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.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination

This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of α-linolenic and linoleic acid in eight types of edible vegetable oils and their blending. For this purpose, a combination of spectral wavelength selection by wavelet transform (WT) and elimination of uninformative variables (UVE) was proposed to obtain simple partial least square (PLS) models based on a small subset of wavelengths. WT was firstly utilized to compress full NIR spectra which contain 1413 redundant variables, and 42 wavelet approximate coefficients were obtained. UVE was then carried out to further select the informative variables. Finally, 27 and 19 wavelet approximate coefficients were selected by UVE for α-linolenic and linoleic acid, respectively. The selected variables were used as inputs of PLS model. Due to original spectra were compressed, and irrelevant variables were eliminated, more parsimonious and efficient model based on WT-UVE was obtained compared with the conventional PLS model with full spectra data. The coefficient of determination (r²) and root mean square error prediction set (RMSEP) for prediction set were 0.9345 and 0.0123 for α-linolenic acid prediction by WT-UVE-PLS model. The r² and RMSEP were 0.9054, 0.0437 for linoleic acid prediction. The good performance showed a potential application using WT-UVE to select NIR effective variables. WT-UVE can both speed up the calculation and improve the predicted results. The results indicated that it was feasible to fast determine α-linolenic acid and linoleic acid content in edible oils using NIR spectroscopy.     

Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration

A principal component regression (PCR) model is built for prediction of total antioxidant capacity in green tea using near-infrared (NIR) spectroscopy. The modelling procedures are systematically studied with the focus on outlier detection. Different outlier detection methods are used and compared. The root mean square error of prediction (RMSEP) of the final model is comparable to the precision of the reference method.     

Full Validation of a Simple Method for Determination of Catechins and Caffeine in Brazilian Green Tea (Camellia sinensis var. assamica) Using HPLC

This paper describes the validation of an HPLC method for the assay of a green tea brew. The method employs a RP-18 column with water:methanol:ethyl acetate elution and UV detection at 280 nm. Specificity was evaluated using a photodiode array detector. The validation data showed that the assay is specific, accurate, precise, and reproducible for determination of six catechins and caffeine simultaneously. The response was linear over a range of 37–185 μg mL^?1 for caffeine, 99–500 μg mL^?1 for (?)-epigallocatechin (EGC), 20–100 μg mL^?1 for (+)-catechin (C), 30–150 μg mL^?1 for (?)-epicatechin (EC), 150–800 μg mL^?1 for (?)-epigallocatechin gallate (EGCG), 20–105 μg mL^?1 for (?)-gallocatechin gallate (GCG) and 40–205 μg mL^?1 for (?)-epicatechin gallate (ECG) (r > 0.9999 for all compounds). The range of recoveries was 96.12–110.48% according to substances. The RSD values for intra- and inter-day precision studies were <2.07 and <6.65%, respectively. The composition of samples assayed suggests that the summer is the best season for extract a major content of EGCG and caffeine. This assay can be readily utilized as quality controlled method for major green tea compounds.     

Novel Spectral Interval Selection Method Based on Synchronous Two-Dimensional Correlation Spectroscopy

Synchronous 2D correlation spectroscopy was first proposed to select informational spectral intervals in PLS calibration. The proposed method could extract the spectral intervals related to analyte. The results of its application to NIR/PLS determination of quercetin in extract of Ginkgo biloba leaves showed that the proposed method could find out an optimized region with which one could improve the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP), and comparing with the result obtained using whole spectra and interval PLS.     

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.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Effect of Water Hardness on Catechin and Caffeine Content in Green Tea Infusions

The health benefits of green tea are associated with its high catechin content. In scientific studies, green tea is often prepared with deionized water. However, casual consumers will simply use their local tap water, which differs in alkalinity and mineral content depending on the region. To assess the effect of water hardness on catechin and caffeine content, green tea infusions were prepared with synthetic freshwater in five different hardness levels, a sodium bicarbonate solution, a mineral salt solution, and deionized water. HPLC analysis was performed with a superficially porous pentafluorophenyl column. As water hardness increased, total catechin yield decreased. This was mostly due to the autoxidation of epigallocatechin (EGC) and epigallocatechin gallate (EGCG). Epicatechin (EC), epicatechin gallate (ECG), and caffeine showed greater chemical stability. Autoxidation was promoted by alkaline conditions and resulted in the browning of the green tea infusions. High levels of alkaline sodium bicarbonate found in hard water can render some tap waters unsuitable for green tea preparation.     

