Metabolite profiling using (1)H NMR spectroscopy for quality assessment of green tea, Camellia sinensis (L.)

A set of 191 green teas from different countries was collected and analyzed by (1)H NMR. It was proposed to establish if the teas could be discriminated according to the country of origin or with respect to quality. Both principal component analysis (PCA) and cluster analysis were applied to the data. Some separation of Chinese and non-Chinese teas was observed. The present results did not allow allocation of samples to individual countries, but cluster analysis suggested that it might be possible with an augmented sample set. The PCA did show a separation between the Longjing type (highest quality Chinese tea) and most other Chinese teas and indicated some metabolites that could be responsible for the difference. Longjing teas showed higher levels of theanine, gallic acid, caffeine, epigallocatechin gallate, and epicatechin gallate and lower levels of epigallocatechin when compared with other teas. These compounds have been mentioned previously in connection with quality, but it was also shown that higher levels of theogallin (5-galloyl quinic acid), theobromine, 2-O-(beta-l-arabinopyranosyl)-myo-inositol and some minor sugar-containing compounds were found in Longjing teas while higher levels of fatty acids and sucrose were found in the other teas. These new markers could prove to be useful for the authentication of bulk tea.     

Metabolic dependence of green tea on plucking positions revisited: a metabolomic study

The dependence of global green tea metabolome on plucking positions was investigated through (1)H nuclear magnetic resonance (NMR) analysis coupled with multivariate statistical data set. Pattern recognition methods, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), were employed for a finding metabolic discrimination among fresh green tea leaves plucked at different positions from young to old leaves. In addition to clear metabolic discrimination among green tea leaves, elevations in theanine, caffeine, and gallic acid levels but reductions in catechins, such as epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epigallocatechin-3-gallate (EGCG), glucose, and sucrose levels were observed, as the green tea plant grows up. On the other hand, the younger the green tea leaf is, the more theanine, caffeine, and gallic acid but the lesser catechins accumlated in the green tea leaf, revealing a reverse assocation between theanine and catechins levels due to incorporaton of theanine into catechins with growing up green tea plant. Moreover, as compared to the tea leaf, the observation of marked high levels of theanine and low levels of catechins in green tea stems exhibited a distinct tea plant metabolism between the tea leaf and the stem. This metabolomic approach highlights taking insight to global metabolic dependence of green tea leaf on plucking position, thereby providing distinct information on green tea production with specific tea quality.     

Differentiation of tea (Camellia sinensis) varieties and their geographical origin according to their metal content

The metal content of 46 tea samples, including green, black, and instant teas, was analyzed. Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Sr, Ti, and Zn were determined by ICP-AES. Potassium, with an average content of 15145.4 mg kg(-1) was the metal with major content. Calcium, magnesium, and aluminum had average contents of 4252.4, 1978.2, and 1074.0 mg kg(-1), respectively. The average amount of manganese was 824.8 mg kg(-1). There were no clear differences between the metal contents of green and black teas. Pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neural networks (ANN), were applied to differentiate the tea types. LDA and ANN provided the best results in the classification of tea varieties. These chemometric procedures were also useful for distinguishing between Asian and African teas and between the geographical origin of different Asian teas.     

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.     

Characterization of tea cultivated at four different altitudes using 1H NMR analysis coupled with multivariate statistics

The taste of black tea differs according to the different areas in which the tea is grown, even for the same species of tea. A combination of (1)H NMR spectroscopy and partial least-squares discriminate analysis (PLS-DA) was used to assess the quality differences of tea leaves from four cultivation areas with different elevations, RAN > 1800 m, UDA = 1200 m, MEDA = 600 m, and YATA < 300 m, in Sri Lanka. As a result of a statistical analysis, PLS-DA showed a separation between high- and low-quality black teas derived from the four different tea cultivation areas. RAN from the highest elevation showed characteristic trends in the levels of theaflavin and theaflavin 3,3'-digallate that were found only in RAN, and the levels of theanine and caffeine were higher, and the levels of thearubigins, especially thearubigin 3,3'-digallate, were lower in RAN than in UDA, MEDA, and YATA. The structures of these components were determined by 1D and 2D NMR analyses. These results demonstrate that this method can be used to evaluate black tea quality according to the chemical composition or metabolites, which are characteristic of the tea leaves cultivated in four regions with different elevations in Sri Lanka.     

Metabolic discrimination of mango juice from various cultivars by band-selective NMR spectroscopy

NMR-based metabolic analysis of foods has been widely applied in food science. In this study, we performed discrimination of five different mango cultivars, Awin, Carabao, Keitt, Kent, and Nam Dok Mai, using metabolic analysis with band-selective excitation NMR spectra. A combination of unsupervised principal component analysis (PCA) with low-field region (1)H NMR spectra obtained by band-selective excitation provided a good discriminant model of the five mango cultivars. Using F(2)-selective 2D NMR spectra, we also identified various minor components in the mango juice. Signal assignment of the minor components facilitated the interpretation of the loading plot, and it was found that arginine, histidine, phenylalanine, glutamine, shikimic acid, and trigonelline were important for classification of the five mango cultivars.     

