Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

Metabolic analysis of the antidepressive effects of Yangxinshi Tablet in a vascular depression model in mice

In recent years, vascular depression has become the focus of international attention. Yangxinshi Tablet (YXST) is usually used in cthe linic for the treatment of arrhythmia and heart failure, but we found that it also has antidepressive effects. The objective of the study was to identify biomarkers related to vascular depression in hippocampus and explore the antidepressive effects of YXST on the mouse model. Untargeted metabolomics based on UHPLC‐Q‐TOF/MS was applied to identify significantly differential biomarkers between the model group and control group. Unsupervised principal component analysis (PCA) was used to scan the tendency of groups and partial least squares‐discriminant analysis (PLS‐DA) to distinguish between the vascular depressive mice and the sham. PCA stores showed clear differences in metabolism between the vascular depressive mice and sham groups. The PLS‐DA model exhibited 38 metabolites as the biomarkers to distinguish the vascular depressive mice and the sham. Further, YXST significantly regulated 22 metabolites to normal levels. The results suggested that YXST has a comprehensive antidepressive effect on vascular depression via regulation of multiple metabolic pathways including amino acid, the tricarboxylic acid cycle and phosphoglyceride metabolisms. These findings provide insight into the pathophysiological mechanism underlying vascular depression and the mechanism of YXST.     

Characterization of Gasoline by Raman Spectroscopy with Chemometric Analysis

A rapid Raman spectroscopy protocol is reported to classify gasoline according to its distributor and to identify and quantify common adulterants. Gasoline from three distributors was collected from 19 stations in São Paulo, Brazil. Principal component analysis (PCA) showed specific clusters for each distributor, and partial least squares discriminant analysis (PLS-DA) correctly identified the origin of the samples. To evaluate the technique for the identification and quantification of the adulterants, authentic samples from each distributor were fortified at levels from 2.5 up to 25.0% (v/v) using ethanol, methanol, toluene, and turpentine to obtain 120 altered samples. PCA showed clear separation among the samples with the adulterants and PLS-DA precisely identified the adulterants (478 in 480 predictions by cross-validation), irrespective of the distributor and the concentration. One classification model was used to characterize all distributors. To quantify the adulterants, 36 multivariate calibration models were constructed using partial least squares (PLS), interval PLS, and PLS genetic algorithm for each distributor and for each adulterant. Cross-validation errors of less than 5.0% were obtained for all adulterants regardless of the distributor. Raman spectroscopy and multivariate analysis were shown to be powerful for rapid and inexpensive for the characterization of gasoline origin and the identification and quantification of common adulterants.     

Chemometric analysis of metabolism disorders in blood plasma of S180 and H22 tumor‐bearing mice by high performance liquid chromatography‐diode array detection

The aim of this paper is to characterize metabolism disorders in Kunming mice induced by S180 and H22 tumor cells. Metabolic fingerprint based on high performance liquid chromatography‐diode array detector (HPLC‐DAD) was developed to map the disturbed metabolic responses. In vivo testing of the antitumor activity of paclitaxel (Taxol) was carried out by inhibiting the growth of S180 and H22 tumor cells. Based on 27 common peaks, principal component analysis (PCA) and partial least squares‐discriminant analysis (PLS‐DA) were used to distinguish the abnormal from control and to find significant endogenous compounds (SECs) which have significant contributions to classification. The tumor growth inhibition ratios (TIRs) of Taxol groups were used to validate the predictive accuracies of the PLS‐DA models. The predictive accuracies of PLS‐DA models for S180 and H22 tumor model groups were 97.6 and 100%, respectively. Nine (S180) and seven (H22) SECs were discovered, including uric acid and cytidine. In addition, the correlations between relative tumor weights (RTWs) and chromatographic data for the SECs were significant (p < 0.05). Investigations on the stability and precision of the established metabolic fingerprints demonstrate that the experiment is well controlled and reliable. This work shows that the platform of HPLC‐DAD coupled with chemometric methods provides a promising method for the study of metabolism disorders induced by tumor cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

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

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar‐baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra‐high‐performance liquid chromatography with quadruple time‐of‐flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square‐discriminant analysis was developed to holistically compare the difference between raw and vinegar‐baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b₃, saikosaponin e, saikosaponin c, saikosaponin b₂, saikosaponin b₁, 4′′‐O‐acetyl‐saikosaponin d, hyperoside and 3′,4′‐dimethoxy quercetin were explored as potential markers of raw and vinegar‐baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar‐processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling.     

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

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

Powered partial least squares discriminant analysis

From the fundamental parts of PLS‐DA, Fisher's canonical discriminant analysis (FCDA) and Powered PLS (PPLS), we develop the concept of powered PLS for classification problems (PPLS‐DA). By taking advantage of a sequence of data reducing linear transformations (consistent with the computation of ordinary PLS‐DA components), PPLS‐DA computes each component from the transformed data by maximization of a parameterized Rayleigh quotient associated with FCDA. Models found by the powered PLS methodology can contribute to reveal the relevance of particular predictors and often requires fewer and simpler components than their ordinary PLS counterparts. From the possibility of imposing restrictions on the powers available for optimization we obtain an explorative approach to predictive modeling not available to the traditional PLS methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Classification of Rhizoma et Radix Notoperygii by HPLC Fingerprints with the Aid of Chemometrics

Chromatographic profiles of Rhizoma et Radix Notoperygii (RRN, “Qianghuo” in Chinese), a complex traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography with diode array detection (HPLC-DAD) at 330 nm. These data profiles were used as fingerprints to investigate quality control classification modeling of the RRN samples. In contrast to the classical methods for discrimination of TCMs, that is, just using common HPLC peaks, all chromatographic profile data were pre-processed by the correlation optimized warping method and polynomial functions; then, these data were submitted as fingerprints (variables) for classification on the basis of sample origin. Chemometrics methods used for calibration modeling and subsequent sample classification-least square support vector machine (LS-SVM), artificial neural network (ANN), and partial least square discriminant analysis (PLS-DA); all produced satisfactory calibrations as well as classification results.     

