Prediction of total green tea antioxidant capacity from chromatograms by multivariate modeling

In this paper, a fast strategy for determining the total antioxidant capacity of Chinese green tea extracts is developed. This strategy includes the use of experimental techniques, such as fast high-performance liquid chromatography (HPLC) on monolithic columns and a spectrophotometric approach to determine the total antioxidant capacity of the extracts. To extract the chemically relevant information from the obtained data, chemometrical approaches are used. Among them there are correlation optimized warping (COW) to align the chromatograms, robust principal component analysis (robust PCA) to detect outliers, and partial least squares (PLS) and uninformative variable elimination partial least squares (UVE-PLS) to construct a reliable multivariate regression model to predict the total antioxidant capacity from the fast chromatograms.     

Exploration of linear multivariate calibration techniques to predict the total antioxidant capacity of green tea from chromatographic fingerprints

Nowadays fingerprinting is a generally applied technique for the identification and quality assessment of herbal products. In this study it was aimed to predict a quantitative property, the antioxidant capacity of green tea, from chromatographic fingerprints. Different linear multivariate calibration techniques, commonly applied on spectral data, were explored and compared. When the chromatograms were appropriately pretreated, all tested techniques were able to predict the total antioxidant capacity with a precision comparable to that of the reference method (Trolox equivalent antioxidant capacity assay). Stepwise multiple linear regression (MLR) however is less recommended because of inadequate variable selection. Principal components regression (PCR) also seems less preferable, because large variations not correlated with the total antioxidant capacity were also included in the model. This problem does not occur with partial least squares (PLS) models. Of all tested PLS methods, orthogonal projections to latent structures (O-PLS) was preferred because of its simplicity, reproducibility, good interpretability of the compounds' contribution to the antioxidant capacity and its good predictive and describing abilities.     

Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)

This paper indicates the possibility to use near infrared spectroscopy (NIR) combined with PLS as a rapid method to estimate the quality of green tea. NIR is used to build calibration models to predict the content of caffeine, epigallocatechin gallate (EGCG) and epicatechin (EC) and for the prediction of the total antioxidant capacity of green tea. For the determination of the total antioxidant capacity, the trolox equivalent antioxidant capacity (TEAC) method is used. Until now, the prediction of the antioxidant capacity as such by use of NIR has not been reported. For caffeine and TEAC, models are build for the whole green tea leaves and also for the ground leaves. For the polyphenols (EGCG and EC), only models for the whole leaves are investigated. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV) for the training set is chosen. The correlation coefficient (r) between the predicted and the reference results for the test set is used as an evaluation parameter for the models: for the TEAC results r=0.90 for the model with the whole leaves, r=0.86 for the model with the powdered leaves are obtained. The caffeine prediction model has a correlation coefficient r=0.96 for the whole leaves and r=0.93 for the ground leaves. The correlation coefficient for the EGCG and the EC content models are, respectively 0.83 and 0.44.     

Quantification of naphthalenes in jet fuel with GC x GC/Tri-PLS and windowed rank minimization retention time alignment

Comprehensive, two-dimensional gas chromatography (GC x GC) is used in conjunction with trilinear partial least squares (Tri-PLS) to quantify the percent weight of naphthalenes (two-ring aromatic compounds) in jet fuel samples. The increased peak capacity and selectivity of GC x GC makes the technique attractive for the rapid, and possibly less tedious analysis of jet fuel. The analysis of complex mixtures by GC x GC is further enhanced through the use of chemometric techniques, including those designed for use on 2-D data such as Tri-PLS. Unfortunately, retention time variation, unless corrected, can be an impediment to chemometric analysis. Previous work has demonstrated that the effects of retention time variation can be mitigated in sub-regions of GC x GC chromatograms through the application of an objective retention time alignment algorithm based on rank minimization. Building upon this previous work, it is demonstrated here that the effects of retention time variation can be mitigated throughout an entire GC x GC chromatogram with an objective retention time alignment algorithm based on windowed rank minimization alignment. A significant decrease in calibration error is observed when the algorithm is applied to chromatograms prior to construction of Tri-PLS models. Fourteen jet fuel samples with known weight percentages of naphthalenes (ASTM D1840) were obtained. Each sample was subjected to five replicate five-minute GC x GC separations over a period of two days. A subset of nine samples spanning the range of weight percentages of naphthalenes was chosen as a calibration set and Tri-PLS calibration models were subsequently developed in order to predict the naphthalene content of the samples from the GC x GC chromatograms of the remaining five samples. Calibration models constructed from GC x GC chromatograms that were retention time corrected are shown to exhibit a root mean square error of prediction of roughly half that of calibration models constructed from uncorrected chromatograms. The error of prediction is lowered further to a value that nearly matches the uncertainty in the standard percent weight values (ca. 1% of the median percent volume value) when the aligned chromatograms are truncated to include only regions of the chromatogram populated by naphthalenes and compounds of similar polarity and boiling point.     

