantitative determination of hydrocarbon renewable diesel (farnesane), biodiesel and petroleum diesel fuel blends using NIR spectroscopy combined with chemometric methods, such as partial least squares (PLS) and support vector machines (SVM). This development leads to a more accurate, simpler, faster and cheaper method when compared to the standard reference method ASTM D6866 and with the main advantage of providing the individual quantification of two different biofuels in a mixture with petroleum diesel fuel. Using the developed PLS model the three fuel blend components were determined simultaneously with values of root mean square error of prediction (RMSEP) of 0.25%, 0.19% and 0.38% for hydrocarbon renewable diesel, biodiesel and petroleum diesel, respectively, the values obtained were in agreement with those suggested by

Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics.

PubMed

Ali, Hina; Saleem, Muhammad; Anser, Muhammad Ramzan; Khan, Saranjam; Ullah, Rahat; Bilal, Muhammad

2018-01-01

Due to high price and nutritional values of extra virgin olive oil (EVOO), it is vulnerable to adulteration internationally. Refined oil or other vegetable oils are commonly blended with EVOO and to unmask such fraud, quick, and reliable technique needs to be standardized and developed. Therefore, in this study, adulteration of edible oil (sunflower oil) is made with pure EVOO and analyzed using fluorescence spectroscopy (excitation wavelength at 350 nm) in conjunction with principal component analysis (PCA) and partial least squares (PLS) regression. Fluorescent spectra contain fingerprints of chlorophyll and carotenoids that are characteristics of EVOO and differentiated it from sunflower oil. A broad intense hump corresponding to conjugated hydroperoxides is seen in sunflower oil in the range of 441-489 nm with the maximum at 469 nm whereas pure EVOO has low intensity doublet peaks in this region at 441 nm and 469 nm. Visible changes in spectra are observed in adulterated EVOO by increasing the concentration of sunflower oil, with an increase in doublet peak and correspondingly decrease in chlorophyll peak intensity. Principal component analysis showed a distinct clustering of adulterated samples of different concentrations. Subsequently, the PLS regression model was best fitted over the complete data set on the basis of coefficient of determination (R 2 ), standard error of calibration (SEC), and standard error of prediction (SEP) of values 0.99, 0.617, and 0.623 respectively. In addition to adulterant, test samples and imported commercial brands of EVOO were also used for prediction and validation of the models. Fluorescence spectroscopy combined with chemometrics showed its robustness to identify and quantify the specified adulterant in pure EVOO.

Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics.

PubMed

Zhang, Guowen; Ni, Yongnian; Churchill, Jane; Kokot, Serge

2006-09-15

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.

Investigation of different spectrophotometric and chemometric methods for determination of entacapone, levodopa and carbidopa in ternary mixture

NASA Astrophysics Data System (ADS)

Abdel-Ghany, Maha F.; Hussein, Lobna A.; Ayad, Miriam F.; Youssef, Menatallah M.

2017-01-01

New, simple, accurate and sensitive UV spectrophotometric and chemometric methods have been developed and validated for determination of Entacapone (ENT), Levodopa (LD) and Carbidopa (CD) in ternary mixture. Method A is a derivative ratio spectra zero-crossing spectrophotometric method which allows the determination of ENT in the presence of both LD and CD by measuring the peak amplitude at 249.9 nm in the range of 1-20 μg mL- 1. Method B is a double divisor-first derivative of ratio spectra method, used for determination of ENT, LD and CD at 245, 239 and 293 nm, respectively. Method C is a mean centering of ratio spectra which allows their determination at 241, 241.6 and 257.1 nm, respectively. Methods B and C could successfully determine the studied drugs in concentration ranges of 1-20 μg mL- 1 for ENT and 10-90 μg mL- 1 for both LD and CD. Methods D and E are principal component regression and partial least-squares, respectively, used for the simultaneous determination of the studied drugs by using seventeen mixtures as calibration set and eight mixtures as validation set. The developed methods have the advantage of simultaneous determination of the cited components without any pre-treatment. All the results were statistically compared with the reported methods, where no significant difference was observed. The developed methods were satisfactorily applied to the analysis of the investigated drugs in their pure form and in pharmaceutical dosage forms.

A chemometric approach for characterization of serum transthyretin in familial amyloidotic polyneuropathy type I (FAP-I) by electrospray ionization-ion mobility mass spectrometry.

PubMed

Pont, Laura; Sanz-Nebot, Victoria; Vilaseca, Marta; Jaumot, Joaquim; Tauler, Roma; Benavente, Fernando

2018-05-01

In this study, we describe a chemometric data analysis approach to assist in the interpretation of the complex datasets from the analysis of high-molecular mass oligomeric proteins by ion mobility mass spectrometry (IM-MS). The homotetrameric protein transthyretin (TTR) is involved in familial amyloidotic polyneuropathy type I (FAP-I). FAP-I is associated with a specific TTR mutant variant (TTR(Met30)) that can be easily detected analyzing the monomeric forms of the mutant protein. However, the mechanism of protein misfolding and aggregation onset, which could be triggered by structural changes in the native tetrameric protein, remains under investigation. Serum TTR from healthy controls and FAP-I patients was purified under non-denaturing conditions by conventional immunoprecipitation in solution and analyzed by IM-MS. IM-MS allowed separation and characterization of several tetrameric, trimeric and dimeric TTR gas ions due to their differential drift time. After an appropriate data pre-processing, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the complex datasets. A group of seven independent components being characterized by their ion mobility profiles and mass spectra were resolved to explain the observed data variance in control and patient samples. Then, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were considered for exploration and classification. Only four out of the seven resolved components were enough for an accurate differentiation. Furthermore, the specific TTR ions identified in the mass spectra of these components and the resolved ion mobility profiles provided a straightforward insight into the most relevant oligomeric TTR proteoforms for the disease. Copyright © 2018 Elsevier B.V. All rights reserved.

Raman spectroscopy and chemometrics for identification and strain discrimination of the wine spoilage yeasts Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Brettanomyces bruxellensis.

PubMed

Rodriguez, Susan B; Thornton, Mark A; Thornton, Roy J

2013-10-01

The yeasts Zygosaccharomyces bailii, Dekkera bruxellensis (anamorph, Brettanomyces bruxellensis), and Saccharomyces cerevisiae are the major spoilage agents of finished wine. A novel method using Raman spectroscopy in combination with a chemometric classification tool has been developed for the identification of these yeast species and for strain discrimination of these yeasts. Raman spectra were collected for six strains of each of the yeasts Z. bailii, B. bruxellensis, and S. cerevisiae. The yeasts were classified with high sensitivity at the species level: 93.8% for Z. bailii, 92.3% for B. bruxellensis, and 98.6% for S. cerevisiae. Furthermore, we have demonstrated that it is possible to discriminate between strains of these species. These yeasts were classified at the strain level with an overall accuracy of 81.8%.

Cartilage analysis by reflection spectroscopy

NASA Astrophysics Data System (ADS)

Laun, T.; Muenzer, M.; Wenzel, U.; Princz, S.; Hessling, M.

2015-07-01

A cartilage bioreactor with analytical functions for cartilage quality monitoring is being developed. For determining cartilage composition, reflection spectroscopy in the visible (VIS) and near infrared (NIR) spectral region is evaluated. Main goal is the determination of the most abundant cartilage compounds water, collagen I and collagen II. Therefore VIS and NIR reflection spectra of different cartilage samples of cow, pig and lamb are recorded. Due to missing analytical instrumentation for identifying the cartilage composition of these samples, typical literature concentration values are used for the development of chemometric models. In spite of these limitations the chemometric models provide good cross correlation results for the prediction of collagen I and II and water concentration based on the visible and the NIR reflection spectra.

Application of inorganic element ratios to chemometrics for determination of the geographic origin of welsh onions.

PubMed

Ariyama, Kaoru; Horita, Hiroshi; Yasui, Akemi

2004-09-22

The composition of concentration ratios of 19 inorganic elements to Mg (hereinafter referred to as 19-element/Mg composition) was applied to chemometric techniques to determine the geographic origin (Japan or China) of Welsh onions (Allium fistulosum L.). Using a composition of element ratios has the advantage of simplified sample preparation, and it was possible to determine the geographic origin of a Welsh onion within 2 days. The classical technique based on 20 element concentrations was also used along with the new simpler one based on 19 elements/Mg in order to validate the new technique. Twenty elements, Na, P, K, Ca, Mg, Mn, Fe, Cu, Zn, Sr, Ba, Co, Ni, Rb, Mo, Cd, Cs, La, Ce, and Tl, in 244 Welsh onion samples were analyzed by flame atomic absorption spectroscopy, inductively coupled plasma atomic emission spectrometry, and inductively coupled plasma mass spectrometry. Linear discriminant analysis (LDA) on 20-element concentrations and 19-element/Mg composition was applied to these analytical data, and soft independent modeling of class analogy (SIMCA) on 19-element/Mg composition was applied to these analytical data. The results showed that techniques based on 19-element/Mg composition were effective. LDA, based on 19-element/Mg composition for classification of samples from Japan and from Shandong, Shanghai, and Fujian in China, classified 101 samples used for modeling 97% correctly and predicted another 119 samples excluding 24 nonauthentic samples 93% correctly. In discriminations by 10 times of SIMCA based on 19-element/Mg composition modeled using 101 samples, 220 samples from known production areas including samples used for modeling and excluding 24 nonauthentic samples were predicted 92% correctly.

Multivariate Analysis of Combined Fourier Transform Near-Infrared Spectrometry (FT-NIR) and Raman Datasets for Improved Discrimination of Drying Oils.

PubMed

Carlesi, Serena; Ricci, Marilena; Cucci, Costanza; La Nasa, Jacopo; Lofrumento, Cristiana; Picollo, Marcello; Becucci, Maurizio

2015-07-01

This work explores the application of chemometric techniques to the analysis of lipidic paint binders (i.e., drying oils) by means of Raman and near-infrared spectroscopy. These binders have been widely used by artists throughout history, both individually and in mixtures. We prepared various model samples of the pure binders (linseed, poppy seed, and walnut oils) obtained from different manufacturers. These model samples were left to dry and then characterized by Raman and reflectance near-infrared spectroscopy. Multivariate analysis was performed by applying principal component analysis (PCA) on the first derivative of the corresponding Raman spectra (1800-750 cm(-1)), near-infrared spectra (6000-3900 cm(-1)), and their combination to test whether spectral differences could enable samples to be distinguished on the basis of their composition. The vibrational bands we found most useful to discriminate between the different products we studied are the fundamental ν(C=C) stretching and methylenic stretching and bending combination bands. The results of the multivariate analysis demonstrated the potential of chemometric approaches for characterizing and identifying drying oils, and also for gaining a deeper insight into the aging process. Comparison with high-performance liquid chromatography data was conducted to check the PCA results.

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.

Non-destructive Measurement of Total Carotenoid Content in Processed Tomato Products: Infrared Lock-In Thermography, Near-Infrared Spectroscopy/Chemometrics, and Condensed Phase Laser-Based Photoacoustics—Pilot Study

NASA Astrophysics Data System (ADS)

Bicanic, D.; Streza, M.; Dóka, O.; Valinger, D.; Luterotti, S.; Ajtony, Zs.; Kurtanjek, Z.; Dadarlat, D.

2015-09-01

Carotenes found in a diversity of fruits and vegetables are among important natural antioxidants. In a study described in this paper, the total carotenoid content (TCC) in seven different products derived from thermally processed tomatoes was determined using laser photoacoustic spectroscopy (LPAS), infrared lock-in thermography (IRLIT), and near-infrared spectroscopy (NIRS) combined with chemometrics. Results were verified versus data obtained by traditional VIS spectrophotometry (SP) that served as a reference technique. Unlike SP, the IRLIT, NIRS, and LPAS require a minimum of sample preparation which enables practically direct quantification of the TCC.

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.

Convolutional neural networks for vibrational spectroscopic data analysis.

PubMed

Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

2017-02-15

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemometrics-assisted simultaneous determination of cobalt(II) and chromium(III) with flow-injection chemiluminescence method

NASA Astrophysics Data System (ADS)

Li, Baoxin; Wang, Dongmei; Lv, Jiagen; Zhang, Zhujun

2006-09-01

In this paper, a flow-injection chemiluminescence (CL) system is proposed for simultaneous determination of Co(II) and Cr(III) with partial least squares calibration. This method is based on the fact that both Co(II) and Cr(III) catalyze the luminol-H 2O 2 CL reaction, and that their catalytic activities are significantly different on the same reaction condition. The CL intensity of Co(II) and Cr(III) was measured and recorded at different pH of reaction medium, and the obtained data were processed by the chemometric approach of partial least squares. The experimental calibration set was composed with nine sample solutions using orthogonal calibration design for two component mixtures. The calibration curve was linear over the concentration range of 2 × 10 -7 to 8 × 10 -10 and 2 × 10 -6 to 4 × 10 -9 g/ml for Co(II) and Cr(III), respectively. The proposed method offers the potential advantages of high sensitivity, simplicity and rapidity for Co(II) and Cr(III) determination, and was successfully applied to the simultaneous determination of both analytes in real water sample.

Chemometric optimization of the robustness of the near infrared spectroscopic method in wheat quality control.

PubMed

Pojić, Milica; Rakić, Dušan; Lazić, Zivorad

2015-01-01

A chemometric approach was applied for the optimization of the robustness of the NIRS method for wheat quality control. Due to the high number of experimental (n=6) and response variables to be studied (n=7) the optimization experiment was divided into two stages: screening stage in order to evaluate which of the considered variables were significant, and optimization stage to optimize the identified factors in the previously selected experimental domain. The significant variables were identified by using fractional factorial experimental design, whilst Box-Wilson rotatable central composite design (CCRD) was run to obtain the optimal values for the significant variables. The measured responses included: moisture, protein and wet gluten content, Zeleny sedimentation value and deformation energy. In order to achieve the minimal variation in responses, the optimal factor settings were found by minimizing the propagation of error (POE). The simultaneous optimization of factors was conducted by desirability function. The highest desirability of 87.63% was accomplished by setting up experimental conditions as follows: 19.9°C for sample temperature, 19.3°C for ambient temperature and 240V for instrument voltage. Copyright © 2014 Elsevier B.V. All rights reserved.

The in vitro Antimicrobial Activity and Chemometric Modelling of 59 Commercial Essential Oils against Pathogens of Dermatological Relevance.