Quality assessment of radix salviae miltiorrhizae

This paper describes an improved quality assessment method for Radix Salviae Miltiorrhizae (Root of Salvia miltiorrhiza BGE.) which was established using chromatographic fingerprinting and quantification of multiple marker compounds in the crude drug. High-performance thin-layer chromatography (HPTLC) fingerprinting of water-soluble phenolics and nonpolar tanshinones was performed separately and the authentication of Radix Salviae Miltiorrhizae was achieved by comparing the fingerprints of the samples with those of the reference crude drug and by comparing the Rf values of the bands in TLC fingerprints with those of reference compounds. HPLC fingerprints were obtained by simultaneous separation of phenolics and diterpenoids in Radix Salviae Miltiorrhizae. The HPLC fingerprints of seven batches of samples from different regions of China showed similar chromatographic patterns, and seven peaks were selected as characteristic peaks. The relative retention time of these characteristic peaks in the HPLC fingerprints was established as an important parameter for the identification of this herbal medicine. The pharmacologically active marker compounds salvianolic acid B, rosmarinic acid, and tanshinone IIA in herbal medicine were quantitatively determined using reverse-phase HPLC techniques. The HPLC quantitation methods of the three marker compounds were validated and the measurement uncertainty, which is important for setting the proposed content limit of the marker compounds in herbal medicine, were further evaluated.     

Determination of residual oil in diesel oil by spectrofluorimetric and chemometric analysis

Multivariate calibration (PLS), principal components analysis (PCA) and linear discriminant analysis (LDA), associated to synchronous spectrofluorimetry, were used to identify and quantify non-transesterified residual vegetable oil in diesel oil with the addition of 2% of biodiesel (B2). The addition of residual oil, one of the easiest ways of adultering fuel, damages engines and leads to tax evasion. Using this method, the samples of diesel oil, B2, and B2 contaminated with residual oil were classified correctly and separated into three well-defined groups. The quantification of residual oil in B2 was carried out in the 0-25% (w/w) band, RMSEC and RMSEP values ranging from 0.26 to 0.48% (w/w) and 1.6-2.6% (w/w), respectively. The method is highly sensitive and efficient to identify and quantify this type of adulterant in which 100% of the samples were correctly classified and the average relative error was approximately 4% in the range 0.5-25% (w/w).     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Spectrum-effect relationship between fingerprints and hemopoietic effects of small molecular fraction of Polygoni Multiflori radix praeparata

Polygoni multiflori Radix Praeparata (PMRP) is a traditional medicine used for nourishing essence and blood in China. However, it is unclear which PMRP compounds are responsible for its hematopoietic effect. In this study, spectrum-effect relationship was used to discovery potential hematopoietic compounds. The fingerprints of 20 PMRP batches were established by HPLC and the hematopoietic effect was determined using red blood cell, hemoglobin, hematocrit, and platelet indexes in aplastic anemia model mice. The spectrum-effect relationship between common peaks and hematopoietic efficacy values was established using gray relational analysis and partial least squares analysis. Spectrum-effect relationship results showed that peaks 21 (emodin-8-O-(6´-O-acetyl)-β-D-glucoside), 15 (2, 3, 5, 4′-tetrahydroxystilbene-2-O-di-glucoside), 16 (cis-2,3,5,4′-tetrahydroxy-stilbene-2-O-β-D-glucoside), 11 (unknown), 20(unknown, 12 (epicatechin), 29 (carboxyl emodin), and 31 (emodin) in the fingerprints were closely related to the hematopoietic effect. This work successfully established the spectrum-effect relationship between PMRP hematopoietic effect and its fingerprints, which can be used to explain the material basis for the PMRP hematopoietic effect.     

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.     

Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra

Fourier transform infrared spectroscopy (FTIR) has been studied many times in the context of identification of plant, fungal and bacterial species. Infrared spectra are commonly analyzed using multivariate statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least squares analysis (PLS) and discriminant analysis (DA). In this study, a univariate statistical method for analysis of variance (ANOVA) was used to reduce the number of variables before applying the multivariate methods. Analyzing variables using ANOVA or a combination of ANOVA with CA produced better results. Here, experiments were carried out by performing ANOVA using the first derivative of the spectra instead of the original spectra or its second derivative because using the first‐derivative variables led to improved distinction between species. Different results were obtained by applying different validation methods. The leave‐one‐out validation method gave higher results than the validation‐with‐training and validation sample sets, thus indicating the non‐objectivity of the leave‐one‐out validation method. Copyright © 2010 John Wiley & Sons, Ltd.     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.