Orthogonal signal correction‐based prediction of total antioxidant activity using partial least squares regression from chromatograms

The multivariate calibration methods—partial least squares (PLS), orthogonal signal correction and partial least squares (OSC‐PLS)—were employed for the prediction of total antioxidant activities of four Prunella L. species. High‐performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total antioxidant activity of the Prunella L. samples. Several preprocessing techniques such as smoothing and normalization were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping. The importance of the preprocessing was investigated by calculating the root mean square error for the calibration set for the total antioxidant activity of Prunella L. samples. The models developed on the basis of the preprocessed data were able to predict the total antioxidant activity with a precision comparable to that of the reference 2,2‐azino‐di‐(3‐ethylbenzothialozine‐sulfonic acid) and 2,2‐diphenyl‐1‐picrylhydrazyl methods. The OSC‐PLS model seems preferable because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total antioxidant activity. The contribution of individual phenolic compounds to the total antioxidant activity was identified by HPLC. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

The present study demonstrated the possibility of utilizing the ytterbium (Yb)‐based internal standard near‐infrared (NIR) spectroscopic measurement technique coupled with multivariate calibration for quantitative analysis of tea, including total free amino acids and total polyphenols in tea. Yb is a rare earth element aimed to compensate for the spectral variation induced by the alteration of sample quantity during the spectral measurement of the powdered samples. Boosting was invoked to be combined with least‐squares support vector regression (LS‐SVR), forming boosting least‐squares support vector regression (BLS‐SVR) for the multivariate calibration task. The results showed that the tea quality could be accurately and rapidly determined via the Yb‐based internal standard NIR spectroscopy combined with BLS‐SVR method. Moreover, the introduction of boosting drastically enhanced the performance of individual LS‐SVR, and BLS‐SVR compared favorably with partial least‐squares regression. Copyright © 2013 John Wiley & Sons, Ltd.     

Simultaneous determination of methylxanthines in coffees and teas by UV-Vis spectrophotometry and partial least squares

This work describes a method to simultaneously determine caffeine (CF) and theobromine (TB) in coffee and tea samples using partial least squares (PLS-1). Sample preparation was required to eliminate strong interfering components. High-performance liquid chromatography (HPLC)-found concentrations of caffeine and theobromine (theophylline was not found in any analyzed sample) were used to construct universal calibration matrixes for coffee and tea. Due to the low levels of theobromine when compared to caffeine (up to 1000:1), theobromine addition standard was required to dramatically improve method performance. The method developed did not show statistically significant differences with an HPLC standard technique.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators

The spatial sign is a multivariate extension of the concept of sign. Recently multivariate estimators of covariance structures based on spatial signs have been examined by various authors. These new estimators are found to be robust to outlying observations. From a computational point of view, estimators based on spatial sign are very easy to implement as they boil down to a transformation of the data to their spatial signs, from which the classical estimator is then computed. Hence, one can also consider the transformation to spatial signs to be a preprocessing technique, which ensures that the calibration procedure as a whole is robust. In this paper, we examine the special case of spatial sign preprocessing in combination with partial least squares regression as the latter technique is frequently applied in the context of chemical data analysis. In a simulation study, we compare the performance of the spatial sign transformation to nontransformed data as well as to two robust counterparts of partial least squares regression. It turns out that the spatial sign transform is fairly efficient but has some undesirable bias properties. The method is applied to a recently published data set in the field of quantitative structure-activity relationships, where it is seen to perform equally well as the previously described best linear model for these data.     

Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy

This paper indicates the possibility to use near infrared (NIR) spectroscopy as a rapid method to predict quantitatively the content of caffeine and total polyphenols in green tea. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model is chosen according to the lowest root mean square error of cross-validation (RMSECV) in training. The correlation coefficient R between the NIR predicted and the reference results for the test set is used as an evaluation parameter for the models. The result showed that the correlation coefficients of the prediction models were R = 0.9688 for the caffeine and R = 0.9299 for total polyphenols. The study demonstrates that NIR spectroscopy technology with multivariate calibration analysis can be successfully applied as a rapid method to determine the valid ingredients of tea to control industrial processes.     