PubMed

Orchard, Ané; Sandasi, Maxleene; Kamatou, Guy; Viljoen, Alvaro; van Vuuren, Sandy

2017-01-01

This study reports on the inhibitory concentration of 59 commercial essential oils recommended for dermatological conditions, and identifies putative compounds responsible for antimicrobial activity. Essential oils were investigated for antimicrobial activity using minimum inhibitory concentration assays. Ten essential oils were identified as having superior antimicrobial activity. The essential oil compositions were determined using gas chromatography coupled to mass spectrometry and the data analysed with the antimicrobial activity using multivariate tools. Orthogonal projections to latent structures models were created for seven of the pathogens. Eugenol was identified as the main biomarker responsible for antimicrobial activity in the majority of the essential oils. The essential oils mostly displayed noteworthy antimicrobial activity, with five oils displaying broad-spectrum activity against the 13 tested micro-organisms. The antimicrobial efficacies of the essential oils highlight their potential in treating dermatological infections and through chemometric modelling, bioactive volatiles have been identified. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

[Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

PubMed

Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

2015-11-01

A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

In-Depth Two-Year Study of Phenolic Profile Variability among Olive Oils from Autochthonous and Mediterranean Varieties in Morocco, as Revealed by a LC-MS Chemometric Profiling Approach

PubMed Central

Bajoub, Aadil; Medina-Rodríguez, Santiago; Olmo-García, Lucía; Ajal, El Amine; Monasterio, Romina P.; Hanine, Hafida; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2016-01-01

Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars (“Picholine Marocaine”, “Dahbia”, “Haouzia” and “Menara”), and nine Mediterranean varieties recently introduced in Morocco (“Arbequina”, “Arbosana”, “Cornicabra”, “Frantoio”, “Hojiblanca”, “Koroneiki”, “Manzanilla”, “Picholine de Languedoc” and “Picual”), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils. PMID:28036024

Raman Spectroscopy and Chemometrics for Identification and Strain Discrimination of the Wine Spoilage Yeasts Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Brettanomyces bruxellensis

PubMed Central

Thornton, Mark A.; Thornton, Roy J.

2013-01-01

The yeasts Zygosaccharomyces bailii, Dekkera bruxellensis (anamorph, Brettanomyces bruxellensis), and Saccharomyces cerevisiae are the major spoilage agents of finished wine. A novel method using Raman spectroscopy in combination with a chemometric classification tool has been developed for the identification of these yeast species and for strain discrimination of these yeasts. Raman spectra were collected for six strains of each of the yeasts Z. bailii, B. bruxellensis, and S. cerevisiae. The yeasts were classified with high sensitivity at the species level: 93.8% for Z. bailii, 92.3% for B. bruxellensis, and 98.6% for S. cerevisiae. Furthermore, we have demonstrated that it is possible to discriminate between strains of these species. These yeasts were classified at the strain level with an overall accuracy of 81.8%. PMID:23913433

Relationships between volatile compounds and sensory characteristics in virgin olive oil by analytical and chemometric approaches.

PubMed

Procida, Giuseppe; Cichelli, Angelo; Lagazio, Corrado; Conte, Lanfranco S

2016-01-15

The volatile fraction of virgin olive oil is characterised by low molecular weight compounds that vaporise at room temperature. In order to obtain an aroma profile similar to natural olfactory perception, the composition of the volatile compounds was determined by applying dynamic headspace gas chromatography, performed at room temperature, with a cryogenic trap directly connected to a gas chromatograph-mass spectrometer system. Samples were also evaluated according to European Union and International Olive Council official methods for sensory evaluation. In this paper, the composition of the volatile fraction of 25 extra virgin olive oils from different regions of Italy was analysed and some preliminary considerations on relationships between chemical composition of volatile fraction and sensory characteristics are reported. Forty-two compounds were identified by means of the particular analytical technique used. All the analysed samples, classified as extra virgin by the panel test, never present peaks whose magnitude is important enough in defected oils. The study was focused on the evaluation of volatile compounds responsible for the positive impact on olive odour properties ('green-fruity' and 'sweet') and olfactory perception. Chemometric evaluation of data, obtained through headspace analysis and the panel test evaluation, showed a correlation between chemical compounds and sensory properties. On the basis of the results, the positive attributes of virgin olive oil are divided into two separated groups: sweet types or green types. Sixteen volatile compounds with known positive impact on odour properties were extracted and identified. In particular, eight compounds seem correlated with sweet properties whereas the green sensation appears to be correlated with eight other different substances. The content of the compounds at six carbon atoms proves to be very important in defining positive attributes of extra virgin olive oils and sensory evaluation. © 2015

UV-Vis spectroscopy combined with chemometric study on the interactions of three dietary flavonoids with copper ions.

PubMed

Zhang, Liangliang; Liu, Yuchen; Wang, Yongmei; Xu, Man; Hu, Xinyu

2018-10-15

The complex formation between a copper ion and the dietary flavonoid quercetin (QU) and its two glycosides hyperin (HY) and rutin (RU) was studied by the combined use of spectroscopic measurement and the chemometric method. The spectral changes of pH titration revealed two successively formed deprotonated species of QU: the first formed species was proposed to be the 3-hydroxyl group deprotonated QU, and the second was the quinone form QU, which was formed by oxidation after the hydroxyl groups in the B-ring were deprotonated at high pH values. Similar results were obtained for HY and RU with two deprotonated species forming at high pH values. UV/visible spectroscopy showed successive formation of CuL 2 and CuL species of QU at pH 6.0, while only Cu 2 L was formed for HY and RU at this pH. Glycoside moieties in the C-ring of flavonoids decrease the conditional associated constants between flavonoids and Cu 2+ . Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of spreadable cheese by Raman spectroscopy and chemometric tools.

PubMed

Oliveira, Kamila de Sá; Callegaro, Layce de Souza; Stephani, Rodrigo; Almeida, Mariana Ramos; de Oliveira, Luiz Fernando Cappa

2016-03-01

In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thermodynamics study of the dimerization equilibria of rhodamine B and 6G in different ionic strengths by photometric titration and chemometrics method

NASA Astrophysics Data System (ADS)

Ghasemi, Jahanbakhsh; Niazi, Ali; Kubista, Mikael

2005-11-01

The dimerization constants of rhodamine B and 6G have been determined by studying the dependence of their absorption spectra on the temperature in the range 20-80 °C at different total concentrations of rhodamine B (5.89 × 10 -6 to 2.36 × 10 -4 M) and rhodamine 6G (2.34 × 10 -5 to 5.89 × 10 -4 M) and in different concentrations of LiCl, NaCl and KCl salts as supporting electrolytes. The monomer-dimer equilibrium of rhodamine B and 6G have been determined by chemometrics refinement of the absorption spectra obtained by thermometric titrations performed at different ionic strengths. The quantitative analysis of the data of undefined mixtures, was carried out by simultaneous resolution of the overlapping spectral bands in the whole set of absorption spectra. The dimerization constants are varied by changing the ionic strength and the degree of dimerization are decreased by increasing of the ionic strength of the medium. The enthalpy and entropy of the dimerization reactions were determined from the dependence of the equilibrium constants on the temperature (van't Hoff equation). From the thermodynamic results the TΔ S°-Δ H° plot was sketched. It shows a fairly good positive correlation which indicates the enthalpy-entropy compensation in the dimerization reactions (compensation effect).

Thermodynamics study of the dimerization equilibria of rhodamine B and 6G in different ionic strengths by photometric titration and chemometrics method.

PubMed

Ghasemi, Jahanbakhsh; Niazi, Ali; Kubista, Mikael

2005-11-01

The dimerization constants of rhodamine B and 6G have been determined by studying the dependence of their absorption spectra on the temperature in the range 20-80 degrees C at different total concentrations of rhodamine B (5.89 x 10(-6) to 2.36 x 10(-4)M) and rhodamine 6G (2.34 x 10(-5) to 5.89 x 10(-4)M) and in different concentrations of LiCl, NaCl and KCl salts as supporting electrolytes. The monomer-dimer equilibrium of rhodamine B and 6G have been determined by chemometrics refinement of the absorption spectra obtained by thermometric titrations performed at different ionic strengths. The quantitative analysis of the data of undefined mixtures, was carried out by simultaneous resolution of the overlapping spectral bands in the whole set of absorption spectra. The dimerization constants are varied by changing the ionic strength and the degree of dimerization are decreased by increasing of the ionic strength of the medium. The enthalpy and entropy of the dimerization reactions were determined from the dependence of the equilibrium constants on the temperature (van't Hoff equation). From the thermodynamic results the TDeltaS degrees -DeltaH degrees plot was sketched. It shows a fairly good positive correlation which indicates the enthalpy-entropy compensation in the dimerization reactions (compensation effect).

Enhancing prediction power of chemometric models through manipulation of the fed spectrophotometric data: A comparative study

NASA Astrophysics Data System (ADS)

Saad, Ahmed S.; Hamdy, Abdallah M.; Salama, Fathy M.; Abdelkawy, Mohamed

2016-10-01

Effect of data manipulation in preprocessing step proceeding construction of chemometric models was assessed. The same set of UV spectral data was used for construction of PLS and PCR models directly and after mathematically manipulation as per well known first and second derivatives of the absorption spectra, ratio spectra and first and second derivatives of the ratio spectra spectrophotometric methods, meanwhile the optimal working wavelength ranges were carefully selected for each model and the models were constructed. Unexpectedly, number of latent variables used for models' construction varied among the different methods. The prediction power of the different models was compared using a validation set of 8 mixtures prepared as per the multilevel multifactor design and results were statistically compared using two-way ANOVA test. Root mean squares error of prediction (RMSEP) was used for further comparison of the predictability among different constructed models. Although no significant difference was found between results obtained using Partial Least Squares (PLS) and Principal Component Regression (PCR) models, however, discrepancies among results was found to be attributed to the variation in the discrimination power of adopted spectrophotometric methods on spectral data.

Development of a HS-SPME-GC/MS protocol assisted by chemometric tools to study herbivore-induced volatiles in Myrcia splendens.

PubMed

Souza Silva, Érica A; Saboia, Giovanni; Jorge, Nina C; Hoffmann, Camila; Dos Santos Isaias, Rosy Mary; Soares, Geraldo L G; Zini, Claudia A

2017-12-01

A headspace solid phase microextraction (HS-SPME) method combined with gas chromatography-mass spectrometry (GC/MS) was developed and optimized for extraction and analysis of volatile organic compounds (VOC) of leaves and galls of Myrcia splendens. Through a process of optimization of main factors affecting HS-SPME efficiency, the coating divivnilbenzene-carboxen-polydimethylsiloxane (DVB/Car/PDMS) was chosen as the optimum extraction phase, not only in terms of extraction efficiency, but also for its broader analyte coverage. Optimum extraction temperature was 30°C, while an extraction time of 15min provided the best compromise between extraction efficiencies of lower and higher molecular weight compounds. The optimized protocol was demonstrated to be capable of sampling plant material with high reproducibility, considering that most classes of analytes met the 20% RSD FDA criterion. The optimized method was employed for the analysis of three classes of M. splendens samples, generating a final list of 65 tentatively identified VOC, including alcohols, aldehydes, esters, ketones, phenol derivatives, as well as mono and sesquiterpenes. Significant differences were evident amongst the volatile profiles obtained from non-galled leaves (NGL) and leaf-folding galls (LFG) of M. splendens. Several differences pertaining to amounts of alcohols and aldehydes were detected between samples, particularly regarding quantities of green leaf volatiles (GLV). Alcohols represented about 14% of compounds detected in gall samples, whereas in non-galled samples, alcohol content was below 5%. Phenolic derived compounds were virtually absent in reference samples, while in non-galled leaves and galls their content ranged around 0.2% and 0.4%, respectively. Likewise, methyl salicylate, a well-known signal of plant distress, amounted for 1.2% of the sample content of galls, whereas it was only present in trace levels in reference samples. Chemometric analysis based on Heatmap associated

Essential Oil Variation from Twenty Two Genotypes of Citrus in Brazil-Chemometric Approach and Repellency Against Diaphorina citri Kuwayama.

PubMed

Andrade, Moacir Dos Santos; Ribeiro, Leandro do Prado; Borgoni, Paulo Cesar; Silva, Maria Fátima das Graças Fernandes da; Forim, Moacir Rossi; Fernandes, João Batista; Vieira, Paulo Cezar; Vendramin, José Djair; Machado, Marcos Antônio

2016-06-22

The chemical composition of volatile oils from 22 genotypes of Citrus and related genera was poorly differentiated, but chemometric techniques have clarified the relationships between the 22 genotypes, and allowed us to understand their resistance to D. citri. The most convincing similarities include the synthesis of (Z)-β-ocimene and (E)-caryophyllene for all 11 genotypes of group A. Genotypes of group B are not uniformly characterized by essential oil compounds. When stimulated with odor sources of 22 genotypes in a Y-tube olfactometer D. citri preferentially entered the arm containing the volatile oils of Murraya paniculata, confirming orange jasmine as its best host. C. reticulata × C. sinensis was the least preferred genotype, and is characterized by the presence of phytol, (Z)-β-ocimene, and β-elemene, which were not found in the most preferred genotype. We speculate that these three compounds may act as a repellent, making these oils less attractive to D. citri.

Detection of E. coli O157:H7 from ground beef using Fourier transform infrared (FT-IR) spectroscopy and chemometrics.

PubMed

Davis, Reeta; Irudayaraj, Joseph; Reuhs, Bradley L; Mauer, Lisa J

2010-08-01

FT-IR spectroscopy methods for detection, differentiation, and quantification of E. coli O157:H7 strains separated from ground beef were developed. Filtration and immunomagnetic separation (IMS) were used to extract live and dead E. coli O157:H7 cells from contaminated ground beef prior to spectral acquisition. Spectra were analyzed using chemometric techniques in OPUS, TQ Analyst, and WinDAS software programs. Standard plate counts were used for development and validation of spectral analyses. The detection limit based on a selectivity value using the OPUS ident test was 10(5) CFU/g for both Filtration-FT-IR and IMS-FT-IR methods. Experiments using ground beef inoculated with fewer cells (10(1) to 10(2) CFU/g) reached the detection limit at 6 h incubation. Partial least squares (PLS) models with cross validation were used to establish relationships between plate counts and FT-IR spectra. Better PLS predictions were obtained for quantifying live E. coli O157:H7 strains (R(2)> or = 0.9955, RMSEE < or = 0.17, RPD > or = 14) and different ratios of live and dead E. coli O157:H7 cells (R(2)= 0.9945, RMSEE = 2.75, RPD = 13.43) from ground beef using Filtration-FT-IR than IMS-FT-IR methods. Discriminant analysis and canonical variate analysis (CVA) of the spectra differentiated various strains of E. coli O157:H7 from an apathogenic control strain. CVA also separated spectra of 100% dead cells separated from ground beef from spectra of 0.5% live cells in the presence of 99.5% dead cells of E. coli O157:H7. These combined separation and FT-IR methods could be useful for rapid detection and differentiation of pathogens in complex foods.