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.     

基于近红外光谱技术的眉茶拼配比例预测方法

拼配是出口炒青绿茶精制加工过程中的一项作业,通过对不同原料茶的拼合,达到维持产品品质标准化和一致性的目的.目前仍然依靠专家人工设计茶叶拼配比例,主观性较强,工作量大.为了实现拼配比例设计的客观化、定量化,该研究以眉茶为对象,提出了一种基于近红外光谱技术的拼配比例预测方法,采用源于黄山、湖北、福建3地的4种典型原料茶拼合...     

NMR-based metabonomic studies on the biochemical effects of epicatechin in the rat

Flavonoid consumption via tea drinking has been attributed a number of potential health benefits including cancer prevention, anti-inflammatory action, and cardioprotectant activity. Although the predominant flavonoids in fresh leaf and green tea are known to be flavan-3-ols and flavan-3-O-gallates ("the catechins"), the biochemical effects of tea polyphenol consumption on living systems are generally poorly understood. Metabonomic methods utilizing (1)H NMR spectroscopy of biofluids and principal component analysis (PCA) have been applied to investigate the bioavailability and metabolic responses of rats to a single dose of 22 mg of epicatechin (EC) dissolved in water. Urine samples were collected twice daily (0-8 and 8-24 h) from male Sprague-Dawley rats (n = 10) prior to dosing and for 2 days after dosing. A series of subtle urinary biochemical effects were evident from the (1)H NMR spectra showing that EC was both bioavailable and biochemically active. The identifiable biochemical effects associated with EC dosing included decreased urinary concentrations of taurine, citrate, dimethylamine, and 2-oxoglutarate. These effects were predominately seen within the first 8 h after dosing. EC metabolites were also observed in the urine during this time period. PCA of later time points after dosing (24-32 and 32-48 h) showed that the effects of EC were reversible. This is the first in vivo study demonstrating the overall endogenous metabolic effects of EC consumption and shows the bioavailability of EC via metabolic effects and excretion of EC metabolites.     

Variation of major volatile constituents in various green teas from Southeast Asia

A total of 15 green tea samples were prepared from fresh tea leaves obtained from three different countries: two from Laos, seven from Myanmar, and six from Vietnam. The volatile aroma constituents of the 15 samples were analyzed by gas chromatography/mass spectroscopy. Eleven aroma constituents were chosen from over 100 chemicals found in the samples to compare differences among various teas. They were hexanal, 1-penten-3-ol, heptanal, 1-pentenal, (Z)-2-penten-1-ol, (Z)-3-penten-1-ol, linalool oxide (trans-furanoid), linalool oxide (cis-furanoid), linalool, linalyl propanoate, and geraniol. Generally, concentrations of linalool and hexanal seem to play an important role in the quality of green teas. Green teas from Laos and Myanmar contained heterocyclic compounds, such as pyridines and pyrazines, formed by high-temperature processing. The presence of these heterocyclic compounds suggested that the temperature used for tea processing plays an important role in the formation of aroma chemicals in green teas.     

Rapid differentiation of tea products by surface desorption atmospheric pressure chemical ionization mass spectrometry

Protonated water molecules generated by an ambient corona discharge were directed to impact tea leaves for desorption/ionization at atmospheric pressure. Thus, a novel method based on surface desorption chemical ionization mass spectrometry (DAPCI-MS) has been developed for rapid analysis of tea products without any sample pretreatment. Under the optimized experimental conditions, DAPCI MS spectra of various tea samples are recorded rapidly, and the resulting mass spectra are chemical fingerprints that characterize the tea samples. On the basis of the mass spectral fingerprints, 40 tea samples including green tea, oolong tea, and jasmine tea were successfully differentiated by principal component analysis (PCA) of the mass spectral raw data. The PCA results were also validated with cluster analysis and supervised PCA analysis. The alteration of signal intensity caused by rough surfaces of tea leaves did not cause failure in the separation of the tea products. The experimental findings show that DAPCI-MS creates ions of both volatile and nonvolatile compounds in tea products at atmospheric pressure, providing a practical and convenient tool for high-throughput differentiation of tea products.     