Determination of glycolic acid in cosmetics by online liquid chromatography–Fourier transform infrared spectrometry

An isocratic online liquid chromatography Fourier transform infrared procedure has been developed for the determination of glycolic acid in cosmetics. The method involves the ultrasound-assisted extraction of glycolic acid from the samples with an acetonitrile:phosphate buffer (25 mM, pH 2.7) (3:97 v/v). The extracts were centrifuged and filtered before their injection into the chromatography system, which was equipped with a C18 column and used a flow rate of 150 microL min(-1). FTIR spectra were acquired using a time-resolved rapid scan mode. To calculate the chromatograms, the spectral area was integrated between 1288 and 1215 cm(-1), with baseline correction established between 1319 and 1150 cm(-1), after correcting for the eluent spectral background. Peak area values of the extracted sample chromatograms were interpolated from an external calibration curve. The method provided a limit of detection of 0.034 mg mL(-1) and a relative standard deviation of 6% for five measurements at the 0.174 mg mL(-1) concentration level. Recovery values obtained by spiking 400 mg of three commercially available samples with amounts of glycolic acid from 3.7 to 9.8 mg ranged between 99.6 and 101%. The results obtained for the commercial samples agree well with their declared concentrations. An attempt to directly determine glycolic acid by attenuated total reflectance measurements using partial least squares calibration showed that results were strongly influenced by compounds coextracted from the matrix.     

基于最小二乘支持向量机的色谱指纹图谱预测银杏叶总抗氧化活性

在色谱图基线校正和色谱峰匹配基础上,提出以40个银杏叶提取物HPLC指纹图谱的色谱图轮廓作为输入,相应的提取物总抗氧化活性作为输出,建立最小二乘支持向量机回归模型,并对包含10个样本的测试集进行了预测.最小二乘支持向量机的测试集预测误差均方根(RMSEP)为0.0230,预测结果优于目前普遍使用的误差反向传播神经网络和偏最小二乘回归.与采用色谱峰面积为分析变量的模型预测结果比较表明:采用消除干扰后的色谱图全谱轮廓保留了样本的全部信息,预测结果更好     

How to construct a multiple regression model for data with missing elements and outlying objects

The aim of this study is to show the usefulness of robust multiple regression techniques implemented in the expectation maximization framework in order to model successfully data containing missing elements and outlying objects. In particular, results from a comparative study of partial least squares and partial robust M-regression models implemented in the expectation maximization algorithm are presented. The performances of the proposed approaches are illustrated on simulated data with and without outliers, containing different percentages of missing elements and on a real data set. The obtained results suggest that the proposed methodology can be used for constructing satisfactory regression models in terms of their trimmed root mean squared errors.     

Accurate discrimination of Gastrodia elata from different geographical origins using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis

Gastrodia elata from different geographical origins varies in quality and pharmacological activity. This study focused on the classification and identification of Gastrodia elata from six producing areas using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis. Before recognition analysis, a principal component analysis was applied to ascertain the discrimination possibility with high‐performance liquid chromatography fingerprints. And then, boosting partial least‐squares discriminant analysis and conventional partial least‐squares discriminant analysis were applied in this study. Experimental results indicated that the adaptive iteratively reweighted penalized least‐squares algorithm could eliminate the baseline drift of high‐performance liquid chromatography chromatograms effectively. And compared with partial least‐squares discriminant analysis, the total recognition rates using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis for the calibration sets and prediction sets were improved from 94 to 100% and 86 to 97%, respectively. In conclusion, high‐performance liquid chromatography combined with boosting partial least‐squares discriminant analysis, which has such advantages as effective, specific, accurate, non‐polluting, has an edge for discrimination of traditional Chinese medicine from different geographical origins. And the proposed methodology is a useful tool to classify and identify Gastrodia elata from different geographical origins.     