Synergistic Hepatoprotective and Antioxidant Effect of Artichoke, Fig, Blackberry Herbal Mixture on HepG2 Cells and Their Metabolic Profiling Using NMR Coupled with Chemometrics.

PubMed

Youssef, Fadia S; Labib, Rola M; Eldahshan, Omayma A; Singab, Abdel Nasser B

2017-12-01

The edible plants have long been reported to possess a lot of biological activities. Herein, the hepatoprotective and the antioxidant activities of the aqueous infusion of the edible parts of Cynara cardunculus, Ficus carica, and Morus nigra and their herbal mixture (CFM) was investigated in vitro using CCl 4 induced damage in HepG2 cells. The highest amelioration was observed via the consumption of CFM at 1 mg/ml showing 47.00% and 37.09% decline in aspartate transaminase and alanine transaminase and 77.32% and 101.02% increase in reduced glutathione and superoxide dismutase comparable to CCl 4 treated cells. Metabolic profiling of their aqueous infusions was done using nuclear magnetic resonance spectroscopic experiments coupled with chemometrics particularly hierarchical cluster analysis (HCA) and principal component analysis (PCA). The structural closeness of the various metabolites existing in black berry and the mixture as reflected in the PCA score plot and HCA processed from the 1 H-NMR spectral data could eventually explained the close values in their biological behavior. For fig and artichoke, the existence of different phenolic metabolites that act synergistically could greatly interpret their potent biological behavior. Thus, it can be concluded that a herbal mixture composed of black berry, artichoke, and fig could afford an excellent natural candidate to combat oxidative stress and counteract hepatic toxins owing to its phenolic compounds. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Diffuse Reflectance Spectroscopy for Total Carbon Analysis of Hawaiian Soils

NASA Astrophysics Data System (ADS)

McDowell, M. L.; Bruland, G. L.; Deenik, J. L.; Grunwald, S.; Uchida, R.

2010-12-01

Accurate assessment of total carbon (Ct) content is important for fertility and nutrient management of soils, as well as for carbon sequestration studies. The non-destructive analysis of soils by diffuse reflectance spectroscopy (DRS) is a potential supplement or alternative to the traditional time-consuming and costly combustion method of Ct analysis, especially in spatial or temporal studies where sample numbers are large. We investigate the use of the visible to near-infrared (VNIR) and mid-infrared (MIR) spectra of soils coupled with chemometric analysis to determine their Ct content. Our specific focus is on Hawaiian soils of agricultural importance. Though this technique has been introduced to the soil community, it has yet to be fully tested and used in practical applications for all soil types, and this is especially true for Hawaii. In short, DRS characterizes and differentiates materials based on the variation of the light reflected by a material at certain wavelengths. This spectrum is dependent on the material’s composition, structure, and physical state. Multivariate chemometric analysis unravels the information in a set of spectra that can help predict a property such as Ct. This study benefits from the remarkably diverse soils of Hawaii. Our sample set includes 216 soil samples from 145 pedons from the main Hawaiian Islands archived at the National Soil Survey Center in Lincoln, NE, along with more than 50 newly-collected samples from Kauai, Oahu, Molokai, and Maui. In total, over 90 series from 10 of the 12 soil orders are represented. The Ct values of these samples range from < 1% - 55%. We anticipate that the diverse nature of our sample set will ensure a model with applicability to a wide variety of soils, both in Hawaii and globally. We have measured the VNIR and MIR spectra of these samples and obtained their Ct values by dry combustion. Our initial analyses are conducted using only samples obtained from the Lincoln archive. In this

Fluorescence Spectroscopy for the Monitoring of Food Processes.

PubMed

Ahmad, Muhammad Haseeb; Sahar, Amna; Hitzmann, Bernd

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.

Chemometric evaluation of Cd, Co, Cr, Cu, Ni (inductively coupled plasma optical emission spectrometry) and Pb (graphite furnace atomic absorption spectrometry) concentrations in lipstick samples intended to be used by adults and children.

PubMed

Batista, Érica Ferreira; Augusto, Amanda dos Santos; Pereira-Filho, Edenir Rodrigues

2016-04-01

A method was developed for determining the concentrations of Cd, Co, Cr, Cu, Ni and Pb in lipstick samples intended to be used by adults and children using inductively coupled plasma optical emission spectrometry (ICP OES) and graphite furnace atomic absorption spectrometry (GF AAS) after treatment with dilute HNO3 and hot block. The combination of fractional factorial design and Desirability function was used to evaluate the ICP OES operational parameters and the regression models using Central Composite and Doehlert designs were calculated to stablish the best working condition for all analytes. Seventeen lipstick samples manufactured in different countries with different colors and brands were analyzed. Some samples contained high concentrations of toxic elements, such as Cr and Pb, which are carcinogenic and cause allergic and eczematous dermatitis. The maximum concentration detected was higher than the permissible safe limits for human use, and the samples containing these high metal concentrations were intended for use by children. Principal component analysis (PCA) was used as a chemometrics tool for exploratory analysis to observe the similarities between samples relative to the metal concentrations (a correlation between Cd and Pb was observed). Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate Analysis To Quantify Species in the Presence of Direct Interferents: Micro-Raman Analysis of HNO 3 in Microfluidic Devices

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

Lines, Amanda M.; Nelson, Gilbert L.; Casella, Amanda J.

Microfluidic devices are a growing field with significant potential for application to small scale processing of solutions. Much like large scale processing, fast, reliable, and cost effective means of monitoring the streams during processing are needed. Here we apply a novel Micro-Raman probe to the on-line monitoring of streams within a microfluidic device. For either macro or micro scale process monitoring via spectroscopic response, there is the danger of interfering or confounded bands obfuscating results. By utilizing chemometric analysis, a form of multivariate analysis, species can be accurately quantified in solution despite the presence of overlapping or confounded spectroscopic bands.more » This is demonstrated on solutions of HNO 3 and NaNO 3 within micro-flow and microfluidic devices.« less

Total ion chromatographic fingerprints combined with chemometrics and mass defect filter to predict antitumor components of Picrasma quassioids.

PubMed

Shi, Yuanyuan; Zhan, Hao; Zhong, Liuyi; Yan, Fangrong; Feng, Feng; Liu, Wenyuan; Xie, Ning

2016-07-01

A method of total ion chromatogram combined with chemometrics and mass defect filter was established for the prediction of active ingredients in Picrasma quassioides samples. The total ion chromatogram data of 28 batches were pretreated with wavelet transformation and correlation optimized warping to correct baseline drifts and retention time shifts. Then partial least squares regression was applied to construct a regression model to bridge the total ion chromatogram fingerprints and the antitumor activity of P. quassioides. Finally, the regression coefficients were used to predict the active peaks in total ion chromatogram fingerprints. In this strategy, mass defect filter was employed to classify and characterize the active peaks from a chemical point of view. A total of 17 constituents were predicted as the potential active compounds, 16 of which were identified as alkaloids by this developed approach. The results showed that the established method was not only simple and easy to operate, but also suitable to predict ultraviolet undetectable compounds and provide chemical information for the prediction of active compounds in herbs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

[Gaussian process regression and its application in near-infrared spectroscopy analysis].

PubMed

Feng, Ai-Ming; Fang, Li-Min; Lin, Min

2011-06-01

Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.

Rapid measurement of methyl cellulose precipitable tannins using ultraviolet spectroscopy with chemometrics: application to red wine and inter-laboratory calibration transfer.

PubMed

Dambergs, Robert G; Mercurio, Meagan D; Kassara, Stella; Cozzolino, Daniel; Smith, Paul A

2012-06-01

Information relating to tannin concentration in grapes and wine is not currently available simply and rapidly enough to inform decision-making by grape growers, winemakers, and wine researchers. Spectroscopy and chemometrics have been implemented for the analysis of critical grape and wine parameters and offer a possible solution for rapid tannin analysis. We report here the development and validation of an ultraviolet (UV) spectral calibration for the prediction of tannin concentration in red wines. Such spectral calibrations reduce the time and resource requirements involved in measuring tannins. A diverse calibration set (n = 204) was prepared with samples of Australian wines of five varieties (Cabernet Sauvignon, Shiraz, Merlot, Pinot Noir, and Durif), from regions spanning the wine grape growing areas of Australia, with varying climate and soils, and with vintages ranging from 1991 to 2007. The relationship between tannin measured by the methyl cellulose precipitation (MCP) reference method at 280 nm and tannin predicted with a multiple linear regression (MLR) calibration, using ultraviolet (UV) absorbance at 250, 270, 280, 290, and 315 nm, was strong (r(2)val = 0.92; SECV = 0.20 g/L). An independent validation set (n = 85) was predicted using the MLR algorithm developed with the calibration set and gave confidence in the ability to predict new samples, independent of the samples used to prepare the calibration (r(2)val = 0.94; SEP = 0.18 g/L). The MLR algorithm could also predict tannin in fermenting wines (r(2)val = 0.76; SEP = 0.18 g/L), but worked best from the second day of ferment on. This study also explored instrument-to-instrument transfer of a spectral calibration for MCP tannin. After slope and bias adjustments of the calibration, efficient calibration transfer to other laboratories was clearly demonstrated, with all instruments in the study effectively giving identical results on a transfer set.

Spectrophotometric Analysis of Caffeine

PubMed Central

Ahmad Bhawani, Showkat; Fong, Sim Siong; Mohamad Ibrahim, Mohamad Nasir

2015-01-01

The nature of caffeine reveals that it is a bitter white crystalline alkaloid. It is a common ingredient in a variety of drinks (soft and energy drinks) and is also used in combination with various medicines. In order to maintain the optimum level of caffeine, various spectrophotometric methods have been developed. The monitoring of caffeine is very important aspect because of its consumption in higher doses that can lead to various physiological disorders. This paper incorporates various spectrophotometric methods used in the analysis of caffeine in various environmental samples such as pharmaceuticals, soft and energy drinks, tea, and coffee. A range of spectrophotometric methodologies including chemometric techniques and derivatization of spectra have been used to analyse the caffeine. PMID:26604926

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.

PubMed

Wang, Liang; Yang, Die; Fang, Cheng; Chen, Zuliang; Lesniewski, Peter J; Mallavarapu, Megharaj; Naidu, Ravendra

2015-01-01

Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained. Copyright © 2014 Elsevier B.V. All rights reserved.

Determination of toxic and essential trace elements in serum of healthy and hypothyroid respondents by ICP-MS: A chemometric approach for discrimination of hypothyroidism.

PubMed

Stojsavljević, Aleksandar; Trifković, Jelena; Rasić-Milutinović, Zorica; Jovanović, Dragana; Bogdanović, Gradimir; Mutić, Jelena; Manojlović, Dragan

2018-07-01

Inductively coupled plasma-mass spectrometry ((ICP-MS)) was used to determine three toxic (Ni, As, Cd) and six essential trace elements (Cr, Mn, Co, Cu, Zn, Se) in blood serum of patients with hypothyroidism (Hy group) and healthy people (control group), in order to set the experimental conditions for accurate determination of a unique profile of these elements in hypothyroidism. Method validation was performed with standard reference material of the serum by varying the sample treatment with both standard and collision mode for analysis of elements isotopes. Quadratic curvilinear functions with good performances of models and the lowest detection limits were obtained for 52 Cr, 66 Zn, 75 As, 112 Cd in collision mode, and 55 Mn, 59 Co, 60 Ni, 65 Cu, 78 Se in standard mode. Treatment of serum samples with aqueous solution containing nitric acid, Triton X-100 and n-butanol gave the best results. Chemometric tools were applied for discrimination of patients with hypothyroidism. All nine elements discriminated Hy group of samples with almost the same discriminating power as indicated by their higher values for this group of patients. Statistically significant correlation (p < 0.01) was observed for several elements. Results indicated clear differences in element profile between Hy and control group and it could be used as a unique profile of hypothyroid state. Copyright © 2018 Elsevier GmbH. All rights reserved.

Application of high performance liquid chromatography for the profiling of complex chemical mixtures with the aid of chemometrics.

PubMed

Ni, Yongnian; Zhang, Liangsheng; Churchill, Jane; Kokot, Serge

2007-06-15

In this paper, chemometrics methods were applied to resolve the high performance liquid chromatography (HPLC) fingerprints of complex, many-component substances to compare samples from a batch from a given manufacturer, or from those of different producers. As an example of such complex substances, we used a common Chinese traditional medicine, Huoxiang Zhengqi Tincture (HZT) for this research. Twenty-one samples, each representing a separate HZT production batch from one of three manufacturers were analyzed by HPLC with the aid of a diode array detector (DAD). An Agilent Zorbax Eclipse XDB-C18 column with an Agilent Zorbax high pressure reliance cartridge guard-column were used. The mobile phase consisted of water (A) and methanol (B) with a gradient program of 25-65% (v/v, B) during 0-30min, 65-55% (v/v, B) during 30-35min and 55-100% (v/v, B) during 35-60min (flow rate, 1.0mlmin(-1); injection volume, 20mul; and column temperature-ambient). The detection wavelength was adjusted for maximum sensitivity at different time periods. A peak area matrix with 21objectsx14HPLC variables was obtained by sampling each chromatogram at 14 common retention times. Similarities were then calculated to discriminate the batch-to-batch samples and also, a more informative multi-criteria decision making methodology (MCDM), PROMETHEE and GAIA, was applied to obtain more information from the chromatograms in order to rank and compare the complex HZT profiles. The results showed that with the MCDM analysis, it was possible to match and discriminate correctly the batch samples from the three different manufacturers. Fourier transform infrared (FT-IR) spectra taken from samples from several batches were compared by the common similarity method with the HPLC results. It was found that the FT-IR spectra did not discriminate the samples from the different batches.