The Dual Action of Epigallocatechin Gallate (EGCG), the Main Constituent of Green Tea, against the Deleterious Effects of Visible Light and Singlet Oxygen-Generating Conditions as Seen in Yeast Cells

Green tea extracts (GTEs) as well as their main component, the polyphenol epigallocatechin gallate (EGCG), are known for their versatile antioxidant, antimicrobial, antitumoral or anti-inflammatory effects. In spite of the huge beneficial action, there is increasing evidence that under certain conditions green tea and its components can be detrimental to living organisms. Using Saccharomyces cerevisiae strains with various defects in the response to oxidative stress, we found that GTEs or EGCG act in synergy with visible light, exhibiting either deleterious or protective effects depending on the solvent employed. Similar synergistic effects could be observed under singlet oxygen-generating conditions, such as light exposure in the presence of photosensitizers or UV-A irradiation, therefore solvent variance may represent a powerful tool to modulate the preparation of green tea extracts, depending on the intended target.     

Polychlorinated dibenzo-<Emphasis Type="Italic">p</Emphasis>-dioxins,dibenzofurans and biphenyls in fish from the Netherlands: concentrations,profiles and comparison with DR CALUX® bioassay results

A liquid chromatography–particle-beam mass spectrometer (LC–PB/MS) with interchangeable electron-impact (EI) and glow-discharge (GD) ion sources was evaluated for future application in analysis of botanical extracts. In this work a green tea tincture was characterized for a series of catechin components (catechin, epicatechin, epigallocatechin, and epigallocatechin gallate (EGCG)) and caffeine. Special emphasis was given to EGCG and caffeine, because they are important in determining the possible health effects of the green tea. The effects of instrument operating conditions were evaluated for the EI and GD ionization sources to determine their effect on analyte intensities and fragmentation patterns. These studies furnished information about the effects of these conditions in determining possible ionization pathways in the two ion sources. The mass spectra of these compounds obtained with the GD ion source are EI-like in appearance, with clearly identified molecular ions and fragmentation patterns that are easily rationalized. The absolute limits of detection for EGCG and caffeine were, respectively, 11 ng and 0.77 ng for the EI source and 3.2 ng and 0.61 ng for the GD source. The PB/EIMS and PB/GDMS combinations can be operated in a flow-injection mode, wherein the analyte is injected directly into the mobile phase, or coupled to high-performance liquid chromatography (HPLC), enabling LC–MS analysis of complex mixtures. A reversed-phase chromatographic separation of the green tea tincture was performed on a commercial C₁₈ column using a gradient of water (containing 0.1% TFA) and ACN. Quantification of EGCG and caffeine was performed by the standard addition method. The amounts of EGCG and caffeine in the tested green tea tincture were each ∼14 mg mL⁻¹.     

Health Benefits and Chemical Composition of Matcha Green Tea: A Review

Japanese matcha is a type of powdered green tea, grown in a traditional way. Shading of the plants during the growth period enhances the processes of synthesis and accumulation of biologically active compounds, including theanine, caffeine, chlorophyll and various types of catechins. Green tea contains four main catechins, i.e., (−)-epicatechin (EC), (−)-epicatechin-3-gallate (ECG), (−)-epigallocatechin (EGC) and (−)-epigallocatechin-3-gallate (EGCG), of which the latter is the most active and abundant and matcha is their best condensed source. Due to its unique chemical composition and prized flavour, which sets it apart from other tea beverages, it is considered the highest quality tea. Its health-promoting properties are attributed to the high content of antioxidant and anti-inflammatory substances. Studies confirming the high antioxidant potential of tea beverages claim that it originates from the considerable content of catechins, a type of phenolic compound with beneficial effects on human health. Due to its potential for preventing many diseases and supporting cognitive function, regular consumption of matcha may have a positive effect on both physical and mental health. The aim of this review was to compile the health benefits of matcha tea. It is the first such review to be undertaken, and presents its main bioactive compounds in a systematic manner.     

绿茶多酚对自由基诱导的红细胞膜过氧化的抑制作用

采用水溶性偶氮引发剂2,2'-偶氮二(2-脒基丙烷)二盐酸盐(AAPH)在37引发入血红细胞膜的过氧化,通过测定氧气吸收及维生素E的消耗研究了过氧化过程的动力学,并对从绿茶中提取的主要多酚类化合物的抗氧化活性做了定量研究。使用的绿茶多酚有:(-)-表儿茶素(EC),(-)-表儿茶素(EGC),(-)-表儿茶素酸酯(ECG)和(-)-表儿茶素培酸酯(EGCG)。结果表明,这些绿茶多酚能够显著缩短过氧化反应的动力学链长,有效地抑制红细胞膜的过氧化。抗氧化活性顺序为:EC〉GCG〉EGCG〉EGC。     

Robust partial least squares model for prediction of green tea antioxidant capacity from chromatograms

In this paper a robust version of the partial least squares model (partial robust M-regression, PRM) was built to predict the total antioxidant capacity of green tea extracts. In order to construct a calibration model, chromatograms obtained by a fast high-performance liquid chromatographic method on a monolithic silica column were related with the total antioxidant capacity of green tea extracts as determined by the Trolox antioxidant capacity method. Since natural samples are the subject of the study, some outlying samples are present in the data, as shown in an earlier work. Therefore, to construct reliable calibration models, they were detected and removed prior to modeling. With the applied robust partial least squares approach, where a weighting scheme is embedded to down-weight the negative influence of outliers upon the model it is possible to construct a robust calibration model, without prior identification of outlying objects. It was shown that a robust model, allowing satisfactory prediction for test samples, can be used in controlling green tea antioxidant capacity based on their chromatograms. The constructed robust partial least squares model was shown to have virtually the same fit and predictive power as the classical partial least squares model when outlying samples were removed from the data.     