基于图像和光谱信息融合的红茶萎凋程度量化判别

为了实现对红茶萎凋程度量化判别,该研究提出了一种将图像和光谱信息融合后分别与线性判别分析法和偏最小二乘法结合的技术,进行工夫红茶萎凋程度定性判别及儿茶素与氨基酸比值定量预测研究。通过对图像进行主成分分析,筛选出5个特征波长和对应的光谱特征值,基于灰度共生矩阵提取5个特征波长图像的纹理特征值,并采用连续投影算法优选出14个纹理特征值,然后分别以光谱和纹理特征值融合数据建立红茶萎凋程度的线性判别模型和儿茶素与氨基酸比值的偏最小二乘预测模型。结果表明:采用所研究的方法和建立的模型对工夫红茶萎凋程度判别准确率达到94.64%,儿茶素与氨基酸比值预测相关系数为0.8765,预测均方根误差为0.434,预测结果较好。证明应用这两种方法能实现对红茶萎凋程度量化判别。     

Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils

A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).     

The use of catechins as biochemical markers in diversity studies of tea (Camellia sinensis)

The concentrations of catechins in a Kenyan tea germplasm collection of 102 accessions were determined by HPLC. Total green leaf catechin concentrations and the ratio of dihydroxylated to trihydroxylated catechins were used to establish genetic differentiation in the germplasm. Upon multivariate analysis, accumulation of the various catechins separated the tea clones into 3 major and 5 minor groups according to their phylogenetic origins. The Cambod teas had the highest ratio (7:10) followed by China teas (3:5) while Assam teas had the lowest ratio (1:4). This biochemical differentiation indicates that there is potential for broadening the genetic base of the mainly Assam teas in Kenya (90%) with the putative China and Cambod teas.     

New phenolic components and chromatographic profiles of green and fermented teas

A standardized profiling method based on liquid chromatography with diode array and electrospray ionization mass spectrometric detection (LC-DAD-ESI/MS) was applied to establish the phenolic profiles of 41 green teas and 25 fermented teas. More than 96 phenolic compounds were identified that allowed the teas to be organized into five groups. Epigallocatechin gallate (EGCG) was the major phenolic component of green tea made from mature leaves (group 2), while green tea made from the younger buds and leaves (group 1) contained lower flavonoid concentrations. Partially fermented teas (group 3) contained one-half the EGCG content of the green tea. Fully fermented black teas (group 4) had a trace of EGCG, but contained theaflavins. Highly overfermented black tea (group 5) contained only trace amounts of flavonol glycosides and theaflavins. Over 30 phenolics are new for tea, and this is the first phenolic profile to simultaneously detect C- and O-glycosylated flavonoids, catechins, proanthocyanidins, phenolic acid derivatives, and purine alkaloids.     

Profiles of purine and pyrimidine nucleotides in fresh and manufactured tea leaves

Profiles of nucleotide levels in two varieties of Japanese green teas (cv. Yabukita and Saemidori), a Chinese green tea (Longjing), and two Japanese black teas (cv. Benifuuki and Benihikari) were determined and compared with that of fresh tea leaves. The concentration of 5'-nucleotides in green tea was much higher than in black tea. Nucleoside diphosphates were present in larger amounts than nucleoside triphosphates in manufactured green and black teas, whereas the triphosphates predominated in fresh tea leaves. Low levels of 3'-nucleotides were found in green and black teas. Inosine 5'-monophosphate, which is utilized as a seasoning component, was found in all manufactured teas in concentrations ranging from 50 to 200 nmol/g of dry weight. The levels of both inosine 5'-monophosphate and guanosine 5'-monophosphate were high in Chinese Longjing green tea. The unique profiles of nucleotides in manufactured teas may be a consequence of the action of degradation enzymes, such as ribonuclease, apyrase, phosphatase, nucleotidase, and adenosine 5'-monophsphate deaminase during the commercial processing of the young leaves.     

Effects of heat processing and storage on flavanols and sensory qualities of green tea beverage

This research was conducted to understand the effects of heat processing and storage on flavanols and sensory qualities of green tea extract. Fresh tea leaves were processed into steamed and roasted green teas by commercial methods and then extracted with hot water (80 degrees C) at 1:160 ratio (tea leaves/water by weight). Green tea extracts were heat processed at 121 degrees C for 1 min and then stored at 50 degrees C to accelerate chemical reactions. Changes in flavanol composition and sensory qualities of green tea extracts during processing and storage were measured. Eight major flavanols (catechin, epicatechin, gallocatechin, epigallocatechin, epicatechin gallate, catechin gallate, epigallocatechin gallate, and gallocatechin gallate) were identified in the processed tea extract. Among them, epigallocatechin gallate and epigallocatechin appeared to play the key role in the changes of sensory qualities of processed green tea beverage. The steamed tea leaves produced a more desirable quality of processed green tea beverage than the roasted ones.     

Classification of gilthead sea bream (Sparus aurata) from 1H NMR lipid profiling combined with principal component and linear discriminant analysis

The combination of (1)H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed (1)H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85-97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.