Microheterogeneity in binary mixtures of water with CH3OH and CD3OH: ATR-IR spectroscopic, chemometric and DFT studies

NASA Astrophysics Data System (ADS)

Tomza, Paweł; Wrzeszcz, Władysław; Mazurek, Sylwester; Szostak, Roman; Czarnecki, Mirosław Antoni

2018-05-01

Here we report ATR-IR spectroscopic study on the separation at a molecular level (microheterogeneity) and the degree of deviation of H2O/CH3OH and H2O/CD3OH mixtures from the ideal mixture. Of particular interest is the effect of isotopic substitution in methyl group on molecular structure and interactions in both mixtures. To obtain comprehensive information from the multivariate data we applied the excess molar absorptivity spectra together with two-dimensional correlation analysis (2DCOS) and chemometric methods. In addition, the experimental results were compared and discussed with the structures of various model clusters obtained from theoretical (DFT) calculations. Our results evidence the presence of separation at a molecular level and deviation from the ideal mixture for both mixtures. The experimental and theoretical results show that the maximum of these deviations appears at equimolar mixture. Both mixtures consist of three kinds of species: homoclusters of water and methanol and mixed clusters (heteroclusters). The heteroclusters exist in the whole range of mole fractions with the maximum close to the equimolar mixture. At this mixture composition near 55-60% of molecules are involved in heteroclusters. In contrast, the homoclusters of water occur in a limited range of mole fractions (XME < 0.85-0.9). Upon mixing the molecules of methanol form weaker hydrogen bonding as compared with the pure alcohol. In contrast, the molecules of water in the mixture are involved in stronger hydrogen bonding than those in bulk water. All these results indicate that both mixtures have similar degree of deviation from the ideal mixture.

Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy

NASA Astrophysics Data System (ADS)

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

This research work describes two studies for the classification and characterization of edible oils and its quality parameters through Fourier transform mid infrared spectroscopy (FT-mid-IR) together with chemometric methods. The discrimination of canola, sunflower, corn and soybean oils was investigated using SVM-DA, SIMCA and PLS-DA. Using FT-mid-IR, DPLS was able to classify 100% of the samples from the validation set, but SIMCA and SVM-DA were not. The quality parameters: refraction index and relative density of edible oils were obtained from reference methods. Prediction models for FT-mid-IR spectra were calculated for these quality parameters using partial least squares (PLS) and support vector machines (SVM). Several preprocessing alternatives (first derivative, multiplicative scatter correction, mean centering, and standard normal variate) were investigated. The best result for the refraction index was achieved with SVM as well as for the relative density except when the preprocessing combination of mean centering and first derivative was used. For both of quality parameters, the best results obtained for the figures of merit expressed by the root mean square error of cross validation (RMSECV) and prediction (RMSEP) were equal to 0.0001.

Application of attenuated total reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in MIR range coupled with chemometrics for detection of pig body fat in pure ghee (heat clarified milk fat)

NASA Astrophysics Data System (ADS)

Upadhyay, Neelam; Jaiswal, Pranita; Jha, Shyam Narayan

2018-02-01

Pure ghee is superior to other fats and oils due to the presence of bioactive lipids and its rich flavor. Adulteration of ghee with cheaper fats and oils is a prevalent fraudulent practice. ATR-FTIR spectroscopy was coupled with chemometrics for the purpose of detection of presence of pig body fat in pure ghee. Pure mixed ghee was spiked with pig body fat @ 3, 4, 5, 10, 15% level. The spectra of pure (ghee and pig body fat) along with the spiked samples was taken in MIR from 4000 to 500 cm-1. Some wavenumber ranges were selected on the basis of differences in the spectra obtained. Separate clusters of the samples were obtained by employing principal component analysis at 5% level of significance on the selected wavenumber range. Probable class membership was predicted by applying SIMCA approach. Approximately, 90% of the samples classified into their respective class and pure ghee and pig body fat never misclassified themselves. The value of R2 was >0.99 for both calibration and validation sets using partial least square method. The study concluded that spiking of pig body fat in pure ghee can be detected even at a level of 3%.

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.

Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method

PubMed Central

Dakshanamurthy, Sivanesan; Issa, Naiem T; Assefnia, Shahin; Seshasayee, Ashwini; Peters, Oakland J; Madhavan, Subha; Uren, Aykut; Brown, Milton L; Byers, Stephen W

2012-01-01

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS

Improved chemometric methodologies for the assessment of soil carbon sequestration mechanisms

NASA Astrophysics Data System (ADS)

Jiménez-González, Marco A.; Almendros, Gonzalo; Álvarez, Ana M.; González-Vila, Francisco J.

2016-04-01

The factors involved soil C sequestration, which is reflected in the highly variable content of organic matter in the soils, are not yet well defined. Therefore, their identification is crucial for understanding Earth's biogeochemical cycle and global change. The main objective of this work is to contribute to a better qualitative and quantitative assessment of the mechanisms of organic C sequestration in the soil, using omic approaches not requiring the detailed knowledge of the structure of the material under study. With this purpose, we have carried out a series of chemometric approaches on a set of widely differing soils (35 representative ecosystems). In an exploratory phase, we used multivariate statistical models (e.g., multidimensional scaling, discriminant analysis with automatic backward variable selection…) to analyze arrays of more than 200 independent soil variables (physicochemical, spectroscopic, pyrolytic...) in order to select those factors (descriptors or proxies) that explain most of the total system variance (content and stability of the different C forms). These models showed that the factors determining the stabilization of organic material are greatly dependent on the soil type. In some cases, the molecular structure of organic matter seemed strongly correlated with their resilience, while in other soil types the organo-mineral interactions played a significant bearing on the accumulation of selectively preserved C forms. In any case, it was clear that the factors driving the resilience of organic matter are manifold and not exclusive. Consequently, in a second stage, prediction models of the soil C content and their biodegradability (laboratory incubation experiments) were carried out by massive data processing by partial least squares (PLS) regression of data from Py-GC-MS and Py-MS. In some models, PLS was applied to a matrix of 150 independent variables corresponding to major pyrolysis compounds (peak areas) from the 35 samples of whole

Chemometric resolution of fully overlapped CE peaks: quantitation of carbamazepine in human serum in the presence of several interferences.

PubMed

Vera-Candioti, Luciana; Culzoni, María J; Olivieri, Alejandro C; Goicoechea, Héctor C

2008-11-01

Drug monitoring in serum samples was performed using second-order data generated by CE-DAD, processed with a suitable chemometric strategy. Carbamazepine could be accurately quantitated in the presence of its main metabolite (carbamazepine epoxide), other therapeutic drugs (lamotrigine, phenobarbital, phenytoin, phenylephrine, ibuprofen, acetaminophen, theophylline, caffeine, acetyl salicylic acid), and additional serum endogenous components. The analytical strategy consisted of the following steps: (i) serum sample clean-up to remove matrix interferences, (ii) data pre-processing, in order to reduce the background and to correct for electrophoretic time shifts, and (iii) resolution of fully overlapped CE peaks (corresponding to carbamazepine, its metabolite, lamotrigine and unexpected serum components) by the well-known multivariate curve resolution-alternating least squares algorithm, which extracts quantitative information that can be uniquely ascribed to the analyte of interest. The analyte concentration in serum samples ranged from 2.00 to 8.00 mg/L. Mean recoveries were 102.6% (s=7.7) for binary samples, and 94.8% (s=13.5) for spiked serum samples, while CV (%)=4.0 was computed for five replicate, indicative of the acceptable accuracy and precision of the proposed method.

Microheterogeneity in binary mixtures of water with CH3OH and CD3OH: ATR-IR spectroscopic, chemometric and DFT studies.

PubMed

Tomza, Paweł; Wrzeszcz, Władysław; Mazurek, Sylwester; Szostak, Roman; Czarnecki, Mirosław Antoni

2018-05-15

Here we report ATR-IR spectroscopic study on the separation at a molecular level (microheterogeneity) and the degree of deviation of H 2 O/CH 3 OH and H 2 O/CD 3 OH mixtures from the ideal mixture. Of particular interest is the effect of isotopic substitution in methyl group on molecular structure and interactions in both mixtures. To obtain comprehensive information from the multivariate data we applied the excess molar absorptivity spectra together with two-dimensional correlation analysis (2DCOS) and chemometric methods. In addition, the experimental results were compared and discussed with the structures of various model clusters obtained from theoretical (DFT) calculations. Our results evidence the presence of separation at a molecular level and deviation from the ideal mixture for both mixtures. The experimental and theoretical results show that the maximum of these deviations appears at equimolar mixture. Both mixtures consist of three kinds of species: homoclusters of water and methanol and mixed clusters (heteroclusters). The heteroclusters exist in the whole range of mole fractions with the maximum close to the equimolar mixture. At this mixture composition near 55-60% of molecules are involved in heteroclusters. In contrast, the homoclusters of water occur in a limited range of mole fractions (X ME  < 0.85-0.9). Upon mixing the molecules of methanol form weaker hydrogen bonding as compared with the pure alcohol. In contrast, the molecules of water in the mixture are involved in stronger hydrogen bonding than those in bulk water. All these results indicate that both mixtures have similar degree of deviation from the ideal mixture. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectrally resolved infrared microscopy and chemometric tools to reveal the interaction between blue light (470nm) and methicillin-resistant Staphylococcus aureus.

PubMed

Bumah, Violet V; Aboualizadeh, Ebrahim; Masson-Meyers, Daniela S; Eells, Janis T; Enwemeka, Chukuka S; Hirschmugl, Carol J

2017-02-01

Blue light inactivates methicillin-resistant Staphylococcus aureus (MRSA), a Gram-positive antibiotic resistant bacterium that leads to fatal infections; however, the mechanism of bacterial death remains unclear. In this paper, to uncover the mechanism underlying the bactericidal effect of blue light, a combination of Fourier transform infrared (FTIR) spectroscopy and chemometric tools is employed to detect the photoreactivity of MRSA and its distinctive pathway toward apoptosis after treatment. The mechanism of action of UV light and vancomycin against MRSA is also investigated to support the findings. Principal component analysis followed by linear discriminant analysis (PCA- LDA) is employed to reveal clustering of five groups of MRSA samples, namely untreated (control I), untreated and incubated at ambient air (control II), irradiated with 470nm blue light, irradiated with 253.5 UV light, and vancomycin-treated MRSA. Loadings plot from PCA-LDA analysis reveals important functional groups in proteins (1683, 1656, 1596, 1542cm -1 ), lipids (1743, 1409cm -1 ), and nucleic acids region of the spectrum (1060, 1087cm -1 ) that are responsible for the classification of blue light irradiated spectra and control spectra. Cluster vector plots and scores plot reveals that UV light-irradiated spectra are the most biochemically similar to blue light- irradiated spectra; however, some wavenumbers experience a shift. The shifts between blue light and UV light irradiated loadings plot at ν asym PO 2- band (from 1228 to 1238cm -1 ), DNA backbone (from 970 to 966cm -1 ) and base pairing vibration of DNA (from 1717 to 1712cm -1 ) suggest distinctive changes in DNA conformation in response to irradiation. Our findings indicate that irradiation of MRSA with 470nm light induces A-DNA cleavage and that B-DNA is more resistant to damage by blue light. Blue light and UV light treatment of MRSA are complementary and distinct from the known antimicrobial effect of vancomycin. Moreover

Authenticity of rice (Oryza sativa L.) geographical origin based on analysis of C, N, O and S stable isotope ratios: a preliminary case report in Korea, China and Philippine.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Prabakaran, Mayakrishnan; Yang, Jin-Hee; Kim, Seung-Hyun

2016-05-01

Although rice (Oryza sativa L.) is the third largest food crop, relatively fewer studies have been reported on rice geographical origin based on light element isotope ratios in comparison with other foods such as wine, beef, juice, oil and milk. Therefore this study tries to discriminate the geographical origin of the same rice cultivars grown in different Asian countries using the analysis of C, N, O and S stable isotope ratios and chemometrics. The δ(15) NAIR , δ(18) OVSMOW and δ(34) SVCDT values of brown rice were more markedly influenced by geographical origin than was the δ(13) CVPDB value. In particular, the combination of δ(18) OVSMOW and δ(34) SVCDT more efficiently discriminated rice geographical origin than did the remaining combinations. Principal component analysis (PCA) revealed a clear discrimination between different rice geographical origins but not between rice genotypes. In particular, the first components of PCA discriminated rice cultivated in the Philippines from rice cultivated in China and Korea. The present findings suggest that analysis of the light element isotope composition combined with chemometrics can be potentially applicable to discriminate rice geographical origin and also may provide a valuable insight into the control of improper or fraudulent labeling regarding the geographical origin of rice worldwide. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Near infrared spectroscopy and chemometrics analysis of complex traits in animal physiology

USDA-ARS?s Scientific Manuscript database

Near infrared reflectance (NIR) applications have been expanding from the traditional framework of small molecule chemical purity and composition (as defined by spectral libraries) to complex system analysis and holistic exploratory approaches to questions in biochemistry, biophysics and environment...

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics.

PubMed

Liu, Li; Fan, Yao; Fu, Haiyan; Chen, Feng; Ni, Chuang; Wang, Jinxing; Yin, Qiaobo; Mu, Qingling; Yang, Tianming; She, Yuanbin

2017-04-22

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

Chemometrics-assisted investigation of interactions of Tasmar with human serum albumin at a glassy carbon disk: Application to electrochemical biosensing of electro-inactive serum albumin.

PubMed

Mohammadi, Ghobad; Faramarzi, Elahe; Mahmoudi, Majid; Ghobadi, Sirous; Ghiasvand, Ali Reza; Goicoechea, Hector C; Jalalvand, Ali R

2018-07-15

In this work, voltammetric data recorded by a glassy carbon electrode (GCE) was used to investigate the interactions of tolcapone (Tasmar, TAS) with human serum albumin (HSA) at the electrode surface. The recorded voltammetric data was also combined with spectroscopic data to construct an augmented data matrix which was analysed by multivariate curve resolution-alternating least squares (MCR-ALS) as an efficient chemometric tool to obtain more information about TAS-HSA interactions. The results of MCR-ALS confirmed formation of one complex species (HSA-TAS 2 ) and application of MCR-BANDS to the results of MCR-ALS confirmed the absence of rotational ambiguities and existing unambiguous and reliable results. Binding of TAS to HSA was also modeled by molecular docking and the results showed that the TAS was bound to sub-domain IIA of HSA which were compatible with the ones obtained by recording experimental data. Hard-modeling of combined voltammetric and spectroscopic data by EQUISPEC helped us to compute binding constant of HSA-TAS 2 complex species which was compatible with the binding constant value obtained by direct analysis of experimental data. Finally, a new electroanalytical method was developed based on TAS-HSA interactions for determination of HSA in two ranges of 0-541 nM and 541-1200 nM with a limit of detection of 0.04 nM and a sensitivity of 0.02 μA nM -1 . Copyright © 2018 Elsevier B.V. All rights reserved.

Transmission near-infrared (NIR) and photon time-of-flight (PTOF) spectroscopy in a comparative analysis of pharmaceuticals.