Green Tea Suppresses Brain Aging

Epidemiological studies have demonstrated that the intake of green tea is effective in reducing the risk of dementia. The most important component of green tea is epigallocatechin gallate (EGCG). Both EGCG and epigallocatechin (EGC) have been suggested to cross the blood–brain barrier to reach the brain parenchyma, but EGCG has been found to be more effective than EGC in promoting neuronal differentiation. It has also been suggested that the products of EGCG decomposition by the intestinal microbiota promote the differentiation of nerve cells and that both EGCG and its degradation products act on nerve cells with a time lag. On the other hand, the free amino acids theanine and arginine contained in green tea have stress-reducing effects. While long-term stress accelerates the aging of the brain, theanine and arginine suppress the aging of the brain due to their anti-stress effect. Since this effect is counteracted by EGCG and caffeine, the ratios between these green tea components are important for the anti-stress action. In this review, we describe how green tea suppresses brain aging, through the activation of nerve cells by both EGCG and its degradation products, and the reductions in stress achieved by theanine and arginine.     

茶中茶多酚的高效液相色谱法分离分析

用改进的Ａｇａｒｗａｌ方法萃取不同种类茶叶和茶饮料中的茶多酚,建立了用高效液相色谱（ＨＰＬＣ）法对茶多酚进行分离分析方法。ＨＰＬＣ可有效分离ＧＴＰｓ主要组成成分ＥＣ、ＥＧＣ、ＥＣＧ和ＥＧＣＧ并精确定量,相对标准偏差小于５％。茶叶加工过程对ＧＴＰｓ含量有很大影响,绿茶总ＧＴＰｓ含量在６￣１５ｇ／１００ｇ干茶叶、乌龙茶总ＧＴＰｓ含量在５￣７ｇ／１００ｇ干茶叶,红茶总ＧＴＰｓ含量低于２ｇ／１００ｇ干茶叶     

Determination of catechins and caffeine in proposed green tea standard reference materials by liquid chromatography-particle beam/electron ionization mass spectrometry (LC-PB/EIMS)

Presented here is the quantitative analysis of green tea NIST standard reference materials (SRMs) via liquid chromatography-particle beam/electron ionization mass spectrometry (LC-PB/EIMS). Three different NIST green tea standard reference materials (SRM 3254 Camellia sinesis Leaves, SRM 3255 C. sinesis Extract and SRM 3256 Green Tea-containing Oral Dosage Form) are characterized for the content of caffeine and a series of catechin species (gallic acid, catechin, epicatechin, epigallocatechin, epicatechin gallate and epigallocatechin gallate (EGCG)). The absolute limits of detection for caffeine and the catechin species were determined to be on the nanogram level. A reversed-phase chromatographic separation of the green tea reference materials was carried out on a commercial C₁₈ column using a gradient of water (containing 0.1% TFA) and 2:1 methanol:acetonitrile (containing 0.1%TFA) at 0.9 mL min⁻¹ and an analysis time of 50 min. Quantification of caffeine and the catechin species was carried out using the standard addition and internal standard methods, with the latter providing appreciable improvements in precision and recovery.     

Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy

A new acetolactate synthase (ALS)-inhibiting herbicide, propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273), was applied to oilseed rape (Brassica napus L.) leaves in different leaf positions. Visible/near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of ALS activity and protein content in rapeseed leaves. Partial least squares (PLS) analysis was the calibration method with comparison of different spectral preprocessing by Savitzky-Golay (SG) smoothing, standard normal variate (SNV), first and second derivative. The best PLS models were obtained by first-derivative spectra for ALS, whereas original spectra for soluble, non-soluble and total protein contents. Simultaneously, certain latent variables (LVs) were used as the inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models. All LS-SVM models outperformed PLS models and BPNN models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias in validation set by LS-SVM were 0.998, 0.715 and 0.079 for ALS, 0.999, 33.084 and 1.178 for soluble protein, 0.997, 42.773 and 6.244 for non-soluble protein, 0.999, 59.562 and 7.437 for total protein, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the determination of ALS activity and protein content of rapeseed leaves. The results would be helpful for further on field analysis of using Vis/NIR spectroscopy to monitor the growing status and physiological properties of oilseed rape.     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.