Sucrose as chiral selector for determining enantiomeric composition of phenylalanine by UV-vis spectroscopy and chemometrics

The determination of enantiomeric composition by partial least squares(PLS) modeling of UV-vis spectral data was investigated for samples of phenylalanine(phe) using sucrose as a chiral auxiliary.And a new data preprocess method,reference band normalization,was introduced to eliminate the spectral variations due to the changes of total concentration of phe.The determination coefficient(R~2) and the standard error of calibration set(SEC) of 13 standard samples are 0.9987 and 0.0128 respectively.The standard error of validation set(SECV) of 7 validation samples is 0.0049.The standard error of predict(SEP) of 6 blind samples for evaluating the robustness of the model is 0.0366.The regression model is robust to determine enantiomeric composition when total concentration varied.It is demonstrated that the reference band normalization is a convenient method of compensating for variations in total concentrations without knowing that in advance.     

A fully robust PARAFAC method for analyzing fluorescence data

Parallel factor analysis (PARAFAC) is a widespread method for modeling fluorescence data by means of an alternating least squares procedure. Consequently, the PARAFAC estimates are highly influenced by outlying excitation–emission landscapes (EEM) and element‐wise outliers, like for example Raman and Rayleigh scatter. Recently, a robust PARAFAC method that circumvents the harmful effects of outlying samples has been developed. For removing the scatter effects on the final PARAFAC model, different techniques exist. Newly, an automated scatter identification tool has been constructed. However, there still exists no robust method for handling fluorescence data encountering both outlying EEM landscapes and scatter. In this paper, we present an iterative algorithm where the robust PARAFAC method and the scatter identification tool are alternately performed. A fully automated robust PARAFAC method is obtained in that way. The method is assessed by means of simulations and a laboratory‐made data set. Copyright © 2009 John Wiley & Sons, Ltd.     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

Problems of calibration in trace, in situ-micro and surface analysis

Summary A systematic survey will be given on different strategies of calibration in dependence on given analytical and statistical conditions, particularly on several procedures of least squares regression (ordinary, orthogonal, unweighted and weighted LSR), of robust regression, addition methods and multicomponent calibration. In this connection calibration by means of latent variables (principal component regression PCR, partial least squares PLS) will be dealt with. The special conditions in the case of microanalysis and surface analysis will be considered under practical analytical as well as chemometrical aspects. Problems of homogeneity, representativness of samples and sample regions will be treated.     

Phenolic,Carotenoid and Saccharide Compositions of Vietnamese Camellia sinensis Teas and Herbal Teas

Tea (Camellia sinensis) and herbal tea have been recognized as rich sources of bioactive constituents with the ability to exert antioxidant actions. The aims of this study were to analyze phenolic, carotenoid and saccharide contents in a set of Vietnamese tea and herbal tea and compare the results with those of green and black teas marketed in the U.S. In total, 27 phenolics, six carotenoids and chlorophylls, and three saccharides were quantitatively identified. Catechins, quercetin glycosides and chlorogenic acid were the predominating phenolics in the teas, with the concentrations following the order: jasmine/green teas > oolong tea > black tea. Lutein was the dominant carotenoid in the teas and its concentrations were generally found to be higher in the jasmine and green teas than in the oolong and black teas. The study showed that the green teas originating in Vietnam had much higher levels of phenolics and carotenoids than their counterparts stemming from another country. The application of partial least squares discriminant analysis (PLS-DA) as a chemometric tool was able to differentiate phenolic profiles between methanolic extracts and tea infusions. Through principal component analysis (PCA), the similarities and dissimilarities among the jasmine, green, oolong, black teas and herbal teas were depicted.     

Implementation of chemometric techniques for evaluation of antioxidant properties of Camellia sinensis extracts

In this study, antioxidant properties of commercial green teas and dietary supplements containing Camellia sinensis extracts were evaluated. Extracts were examined using two antioxidant assays (DPPH^· radical method and ABTS^·+ cation radical method). A Folin-Ciocalteu assay was used to evaluate the total polyphenol content in the extracts. In order to compare and characterize the investigated Camellia sinensis extracts, chemometric techniques based on fingerprint chromatograms, antioxidant activity and total polyphenol content were applied. Application of chemometric methods allowed for reduction of multidimensionality of the data set and grouped the samples into differentiable clusters. The relationship between the antioxidant activity and total polyphenol content was also assessed. The results indicated that extracts with the higher polyphenolic content exhibited the stronger antiradical activity against both DPPH^· radicals and ABTS^·+ cation radicals. The multivariate calibration technique (such as a tree regression algorithm) can be a useful tool for rapid determining the antioxidant activity of a herbal product based on its fingerprint chromatogram