PubMed

Kamran, Faisal; Abildgaard, Otto H A; Sparén, Anders; Svensson, Olof; Johansson, Jonas; Andersson-Engels, Stefan; Andersen, Peter E; Khoptyar, Dmitry

2015-03-01

We present a comprehensive study of the application of photon time-of-flight spectroscopy (PTOFS) in the wavelength range 1050-1350 nm as a spectroscopic technique for the evaluation of the chemical composition and structural properties of pharmaceutical tablets. PTOFS is compared to transmission near-infrared spectroscopy (NIRS). In contrast to transmission NIRS, PTOFS is capable of directly and independently determining the absorption and reduced scattering coefficients of the medium. Chemometric models were built on the evaluated absorption spectra for predicting tablet drug concentration. Results are compared to corresponding predictions built on transmission NIRS measurements. The predictive ability of PTOFS and transmission NIRS is comparable when models are based on uniformly distributed tablet sets. For non-uniform distribution of tablets based on particle sizes, the prediction ability of PTOFS is better than that of transmission NIRS. Analysis of reduced scattering spectra shows that PTOFS is able to characterize tablet microstructure and manufacturing process parameters. In contrast to the chemometric pseudo-variables provided by transmission NIRS, PTOFS provides physically meaningful quantities such as scattering strength and slope of particle size. The ability of PTOFS to quantify the reduced scattering spectra, together with its robustness in predicting drug content, makes it suitable for such evaluations in the pharmaceutical industry.

Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.

PubMed

Monakhova, Yulia B; Mushtakova, Svetlana P

2017-05-01

A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

A scoring metric for multivariate data for reproducibility analysis using chemometric methods

PubMed Central

Sheen, David A.; de Carvalho Rocha, Werickson Fortunato; Lippa, Katrice A.; Bearden, Daniel W.

2017-01-01

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory’s individual performance. PMID:28694553

Triacylglycerol stereospecific analysis and linear discriminant analysis for milk speciation.

PubMed

Blasi, Francesca; Lombardi, Germana; Damiani, Pietro; Simonetti, Maria Stella; Giua, Laura; Cossignani, Lina

2013-05-01

Product authenticity is an important topic in dairy sector. Dairy products sold for public consumption must be accurately labelled in accordance with the contained milk species. Linear discriminant analysis (LDA), a common chemometric procedure, has been applied to fatty acid% composition to classify pure milk samples (cow, ewe, buffalo, donkey, goat). All original grouped cases were correctly classified, while 90% of cross-validated grouped cases were correctly classified. Another objective of this research was the characterisation of cow-ewe milk mixtures in order to reveal a common fraud in dairy field, that is the addition of cow to ewe milk. Stereospecific analysis of triacylglycerols (TAG), a method based on chemical-enzymatic procedures coupled with chromatographic techniques, has been carried out to detect fraudulent milk additions, in particular 1, 3, 5% cow milk added to ewe milk. When only TAG composition data were used for the elaboration, 75% of original grouped cases were correctly classified, while totally correct classified samples were obtained when both total and intrapositional TAG data were used. Also the results of cross validation were better when TAG stereospecific analysis data were considered as LDA variables. In particular, 100% of cross-validated grouped cases were obtained when 5% cow milk mixtures were considered.

Application of support vector machine method for the analysis of absorption spectra of exhaled air of patients with broncho-pulmonary diseases

NASA Astrophysics Data System (ADS)

Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kistenev, Yury V.; Kuzmin, Dmitry A.; Tuzikov, Sergei A.; Yumov, Evgeny L.

2014-11-01

The results of the joint use of laser photoacoustic spectroscopy and chemometrics methods in gas analysis of exhaled air of patients with respiratory diseases (chronic obstructive pulmonary disease, pneumonia and lung cancer) are presented. The absorption spectra of exhaled breath of all volunteers were measured, the classification methods of the scans of the absorption spectra were applied, the sensitivity/specificity of the classification results were determined. It were obtained a result of nosological in pairs classification for all investigated volunteers, indices of sensitivity and specificity.

Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

NASA Astrophysics Data System (ADS)

Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

2005-03-01

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

Fluorescence, electrophoretic and chromatographic fingerprints of herbal medicines and their comparative chemometric analysis.

PubMed

Mazina, Jekaterina; Vaher, Merike; Kuhtinskaja, Maria; Poryvkina, Larisa; Kaljurand, Mihkel

2015-07-01

The aim of the present study was to compare the polyphenolic compositions of 47 medicinal herbs (HM) and four herbal tea mixtures from Central Estonia by rapid, reliable and sensitive Spectral Fluorescence Signature (SFS) method in a front face mode. The SFS method was validated for the main identified HM representatives including detection limits (0.037mgL(-1) for catechin, 0.052mgL(-1) for protocatechuic acid, 0.136mgL(-1) for chlorogenic acid, 0.058mgL(-1) for syringic acid and 0.256mgL(-1) for ferulic acid), linearity (up to 5.0-15mgL(-1)), intra-day precision (RSDs=6.6-10.6%), inter-day precision (RSDs=6.4-13.8%), matrix effect (-15.8 to +5.5) and recovery (85-107%). The phytochemical fingerprints were differentiated by parallel factor analysis (PARAFAC) combined with hierarchical cluster analysis (CA) and principal component analysis (PCA). HM were clustered into four main clusters (catechin-like, hydroxycinnamic acid-like, dihydrobenzoic acid-like derivatives containing HM and HM with low/very low content of fluorescent constituents) and 14 subclusters (rich, medium, low/very low contents). The average accuracy and precision of CA for validation HM set were 97.4% (within 85.2-100%) and 89.6%, (within 66.7-100%), respectively. PARAFAC-PCA/CA has improved the analysis of HM by the SFS method. The results were verified by two separation methods CE-DAD and HPLC-DAD-MS also combined with PARAFAC-PCA/CA. The SFS-PARAFAC-PCA/CA method has potential as a rapid and reliable tool for investigating the fingerprints and predicting the composition of HM or evaluating the quality and authenticity of different standardised formulas. Moreover, SFS-PARAFAC-PCA/CA can be implemented as a laboratory and/or an onsite method. Copyright © 2015 Elsevier B.V. All rights reserved.

Principal coordinate analysis assisted chromatographic analysis of bacterial cell wall collection: A robust classification approach.

PubMed

Kumar, Keshav; Cava, Felipe

2018-04-10

In the present work, Principal coordinate analysis (PCoA) is introduced to develop a robust model to classify the chromatographic data sets of peptidoglycan sample. PcoA captures the heterogeneity present in the data sets by using the dissimilarity matrix as input. Thus, in principle, it can even capture the subtle differences in the bacterial peptidoglycan composition and can provide a more robust and fast approach for classifying the bacterial collection and identifying the novel cell wall targets for further biological and clinical studies. The utility of the proposed approach is successfully demonstrated by analysing the two different kind of bacterial collections. The first set comprised of peptidoglycan sample belonging to different subclasses of Alphaproteobacteria. Whereas, the second set that is relatively more intricate for the chemometric analysis consist of different wild type Vibrio Cholerae and its mutants having subtle differences in their peptidoglycan composition. The present work clearly proposes a useful approach that can classify the chromatographic data sets of chromatographic peptidoglycan samples having subtle differences. Furthermore, present work clearly suggest that PCoA can be a method of choice in any data analysis workflow. Copyright © 2018 Elsevier Inc. All rights reserved.

Elemental profiling of Noah's Ark shell (Arca noae, Linnaeus, 1758) by plasma optical spectrometry and chemometric tools.

PubMed

Kobelja, Kristina; Nemet, Ivan; Župan, Ivan; Čulin, Jelena; Rončević, Sanda

2016-12-01

Determination of metal content in biominerals provides essential information with respect to relations between biomineralization processes and environmental status. Mussels are species that have a great potential as bio-marker species and therefore, they are in the focus of numerous biomineralization and ecological studies. In this study, elemental profile of mussel shell of Noah's Ark (Arca noe, Linnaeus, 1758), which inhabit eastern Adriatic Sea was obtained by determination of seventeen elements content using inductively coupled plasma optical emission spectrometry (ICP-OES). Shell samples were collected from marine protected area and from marine shipping route in eastern Adriatic Sea. The accuracy of applied analytical procedure based on microwave decomposition of shell samples was tested by use of reference materials of limestone and by matrix-matched standards. By aid of chemometric methods, the elemental profile along with variability of elements content of shell was obtained. The impact of different environment on elements content was established by use of multivariate statistical PCA method. Discernment between two groups of samples was manifested. Among results of main, minor and trace elements content, the last one which denoted to Cd, Co, Cu, Pb, and Mn was expressed as principal distinctive feature of shell samples collected from different sampling sites. Elemental profiling of mussel shell Noah's Ark provides novel insight in species status as well as in environmental status on the observed locations. Copyright © 2016 Elsevier GmbH. All rights reserved.

Simultaneous estimation of ramipril, acetylsalicylic acid and atorvastatin calcium by chemometrics assisted UV-spectrophotometric method in capsules.

PubMed

Sankar, A S Kamatchi; Vetrichelvan, Thangarasu; Venkappaya, Devashya

2011-09-01

In the present work, three different spectrophotometric methods for simultaneous estimation of ramipril, aspirin and atorvastatin calcium in raw materials and in formulations are described. Overlapped data was quantitatively resolved by using chemometric methods, viz. inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix. The linearity range was found to be 1-5, 10-50 and 2-10 μg mL-1 for ramipril, aspirin and atorvastatin calcium, respectively. The absorbance matrix was obtained by measuring the zero-order absorbance in the wavelength range between 210 and 320 nm. A training set design of the concentration data corresponding to the ramipril, aspirin and atorvastatin calcium mixtures was organized statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for PLS 1, PCR and ILS to the measured spectra of the calibration set, a suitable model was obtained. This model was selected on the basis of RMSECV and RMSEP values. The same was applied to the prediction set and capsule formulation. Mean recoveries of the commercial formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validity of the proposed approaches was successfully assessed for analyses of drugs in the various prepared physical mixtures and formulations.

[Analysis of H2S/PH3/NH3/AsH3/Cl2 by Full-Spectral Flame Photometric Detector].

PubMed

Ding, Zhi-jun; Wang, Pu-hong; Li, Zhi-jun; Du, Bin; Guo, Lei; Yu, Jian-hua

2015-07-01

Flame photometric analysis technology has been proven to be a rapid and sensitive method for sulfur and phosphorus detection. It has been widely used in environmental inspections, pesticide detection, industrial and agricultural production. By improving the design of the traditional flame photometric detector, using grating and CCD sensor array as a photoelectric conversion device, the types of compounds that can be detected were expanded. Instead of a single point of characteristic spectral lines, full spectral information has been used for qualitative and quantitative analysis of H2S, PH3, NH3, AsH3 and Cl2. Combined with chemometric method, flame photometric analysis technology is expected to become an alternative fast, real-time on-site detection technology to simultaneously detect multiple toxic and harmful gases.

Micro-Raman spectroscopy of natural and synthetic indigo samples.

PubMed

Vandenabeele, Peter; Moens, Luc

2003-02-01

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

PubMed

Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna

2009-01-01

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Microwave-assisted of dispersive liquid-liquid microextraction and spectrophotometric determination of uranium after optimization based on Box-Behnken design and chemometrics methods.

PubMed

Niazi, Ali; Khorshidi, Neda; Ghaemmaghami, Pegah

2015-01-25

In this study an analytical procedure based on microwave-assisted dispersive liquid-liquid microextraction (MA-DLLME) and spectrophotometric coupled with chemometrics methods is proposed to determine uranium. In the proposed method, 4-(2-pyridylazo) resorcinol (PAR) is used as a chelating agent, and chloroform and ethanol are selected as extraction and dispersive solvent. The optimization strategy is carried out by using two level full factorial designs. Results of the two level full factorial design (2(4)) based on an analysis of variance demonstrated that the pH, concentration of PAR, amount of dispersive and extraction solvents are statistically significant. Optimal condition for three variables: pH, concentration of PAR, amount of dispersive and extraction solvents are obtained by using Box-Behnken design. Under the optimum conditions, the calibration graphs are linear in the range of 20.0-350.0 ng mL(-1) with detection limit of 6.7 ng mL(-1) (3δB/slope) and the enrichment factor of this method for uranium reached at 135. The relative standard deviation (R.S.D.) is 1.64% (n=7, c=50 ng mL(-1)). The partial least squares (PLS) modeling was used for multivariate calibration of the spectrophotometric data. The orthogonal signal correction (OSC) was used for preprocessing of data matrices and the prediction results of model, with and without using OSC, were statistically compared. MA-DLLME-OSC-PLS method was presented for the first time in this study. The root mean squares error of prediction (RMSEP) for uranium determination using PLS and OSC-PLS models were 4.63 and 0.98, respectively. This procedure allows the determination of uranium synthesis and real samples such as waste water with good reliability of the determination. Copyright © 2014. Published by Elsevier B.V.

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.

Metabolite Profiling and Classification of DNA-Authenticated Licorice Botanicals

PubMed Central

Simmler, Charlotte; Anderson, Jeffrey R.; Gauthier, Laura; Lankin, David C.; McAlpine, James B.; Chen, Shao-Nong; Pauli, Guido F.

2015-01-01

Raw licorice roots represent heterogeneous materials obtained from mainly three Glycyrrhiza species. G. glabra, G. uralensis, and G. inflata exhibit marked metabolite differences in terms of flavanones (Fs), chalcones (Cs), and other phenolic constituents. The principal objective of this work was to develop complementary chemometric models for the metabolite profiling, classification, and quality control of authenticated licorice. A total of 51 commercial and macroscopically verified samples were DNA authenticated. Principal component analysis and canonical discriminant analysis were performed on 1H NMR spectra and area under the curve values obtained from UHPLC-UV chromatograms, respectively. The developed chemometric models enable the identification and classification of Glycyrrhiza species according to their composition in major Fs, Cs, and species specific phenolic compounds. Further key outcomes demonstrated that DNA authentication combined with chemometric analyses enabled the characterization of mixtures, hybrids, and species outliers. This study provides a new foundation for the botanical and chemical authentication, classification, and metabolomic characterization of crude licorice botanicals and derived materials. Collectively, the proposed methods offer a comprehensive approach for the quality control of licorice as one of the most widely used botanical dietary supplements. PMID:26244884

Rapid analysis of glucose, fructose, sucrose, and maltose in honeys from different geographic regions using fourier transform infrared spectroscopy and multivariate analysis.

PubMed

Wang, Jun; Kliks, Michael M; Jun, Soojin; Jackson, Mel; Li, Qing X

2010-03-01

Quantitative analysis of glucose, fructose, sucrose, and maltose in different geographic origin honey samples in the world using the Fourier transform infrared (FTIR) spectroscopy and chemometrics such as partial least squares (PLS) and principal component regression was studied. The calibration series consisted of 45 standard mixtures, which were made up of glucose, fructose, sucrose, and maltose. There were distinct peak variations of all sugar mixtures in the spectral "fingerprint" region between 1500 and 800 cm(-1). The calibration model was successfully validated using 7 synthetic blend sets of sugars. The PLS 2nd-derivative model showed the highest degree of prediction accuracy with a highest R(2) value of 0.999. Along with the canonical variate analysis, the calibration model further validated by high-performance liquid chromatography measurements for commercial honey samples demonstrates that FTIR can qualitatively and quantitatively determine the presence of glucose, fructose, sucrose, and maltose in multiple regional honey samples.

Partial least-squares with residual bilinearization for the spectrofluorimetric determination of pesticides. A solution of the problems of inner-filter effects and matrix interferents.

PubMed

Piccirilli, Gisela N; Escandar, Graciela M

2006-09-01

This paper demonstrates for the first time the power of a chemometric second-order algorithm for predicting, in a simple way and using spectrofluorimetric data, the concentration of analytes in the presence of both the inner-filter effect and unsuspected species. The simultaneous determination of the systemic fungicides carbendazim and thiabendazole was achieved and employed for the discussion of the scopes of the applied second-order chemometric tools: parallel factor analysis (PARAFAC) and partial least-squares with residual bilinearization (PLS/RBL). The chemometric study was performed using fluorescence excitation-emission matrices obtained after the extraction of the analytes over a C18-membrane surface. The ability of PLS/RBL to recognize and overcome the significant changes produced by thiabendazole in both the excitation and emission spectra of carbendazim is demonstrated. The high performance of the selected PLS/RBL method was established with the determination of both pesticides in artificial and real samples.

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.

Univariate and multivariate analysis of tannin-impregnated wood species using vibrational spectroscopy.

PubMed

Schnabel, Thomas; Musso, Maurizio; Tondi, Gianluca

2014-01-01

Vibrational spectroscopy is one of the most powerful tools in polymer science. Three main techniques--Fourier transform infrared spectroscopy (FT-IR), FT-Raman spectroscopy, and FT near-infrared (NIR) spectroscopy--can also be applied to wood science. Here, these three techniques were used to investigate the chemical modification occurring in wood after impregnation with tannin-hexamine preservatives. These spectroscopic techniques have the capacity to detect the externally added tannin. FT-IR has very strong sensitivity to the aromatic peak at around 1610 cm(-1) in the tannin-treated samples, whereas FT-Raman reflects the peak at around 1600 cm(-1) for the externally added tannin. This high efficacy in distinguishing chemical features was demonstrated in univariate analysis and confirmed via cluster analysis. Conversely, the results of the NIR measurements show noticeable sensitivity for small differences. For this technique, multivariate analysis is required and with this chemometric tool, it is also possible to predict the concentration of tannin on the surface.

Characterization of Leaf Extracts of Schinus terebinthifolius Raddi by GC-MS and Chemometric Analysis

PubMed Central

Carneiro, Fabíola B.; Lopes, Pablo Q.; Ramalho, Ricardo C.; Scotti, Marcus T.; Santos, Sócrates G.; Soares, Luiz A. L.

2017-01-01

Background: Schinus terebinthifolius Raddi belongs to Anacardiacea family and is widely known as “aroeira.” This species originates from South America, and its extracts are used in folk medicine due to its therapeutic properties, which include antimicrobial, anti-inflammatory, and antipyretic effects. The complexity and variability of the chemical constitution of the herbal raw material establishes the quality of the respective herbal medicine products. Objective: Thus, the purpose of this study was to investigate the variability of the volatile compounds from leaves of S. terebinthifolius. Materials and Methods: The samples were collected from different states of the Northeast region of Brazil and analyzed with a gas chromatograph coupled to a mass spectrometer (GC-MS). The collected data were analyzed using multivariate data analysis. Results: The samples’ chromatograms, obtained by GC-MS, showed similar chemical profiles in a number of peaks, but some differences were observed in the intensity of these analytical markers. The chromatographic fingerprints obtained by GC-MS were suitable for discrimination of the samples; these results along with a statistical treatment (principal component analysis [PCA]) were used as a tool for comparative analysis between the different samples of S. terebinthifolius. Conclusion: The experimental data show that the PCA used in this study clustered the samples into groups with similar chemical profiles, which builds an appropriate approach to evaluate the similarity in the phytochemical pattern found in the different leaf samples. SUMMARY The leave extracts of Schinus terebinthifolius were obtained by turbo-extractionThe extracts were partitioned with hexane and analyzed by GC-MSThe chromatographic data were analyzed using the principal component analysis (PCA)The PCA plots showed the main compounds (phellandrene, limonene, and carene), which were used to group the samples from a different geographical location in

[Quality control of Guci tablets using UPLC-ELSD fingerprint analysis coupled with chemometrics].

PubMed

Wang, Ya-Dan; Dai, Zhong; Sun, Cai-Lin; Wu, Xian-Fu; Ma, Shuang-Cheng

2018-03-01

Ultra-performance liquid chromatography-evaporative light scattering detection (UPLC-ELSD) fingerprint analysis method was established for quality control of Guci tablets. Chromatographic separation was performed on Waters Acquity UPLC BEH C₁₈ column (2.1 mm×100 mm, 1.7 μm) at 30 °C of column temperature. Acetonitrile-0.1% formic acid solution was adopted as mobile phase for gradient elution. The flow rate was set at 0.3 mL·min⁻¹, and the injection volume was 3 μL. Detection was carried out on an ELSD with a nitrogen pressure of 0.28 MPa, drift tube temperature of 60 °C, and gain of 400. A total of 39 batches of samples produced by six manufacturers were measured by using the above method and the data were analyzed by ChemPattern software. The peak present in more than 75% of the samples was defined as a common peak, and 30 common peaks were determined. Among them, 19 peaks were identified by rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) method, 16 of which were confirmed by reference substances. The similarity of the tested samples was 0.47-0.98, suggesting that the quality of the samples from different manufacturers varied greatly. Furthermore, principal component analysis (PCA) and hierarchical analysis (HCA) were performed to clarify the main different components in samples. The results indicated that there might be some feeding problems about Paeoniae Radix Alba, Notoginseng Radix et Rhizoma, and Clematidis Radix et Rhizoma in a few manufacturers. This study provided some evidences for the overall quality control of Guci tablets, as well as its quality standard improvements. Copyright© by the Chinese Pharmaceutical Association.

Hyperspectral imaging coupled with chemometric analysis for non-invasive differentiation of black pens

NASA Astrophysics Data System (ADS)

Chlebda, Damian K.; Majda, Alicja; Łojewski, Tomasz; Łojewska, Joanna

2016-11-01

Differentiation of the written text can be performed with a non-invasive and non-contact tool that connects conventional imaging methods with spectroscopy. Hyperspectral imaging (HSI) is a relatively new and rapid analytical technique that can be applied in forensic science disciplines. It allows an image of the sample to be acquired, with full spectral information within every pixel. For this paper, HSI and three statistical methods (hierarchical cluster analysis, principal component analysis, and spectral angle mapper) were used to distinguish between traces of modern black gel pen inks. Non-invasiveness and high efficiency are among the unquestionable advantages of ink differentiation using HSI. It is also less time-consuming than traditional methods such as chromatography. In this study, a set of 45 modern gel pen ink marks deposited on a paper sheet were registered. The spectral characteristics embodied in every pixel were extracted from an image and analysed using statistical methods, externally and directly on the hypercube. As a result, different black gel inks deposited on paper can be distinguished and classified into several groups, in a non-invasive manner.

Towards the identification of plant and animal binders on Australian stone knives.

PubMed

Blee, Alisa J; Walshe, Keryn; Pring, Allan; Quinton, Jamie S; Lenehan, Claire E

2010-07-15

There is limited information regarding the nature of plant and animal residues used as adhesives, fixatives and pigments found on Australian Aboriginal artefacts. This paper reports the use of FTIR in combination with the chemometric tools principal component analysis (PCA) and hierarchical clustering (HC) for the analysis and identification of Australian plant and animal fixatives on Australian stone artefacts. Ten different plant and animal residues were able to be discriminated from each other at a species level by combining FTIR spectroscopy with the chemometric data analysis methods, principal component analysis (PCA) and hierarchical clustering (HC). Application of this method to residues from three broken stone knives from the collections of the South Australian Museum indicated that two of the handles of knives were likely to have contained beeswax as the fixative whilst Spinifex resin was the probable binder on the third. Copyright 2010 Elsevier B.V. All rights reserved.

Analysis and differentiation of paper samples by capillary electrophoresis and multivariate analysis.

PubMed

Fernández de la Ossa, Ma Ángeles; Ortega-Ojeda, Fernando; García-Ruiz, Carmen

2014-11-01

This work reports an investigation for the analysis of different paper samples using CE with laser-induced detection. Papers from four different manufactures (white-copy paper) and four different paper sources (white and recycled-copy papers, adhesive yellow paper notes and restaurant serviettes) were pulverized by scratching with a surgical scalpel prior to their derivatization with a fluorescent labeling agent, 8-aminopyrene-1,3,6-trisulfonic acid. Methodological conditions were evaluated, specifically the derivatization conditions with the aim to achieve the best S/N signals and the separation conditions in order to obtain optimum values of sensitivity and reproducibility. The best conditions, in terms of fastest, and easiest sample preparation procedure, minimal sample consumption, as well as the use of the simplest and fastest CE-procedure for obtaining the best analytical parameters, were applied to the analysis of the different paper samples. The registered electropherograms were pretreated (normalized and aligned) and subjected to multivariate analysis (principal component analysis). A successful discrimination among paper samples without entanglements was achieved. To the best of our knowledge, this work presents the first approach to achieve a successful differentiation among visually similar white-copy paper samples produced by different manufactures and paper from different paper sources through their direct analysis by CE-LIF and subsequent comparative study of the complete cellulose electropherogram by chemometric tools. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish C.; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A.

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1100 to 2500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient ( R2). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R2 value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy.

PubMed

Tewari, Jagdish C; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent

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.

Report: Scientific Software.

ERIC Educational Resources Information Center

Borman, Stuart A.

1985-01-01

Discusses various aspects of scientific software, including evaluation and selection of commercial software products; program exchanges, catalogs, and other information sources; major data analysis packages; statistics and chemometrics software; and artificial intelligence. (JN)

Metabolic fingerprinting of Angelica sinensis during growth using UPLC-TOFMS and chemometrics data analysis

PubMed Central

2013-01-01

Background The radix of Angelica sinensis is widely used as a medicinal herbal and metabolomics research of this plant during growth is necessary. Results Principal component analysis of the UPLC-QTOFMS data showed that these 27 samples could be separated into 4 different groups. The chemical markers accounting for these separations were identified from the PCA loadings plot. These markers were further verified by accurate mass tandem mass and retention times of available reference standards. The study has shown that accumulation of secondary metabolites of Angelica sinensis is closely related to the growth periods. Conclusions The UPLC-QTOFMS based metabolomics approach has great potential for analysis of the alterations of secondary metabolites of Angelica sinensis during growth. PMID:23453085

Quantitative assessment of metal dysregulation in β-thalassemia patients in comparison with healthy controls by ICP-MS and chemometric analyses.

PubMed

Farooq, Sabiha; Mazhar, Wardah; Siddiqui, Amna Jabbar; Ansari, Saqib Hussain; Musharraf, Syed Ghulam

2018-01-31

β-Thalassemia is one of the most common inherited disorders and is widely distributed throughout the world. Owing to severe deficiencies in red blood cell production, blood transfusion is required to correct anemia for normal growth and development but causes additional complications owing to iron overload. The aim of this study is to quantify the biometal dysregulations in β-thalassemia patients as compared with healthy controls. A total of 17 elements were analyzed in serum samples of β-thalassemia patients and healthy controls using ICP-MS followed by chemometric analyses. Out of these analyzed elements, 14 showed a significant difference between healthy and disease groups at p < 0.05 and fold change >3. A PLS-DA model revealed an excellent separation with 89.8% sensitivity and 97.2% specificity and the overall accuracy of the model was 92.2%. This metallomic study revealed that there is major difference in metallomic profiling of β-thalassemia patients specifically in Co, Mn, Ni, V and Ba, whereas the fold changes in Co, Mn, V and Ba were found to be greater than that in Fe, providing evidence that, in addition to Fe, other metals are also altered significantly and therefore chelation therapy for other metals may also needed in β-thalassemia patients. Copyright © 2018 John Wiley & Sons, Ltd.

Qualitative and quantitative analysis of milk for the detection of adulteration by Laser Induced Breakdown Spectroscopy (LIBS).

PubMed

Moncayo, S; Manzoor, S; Rosales, J D; Anzano, J; Caceres, J O

2017-10-01

The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R 2 ) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

PubMed

Riahi, Siavash; Hadiloo, Farshad; Milani, Seyed Mohammad R; Davarkhah, Nazila; Ganjali, Mohammad R; Norouzi, Parviz; Seyfi, Payam

2011-05-01

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories. Copyright © 2011 John Wiley & Sons, Ltd.

Detection of Poisonous Herbs by Terahertz Time-Domain Spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, H.; Li, Z.; Chen, T.; Liu, J.-J.

2018-03-01

The aim of this paper is the application of terahertz (THz) spectroscopy combined with chemometrics techniques to distinguish poisonous and non-poisonous herbs which both have a similar appearance. Spectra of one poisonous and two non-poisonous herbs (Gelsemium elegans, Lonicera japonica Thunb, and Ficus Hirta Vahl) were obtained in the range 0.2-1.4 THz by using a THz time-domain spectroscopy system. Principal component analysis (PCA) was used for feature extraction. The prediction accuracy of classification is between 97.78 to 100%. The results demonstrate an efficient and applicative method to distinguish poisonous herbs, and it may be implemented by using THz spectroscopy combined with chemometric algorithms.

New insight into protein-nanomaterial interactions with UV-visible spectroscopy and chemometrics: human serum albumin and silver nanoparticles.

PubMed

Wang, Yong; Ni, Yongnian

2014-01-21

In recent years, great efforts have focused on the exploration and fabrication of protein nanoconjugates due to potential applications in many fields including bioanalytical science, biosensors, biocatalysis, biofuel cells and bio-based nanodevices. An important aspect of our understanding of protein nanoconjugates is to quantitatively understand how proteins interact with nanomaterials. In this report, human serum albumin (HSA) and citrate-coated silver nanoparticles (AgNPs) are selected as a case study of protein-nanomaterial interactions. UV-visible spectroscopy together with multivariate curve resolution by alternating least squares (MCR-ALS) algorithm is first exploited for the detailed study of AgNPs-HSA interactions. Introduction of the chemometrics tool allows extracting the kinetic profiles, spectra and distribution diagrams of two major absorbing pure species (AgNPs and AgNPs-HSA conjugate). These resolved profiles are then analysed to give the thermodynamic, kinetic and structural information of HSA binding to AgNPs. Transmission electron microscopy, circular dichroism spectroscopy and Fourier transform infrared spectroscopy are used to further characterize the complex system. Moreover, a sensitive spectroscopic biosensor for HSA is fabricated with the MCR-ALS resolved concentration of absorbing pure species. It is found that the linear range for the HSA nanosensor was from 1.9 nM to 45.0 nM with a detection limit of 0.9 nM. It is believed that the proposed method will play an important role in the fabrication and optimization of a robust nanobiosensor or cross-reactive sensors array for the detection and identification of biocomponents.

Is it possible to find presence of lactose in pharmaceuticals? - Preliminary studies by ATR-FTIR spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Banas, A.; Banas, K.; Kalaiselvi, S. M. P.; Pawlicki, B.; Kwiatek, W. M.; Breese, M. B. H.

2017-01-01

Lactose and saccharose have the same molecular formula; however, the arrangement of their atoms is different. A major difference between lactose and saccharose with regard to digestion and processing is that it is not uncommon for individuals to be lactose intolerant (around two thirds of the population has a limited ability to digest lactose after infancy), but it is rather unlikely to be saccharose intolerant. The pharmaceutical industry uses lactose and saccharose as inactive ingredients of drugs to help form tablets because of their excellent compressibility properties. Some patients with severe lactose intolerance may experience symptoms of many allergic reactions after taking medicine that contains this substance. People who are specifically "allergic" to lactose (not just lactose intolerant) should not use tablets containing this ingredient. Fourier Transform Infrared (FTIR) spectroscopy has a unique chemical fingerprinting capability and plays a significant important role in the identification and characterization of analyzed samples and hence has been widely used in pharmaceutical science. However, a typical FTIR spectrum collected from tablets contains a myriad of valuable information hidden in a family of tiny peaks. Powerful multivariate spectral data processing can transform FTIR spectroscopy into an ideal tool for high volume, rapid screening and characterization of even minor tablet components. In this paper a method for distinction between FTIR spectra collected for tablets with or without lactose is presented. The results seem to indicate that the success of identifying one component in FTIR spectra collected for pharmaceutical composition (that is tablet) is largely dependent on the choice of the chemometric technique applied.

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.

Simultaneous spectrophotometric determination of copper, cobalt, nickel and iron in foodstuffs and vegetables with a new bis thiosemicarbazone ligand using chemometric approaches.

PubMed

Rohani Moghadam, Masoud; Poorakbarian Jahromi, Sayedeh Maria; Darehkordi, Ali

2016-02-01

A newly synthesized bis thiosemicarbazone ligand, (2Z,2'Z)-2,2'-((4S,5R)-4,5,6-trihydroxyhexane-1,2-diylidene)bis(N-phenylhydrazinecarbothioamide), was used to make a complex with Cu(2+), Ni(2+), Co(2+) and Fe(3+) for their simultaneous spectrophotometric determination using chemometric methods. By Job's method, the ratio of metal to ligand in Ni(2+) was found to be 1:2, whereas it was 1:4 for the others. The effect of pH on the sensitivity and selectivity of the formed complexes was studied according to the net analyte signal (NAS). Under optimum conditions, the calibration graphs were linear in the ranges of 0.10-3.83, 0.20-3.83, 0.23-5.23 and 0.32-8.12 mg L(-1) with the detection limits of 2, 3, 4 and 10 μg L(-1) for Cu(2+), Co(2+), Ni(2+) and Fe(3+) respectively. The OSC-PLS1 for Cu(2+) and Ni(2+), the PLS1 for Co(2+) and the PC-FFANN for Fe(3+) were selected as the best models. The selected models were successfully applied for the simultaneous determination of elements in some foodstuffs and vegetables. Copyright © 2015 Elsevier Ltd. All rights reserved.

A method based on coffee-ring deposition confocal Raman spectroscopy of analysis of melamine in milk

NASA Astrophysics Data System (ADS)

Tan, Zong; Chen, Da

2016-10-01

In this work, an economical and high-efficiency method for detection of melamine in milk was developed. The enrichment effect of coffee-ring was combined with the micro-region analysis of confocal Raman spectroscopy, in addition, assisted with chemometric algorithmthe. Consequently, a desired result was obtained that the LOD of melamine in this method was 1 ppm, which was excellent because the sensitivity of conventional Raman detection was generally low. Furthermore, the whole process were processed in an easily available condition with almost no chemical reagents consumption, and the chosen substrates for the formation of coffee-ring were reusable. Thus, the method is environmental friendly and has a great potential application in food safety inspection.

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

DOE PAGES

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane; ...

2017-02-07

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

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

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Chemometric approach to evaluate element distribution in muscle, liver and fish bone of roach (Rutilus rutilus), silver bream (Blicca bjoerkna) and crucian carp (Carassius carassius) from Swarzędzkie Lake (Poland) using ICP-MS and FIAS-CVAAS techniques.

PubMed

Chudzińska, Maria; Komorowicz, Izabela; Hanć, Anetta; Gołdyn, Ryszard; Barałkiewicz, Danuta

2016-11-01

The content of elements in fish tissues and organs from Swarzędzkie Lake was investigated in order to evaluate the possible risk associated with their consumption by animals as well as humans. Samples of muscle, liver and fish bone of three fish species; roach (Rutilus rutilus), silver bream (Blicca bjoerkna) and crucian carp (Carassius carassius) were collected from seine catches undertaken as part of the biomanipulation of Swarzędzkie Lake. Element concentration (Al, As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Zn) was determined by inductively coupled plasma mass spectrometry (ICP-MS), with the exception of Hg where the flow injection analysis system cold vapour atomic absorption spectrometry (FIAS-CVAAS) was applied. The study indicated a large variation in the occurrence of the investigated elements in different parts of the fish body. The highest content of Al and Zn was stated in all fish organs for each fish species. The majority of the applied statistical and chemometric methods (e.g., PCA, CA) refer to roach since we had a large number of data for this species. The obtained results were assessed in terms of their accuracy and precision using certified reference material of Fish Muscle ERM BB422.

Antimicrobial Activity of Serbian Propolis Evaluated by Means of MIC, HPTLC, Bioautography and Chemometrics

PubMed Central

Trifković, Jelena; Berić, Tanja; Vovk, Irena; Milojković-Opsenica, Dušanka; Stanković, Slaviša

2016-01-01

New information has come to light about the biological activity of propolis and the quality of natural products which requires a rapid and reliable assessment method such as High Performance Thin-Layer Chromatography (HPTLC) fingerprinting. This study investigates chromatographic and chemometric approaches for determining the antimicrobial activity of propolis of Serbian origin against various bacterial species. A linear multivariate calibration technique, using Partial Least Squares, was used to extract the relevant information from the chromatographic fingerprints, i.e. to indicate peaks which represent phenolic compounds that are potentially responsible for the antimicrobial capacity of the samples. In addition, direct bioautography was performed to localize the antibacterial activity on chromatograms. The biological activity of the propolis samples against various bacterial species was determined by a minimum inhibitory concentration assay, confirming their affiliation with the European poplar type of propolis and revealing the existence of two types (blue and orange) according to botanical origin. The strongest antibacterial activity was exhibited by sample 26 against Staphylococcus aureus, with a MIC value of 0.5 mg/mL, and Listeria monocytogenes, with a MIC as low as 0.1 mg/mL, which was also the lowest effective concentration observed in our study. Generally, the orange type of propolis shows higher antimicrobial activity compared to the blue type. PLS modelling was performed on the HPTLC data set and the resulting models might qualitatively indicate compounds that play an important role in the activity exhibited by the propolis samples. The most relevant peaks influencing the antimicrobial activity of propolis against all bacterial strains were phenolic compounds at RF values of 0.37, 0.40, 0.45, 0.51, 0.60 and 0.70. The knowledge gained through this study could be important for attributing the antimicrobial activity of propolis to specific chemical

The Influence of the Variety, Vineyard, and Vintage on the Romanian White Wines Quality

PubMed Central

Hosu, Anamaria; Floare-Avram, Veronica; Feher, Ioana; Inceu, Mihai

2016-01-01

The wine is one of the most consumed drinks over the world, being subjected to falsification or adulteration regarding the variety, vintage, and geographical region. In this study, the influence of different characteristics of wines (type, production year, and origin) on the total phenolic content, total flavonoids content, antioxidant activity, total sugars content, pH, and 18O/16O isotopic ratio was investigated. The differentiation of selected wines on the basis of tested parameters was investigated using chemometric techniques, such as analysis of variance, cluster analysis, and principal component analysis. The experimental results are in agreement with other outcomes and allow concluding that variety and vineyard have the major influence on the studied parameters, but, also, statistical interaction effect between year and vineyard and year and variety is observed in some cases. The obtained results have demonstrated that these parameters together with chemometric techniques show a significant potential to be used for discrimination of white wines. PMID:27840767

Multiple fingerprinting analyses in quality control of Cassiae Semen polysaccharides.

PubMed

Cheng, Jing; He, Siyu; Wan, Qiang; Jing, Pu

2018-03-01

Quality control issue overshadows potential health benefits of Cassiae Semen due to the analytic limitations. In this study, multiple-fingerprint analysis integrated with several chemometrics was performed to assess the polysaccharide quality of Cassiae Semen harvested from different locations. FT-IR, HPLC, and GC fingerprints of polysaccharide extracts from the authentic source were established as standard profiles, applying to assess the quality of foreign sources. Analyses of FT-IR fingerprints of polysaccharide extracts using either Pearson correlation analysis or principal component analysis (PCA), or HPLC fingerprints of partially hydrolyzed polysaccharides with PCA, distinguished the foreign sources from the authentic source. However, HPLC or GC fingerprints of completely hydrolyzed polysaccharides couldn't identify all foreign sources and the methodology using GC is quite limited in determining the monosaccharide composition. This indicates that FT-IR/HPLC fingerprints of non/partially-hydrolyzed polysaccharides, respectively, accompanied by multiple chemometrics methods, might be potentially applied in detecting and differentiating sources of Cassiae Semen. Copyright © 2018 Elsevier B.V. All rights reserved.

A Comparison of Raman Spectral Features of Frozen and Deparaffinized Tissues in Neuroblastoma and Ganglioneuroma

NASA Astrophysics Data System (ADS)

Devpura, Suneetha; Thakur, Jagdish S.; Poulik, Janet M.; Rabah, Raja; Naik, Vaman M.; Naik, Ratna

2012-02-01

We have investigated the cellular regions in neuroblastoma and ganglioneuroma using Raman spectroscopy and compared their spectral characteristics with those of normal adrenal gland. Thin sections from both frozen and deparaffinized tissues, obtained from the same tissue specimen, were studied in conjunction with the pathological examination of the tissues. We found a significant difference in the spectral features of frozen sections of normal adrenal gland, neuroblastoma, and ganglioneuroma when compared to deparaffinized tissues. The quantitative analysis of the Raman data using chemometric methods of principal component analysis and discriminant function analysis obtained from the frozen tissues show a sensitivity and specificity of 100% each. The biochemical identification based on the spectral differences shows that the normal adrenal gland tissues have higher levels of carotenoids, lipids, and cholesterol compared to the neuroblastoma and ganglioneuroma frozen tissues. However, deparaffinized tissues show complete removal of these biochemicals in adrenal tissues. This study demonstrates that Raman spectroscopy combined with chemometric methods can successfully distinguish neuroblastoma and ganglioneuroma at cellular level.

Differentiation of Candida albicans, Candida glabrata, and Candida krusei by FT-IR and chemometrics by CHROMagar™ Candida.

PubMed

Wohlmeister, Denise; Vianna, Débora Renz Barreto; Helfer, Virginia Etges; Calil, Luciane Noal; Buffon, Andréia; Fuentefria, Alexandre Meneghello; Corbellini, Valeriano Antonio; Pilger, Diogo André

2017-10-01

Pathogenic Candida species are detected in clinical infections. CHROMagar™ is a phenotypical method used to identify Candida species, although it has limitations, which indicates the need for more sensitive and specific techniques. Infrared Spectroscopy (FT-IR) is an analytical vibrational technique used to identify patterns of metabolic fingerprint of biological matrixes, particularly whole microbial cell systems as Candida sp. in association of classificatory chemometrics algorithms. On the other hand, Soft Independent Modeling by Class Analogy (SIMCA) is one of the typical algorithms still little employed in microbiological classification. This study demonstrates the applicability of the FT-IR-technique by specular reflectance associated with SIMCA to discriminate Candida species isolated from vaginal discharges and grown on CHROMagar™. The differences in spectra of C. albicans, C. glabrata and C. krusei were suitable for use in the discrimination of these species, which was observed by PCA. Then, a SIMCA model was constructed with standard samples of three species and using the spectral region of 1792-1561cm -1 . All samples (n=48) were properly classified based on the chromogenic method using CHROMagar™ Candida. In total, 93.4% (n=45) of the samples were correctly and unambiguously classified (Class I). Two samples of C. albicans were classified correctly, though these could have been C. glabrata (Class II). Also, one C. glabrata sample could have been classified as C. krusei (Class II). Concerning these three samples, one triplicate of each was included in Class II and two in Class I. Therefore, FT-IR associated with SIMCA can be used to identify samples of C. albicans, C. glabrata, and C. krusei grown in CHROMagar™ Candida aiming to improve clinical applications of this technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization of Brazilian lager and brown ale beers based on color, phenolic compounds, and antioxidant activity using chemometrics.

PubMed

Granato, Daniel; Branco, Gabriel Favalli; Faria, José de Assis Fonseca; Cruz, Adriano Gomes

2011-02-01

Epidemiological studies have shown that beer has positive effects on inhibiting atherosclerosis, decreasing the content of serum low-density lipoprotein cholesterol and triglycerides, by acting as in vivo free radical scavenger. In this research, the antioxidant activity of commercial Brazilian beers (n = 29) was determined by the oxygen radical absorbance capacity (ORAC) and 1,1-diphenyl-2-picrylhydrazyl (DPPH(·) ) assays and results were analyzed by chemometrics. The brown ale samples (n = 11) presented higher (P < 0.05) flavonoids (124.01 mg L(-1) ), total phenolics (362.22 mg L(-1) ), non-flavonoid phenolics (238.21 mg L(-1) ), lightness (69.48), redness (35.75), yellowness (55.71), color intensity (66.86), hue angle (59.14), color saturation (0.9620), DPPH(·) values (30.96% inhibition), and ORAC values (3, 659.36 µmol Trolox equivalents L(-1) ), compared to lager samples (n = 18). Brown ale beers presented higher antioxidant properties (P < 0.05) measured by ORAC (1.93 times higher) and DPPH (1.65 times higher) compared to lager beer. ORAC values correlated well with the content of flavonoids (r = 0.47; P = 0.01), total phenolic compounds (r = 0.44; P < 0.01) and DPPH (r = 0.67; P < 0.01). DPPH values also correlated well to the content of flavonoids (r = 0.69; P < 0.01), total phenolic compounds (r = 0.60; P < 0.01), and non-flavonoid compounds (r = 0.46; P = 0.01). The results suggest that brown ale beers, and less significantly lager beers, could be sources of bioactive compounds with suitable free radical scavenging properties. 2010 Society of Chemical Industry.

Investigation of hydrogenation of toluene to methylcyclohexane in a trickle bed reactor by low-field nuclear magnetic resonance spectroscopy.

PubMed

Guthausen, Gisela; von Garnier, Agnes; Reimert, Rainer

2009-10-01

Low-field nuclear magnetic resonance (NMR) spectroscopy is applied to study the hydrogenation of toluene in a lab-scale reactor. A conventional benchtop NMR system was modified to achieve chemical shift resolution. After an off-line validity check of the approach, the reaction product is analyzed on-line during the process, applying chemometric data processing. The conversion of toluene to methylcyclohexane is compared with off-line gas chromatographic analysis. Both classic analytical and chemometric data processing was applied. As the results, which are obtained within a few tens of seconds, are equivalent within the experimental accuracy of both methods, low-field NMR spectroscopy was shown to provide an analytical tool for reaction characterization and immediate feedback.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines.

PubMed

Giménez-Gómez, Pablo; Escudé-Pujol, Roger; Capdevila, Fina; Puig-Pujol, Anna; Jiménez-Jorquera, Cecilia; Gutiérrez-Capitán, Manuel

2016-10-27

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na⁺, K⁺, Ca 2+ , Cl - , and CO₃ 2- , one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines

PubMed Central

Giménez-Gómez, Pablo; Escudé-Pujol, Roger; Capdevila, Fina; Puig-Pujol, Anna; Jiménez-Jorquera, Cecilia; Gutiérrez-Capitán, Manuel

2016-01-01

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na+, K+, Ca2+, Cl−, and CO32−, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions. PMID:27801796

Characterization of ancient glass excavated in Enez (Ancient Ainos) Turkey by combined Instrumental Neutron Activation Analysis and Fourier Transform Infrared spectrometry techniques

NASA Astrophysics Data System (ADS)

Akyuz, Sevim; Akyuz, Tanil; Mukhamedshina, Nuranya M.; Mirsagatova, A. Adiba; Basaran, Sait; Cakan, Banu

2012-05-01

Ancient glass fragments excavated in the archaeological district Enez (Ancient Ainos)-Turkey were investigated by combined Instrumental Neutron Activation Analysis (INAA) and Fourier Transform Infrared (FTIR) spectrometry techniques. Multi-elemental contents of 15 glass fragments that belong to Hellenistic, Roman, Byzantine, and Ottoman Periods, were determined by INAA. The concentrations of twenty six elements (Na, K, Ca, Sc, Cr, Mn, Fe, Co, Cu, Zn, As, Rb, Sr, Sb, Cs, Ba, Ce, Sm, Eu, Tb, Yb, Lu, Hf, Ta, Au and Th), which might be present in the samples as flux, stabilizers, colorants or opacifiers, and impurities, were examined. Chemometric treatment of the INAA data was performed and principle component analysis revealed presence of 3 distinct groups. The thermal history of the glass samples was determined by FTIR spectrometry.

Chemical profiling and quantification of monacolins and citrinin in red yeast rice commercial raw materials and dietary supplements using liquid chromatography-accurate QToF mass spectrometry: Chemometrics application.

PubMed

Avula, Bharathi; Cohen, Pieter A; Wang, Yan-Hong; Sagi, Satyanarayanaraju; Feng, Wei; Wang, Mei; Zweigenbaum, Jerry; Shuangcheng, Ma; Khan, Ikhlas A

2014-11-01

Red yeast rice (RYR) is prepared by fermenting rice with various strains of the yeast Monascus spp of the Aspergillaceae family. Depending on the Monascus strains and the fermentation conditions, the products may contain monacolins, pigments and citrinin as secondary metabolites. Authentic and commercial RYR samples were analyzed using UHPLC-DAD-QToF-MS for monacolins, pigments and citrinin. A separation by UHPLC was achieved using a reversed-phase column and a gradient of water/acetonitrile each containing formic acid as the mobile phase. Accurate mass QToF spectrometry was used to distinguish isobaric monacolins. Principle component analysis (PCA), a chemometric technique was used to discriminate between authentic RYR, commercial RYR raw materials and dietary supplements. Three authentic RYR samples, 31 commercial RYR raw materials and 14 RYR dietary supplements were analyzed. Monacolin K content in 600mg of authentic RYR samples ranged from 1.2mg to 1.38mg. Amounts of monacolin K in dietary supplements labeled as containing 600mg of RYR varied more than 40-fold from 0.03mg to 2.18mg. Monacolin K content of dietary supplements labeled as containing 1200mg RYR varied more than 20-fold from 0.22mg to 5.23mg. In addition to large variations in quantity of monacolin K found in dietary supplements, RYR dietary supplements contained ratios of monacolins that differed significantly from authentic samples. The results indicated that RYR commercial products are of variable quality and the analytical method is suitable for quality control testing of a variety of RYR products. Copyright © 2014 Elsevier B.V. All rights reserved.

A statistical approach to determine fluxapyroxad and its three metabolites in soils, sediment and sludge based on a combination of chemometric tools and a modified quick, easy, cheap, effective, rugged and safe method.

PubMed

Li, Shasha; Liu, Xingang; Zhu, Yulong; Dong, Fengshou; Xu, Jun; Li, Minmin; Zheng, Yongquan

2014-09-05

An effective method for the quantification of fluxapyroxad and its three metabolites in soils, sediment and sludge was developed using ultrahigh performance chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Both the extraction and clean-up steps of the QuEChERS procedure were optimised using a chemometric tool, which was expected to facilitate the rapid analysis with minimal procedures. Several operating parameters (MeCN/acetic acid ratio in the extraction solution (i.e., acetic acid percentage), water volume, extraction time, PSA amount, C18 amount, and GCB amount) were investigated using a Plackett-Burman (P-B) screening design. Afterward, the significant factors (acetic acid percentage, water volume, and PSA amount) obtained were optimised using central composite design (CCD) combined with the desirability function (DF) to determine the optimum experimental conditions. The optimised procedure provides high-level linearity for all studied compounds with correlation coefficients ranging between 0.9972 and 0.9999. The detection limits were in the range of 0.1 to 1.0μg/kg and the limits of quantitation (LOQs) were between 0.5 and 3.4μg/kg with relative standard deviations (RSD) between 2.3% and 9.6% (n=6). Therefore, the developed protocol can serve as a simple and sensitive tool for monitoring fluxapyroxad and its three metabolites in soil, sediment and sludge samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Chemical Fingerprint and Quantitative Analysis for the Quality Evaluation of Platycladi cacumen by Ultra-performance Liquid Chromatography Coupled with Hierarchical Cluster Analysis.

PubMed

Shan, Mingqiu; Li, Sam Fong Yau; Yu, Sheng; Qian, Yan; Guo, Shuchen; Zhang, Li; Ding, Anwei

2018-01-01

Platycladi cacumen (dried twigs and leaves of Platycladus orientalis (L.) Franco) is a frequently utilized Chinese medicinal herb. To evaluate the quality of the phytomedcine, an ultra-performance liquid chromatographic method with diode array detection was established for chemical fingerprinting and quantitative analysis. In this study, 27 batches of P. cacumen from different regions were collected for analysis. A chemical fingerprint with 20 common peaks was obtained using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A). Among these 20 components, seven flavonoids (myricitrin, isoquercitrin, quercitrin, afzelin, cupressuflavone, amentoflavone and hinokiflavone) were identified and determined simultaneously. In the method validation, the seven analytes showed good regressions (R ≥ 0.9995) within linear ranges and good recoveries from 96.4% to 103.3%. Furthermore, with the contents of these seven flavonoids, hierarchical clustering analysis was applied to distinguish the 27 batches into five groups. The chemometric results showed that these groups were almost consistent with geographical positions and climatic conditions of the production regions. Integrating fingerprint analysis, simultaneous determination and hierarchical clustering analysis, the established method is rapid, sensitive, accurate and readily applicable, and also provides a significant foundation for quality control of P. cacumen efficiently. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

PubMed

Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O

2017-03-05

The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized

Recent advances in the use of NIR spectroscopy for qualitative control and protection of extra virgin olive oil

USDA-ARS?s Scientific Manuscript database

Recent studies on the use of near infrared (NIR) spectroscopy for the qualitative characterization of extra virgin olive oil, are reported and discussed in this paper. Research results confirms that NIR spectroscopy, combined with chemometric data analysis, allows to simultaneously evaluate all qual...

Geographic authentication of Asian rice (Oryza sativa L.) using multi-elemental and stable isotopic data combined with multivariate analysis.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Lee, Kyoung-Jin; Park, Sung-Kyu; Lee, Ji-Hee; Son, Na-Young; Jin, Yong-Ik; Kim, Seung-Hyun

2018-02-01

Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged asan important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice of six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were δ 34 S, Mn, and Mg. Our findings contribute to understanding the variations of elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

ComDim for explorative multi-block data analysis of Cantal-type cheeses: Effects of salts, gentle heating and ripening.

PubMed

Loudiyi, M; Rutledge, D N; Aït-Kaddour, A

2018-10-30

Common Dimension (ComDim) chemometrics method for multi-block data analysis was employed to evaluate the impact of different added salts and ripening times on physicochemical, color, dynamic low amplitude oscillatory rheology, texture profile, and molecular structure (fluorescence and MIR spectroscopies) of five Cantal-type cheeses. Firstly, Independent Components Analysis (ICA) was applied separately on fluorescence and MIR spectra in order to extract the relevant signal source and the associated proportions related to molecular structure characteristics. ComDim was then applied on the 31 data tables corresponding to the proportion of ICA signals obtained for spectral methods and the global analysis of cheeses by the other techniques. The ComDim results indicated that generally cheeses made with 50% NaCl or with 75:25% NaCl/KCl exhibit the equivalent characteristics in structural, textural, meltability and color properties. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different techniques describe the same samples. Copyright © 2018 Elsevier Ltd. All rights reserved.

Decorin content and near infrared spectroscopy analysis of dried collagenous biomaterial samples.

PubMed

Aldema-Ramos, Mila L; Castell, Joan Carles; Muir, Zerlina E; Adzet, Jose Maria; Sabe, Rosa; Schreyer, Suzanne

2012-12-14

The efficient removal of proteoglycans, such as decorin, from the hide when processing it to leather by traditional means is generally acceptable and beneficial for leather quality, especially for softness and flexibility. A patented waterless or acetone dehydration method that can generate a product similar to leather called Dried Collagenous Biomaterial (known as BCD) was developed but has no effect on decorin removal efficiency. The Alcian Blue colorimetric technique was used to assay the sulfated glycosaminoglycan (sGAG) portion of decorin. The corresponding residual decorin content was correlated to the mechanical properties of the BCD samples and was comparable to the control leather made traditionally. The waterless dehydration and instantaneous chrome tanning process is a good eco-friendly alternative to transforming hides to leather because no additional effects were observed after examination using NIR spectroscopy and additional chemometric analysis.

Decorin Content and Near Infrared Spectroscopy Analysis of Dried Collagenous Biomaterial Samples

PubMed Central

Aldema-Ramos, Mila L.; Castell, Joan Carles; Muir, Zerlina E.; Adzet, Jose Maria; Sabe, Rosa; Schreyer, Suzanne

2012-01-01

The efficient removal of proteoglycans, such as decorin, from the hide when processing it to leather by traditional means is generally acceptable and beneficial for leather quality, especially for softness and flexibility. A patented waterless or acetone dehydration method that can generate a product similar to leather called Dried Collagenous Biomaterial (known as BCD) was developed but has no effect on decorin removal efficiency. The Alcian Blue colorimetric technique was used to assay the sulfated glycosaminoglycan (sGAG) portion of decorin. The corresponding residual decorin content was correlated to the mechanical properties of the BCD samples and was comparable to the control leather made traditionally. The waterless dehydration and instantaneous chrome tanning process is a good eco-friendly alternative to transforming hides to leather because no additional effects were observed after examination using NIR spectroscopy and additional chemometric analysis. PMID:24970152

Application of an e-tongue to the analysis of monovarietal and blends of white wines.

PubMed

Gutiérrez, Manuel; Llobera, Andreu; Ipatov, Andrey; Vila-Planas, Jordi; Mínguez, Santiago; Demming, Stefanie; Büttgenbach, Stephanus; Capdevila, Fina; Domingo, Carme; Jiménez-Jorquera, Cecilia

2011-01-01

This work presents a multiparametric system capable of characterizing and classifying white wines according to the grape variety and geographical origin. Besides, it quantifies specific parameters of interest for quality control in wine. The system, known as a hybrid electronic tongue, consists of an array of electrochemical microsensors-six ISFET based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, a gold microelectrode and a microelectrode for sensing electrochemical oxygen demand--and a miniaturized optofluidic system. The test sample set comprised eighteen Catalan monovarietal white wines from four different grape varieties, two Croatian monovarietal white wines and seven bi- and trivarietal mixtures prepared from the Catalan varieties. Different chemometric tools were used to characterize (i.e., Principal Component Analysis), classify (i.e., Soft Independent Modeling Class Analogy) and quantify (i.e., Partial-Least Squares) some parameters of interest. The results demonstrate the usefulness of the multisensor system for analysis of wine.