Geographical tracing of Xihu Longjing tea using high performance liquid chromatography

Intelligent identification of picking periods of Lu'an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning

Lu'an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In this work, a simple and cheap indicator displacement colorimetric sensor array combined with smartphone was developed to rapidly identify LAGP picked during different picking periods. First, the chemical component contents of LAGP picked before and after Qingming Festival were analyzed. Second, a well-designed colorimetric sensor array was proposed based on the tea component contents differences. Finally, machine learning was used to process the array data taken by a smartphone. By comparison, the accuracy of the best model for the prediction set was 97%. Meanwhile, the multi-channel advantages of the sensing array were demonstrated by an ablation experiment. In addition, the method achieved an AGREE analysis score of 0.88, indicating that it was environmental-friendly.

1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea Longjing Tea

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.

Comparative Metabolomics Study of Four Kinds of Xihu Longjing Tea Based on Machine Fixing and Manual Fixing Methods

China Xihu Longjing tea is famous for its good flavor and quality. However, information on its related metabolites, except for flavonoids, is largely deficient. Different processing methods for China Xihu Longjing tea fixing-by machines at both the first and second step (A1), first step by machine and second step by hand (A2), first step by hand and second step by machine (A3), and by hand at both the first and second step (A4)-were compared using a UHPLC-QE-MS-based metabolomics approach. Liquid chromatography-mass spectrometry was used to analyze the metabolic profiles of the processed samples. A total of 490 metabolites (3 alkaloids, 3 anthracenes, 15 benzene and substituted derivatives, 2 benzopyrans, 13 coumarins and derivatives, 128 flavonoids, 4 furanoid lignans, 16 glycosides and derivatives, 5 indoles and derivatives, 18 isocoumarins and derivatives, 4 chalcones and dihydrochalcones, 4 naphthopyrans, 3 nucleosides, 78 organic acids and derivatives, 55 organooxygen compounds, 5 phenols, 109 prenol lipids, 3 saccharolipids, 3 steroids and steroid derivatives, and 17 tannins) were identified. The different metabolic profiles were distinguished using PCA and OPLS-DA. There were differences in the types and contents of the metabolites, especially flavonoids, furanoid lignans, glycosides and derivatives, organic acids and derivatives, and organooxygen compounds. There was a positive correlation between flavonoid metabolism and amino acid metabolism. However, there was a negative correlation between flavonoid metabolism and amino acid metabolism, which had the same trend as prenol lipid metabolism and tannins. This study provides new valuable information regarding differences in the metabolite profile of China Xihu Longjing tea processed based on machine fixing and on manual fixing methods.

Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.

High-Throughput Flow Injection Analysis-Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea Application to the Detection of Teas Adulterated with Chicory

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, including its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprinting methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to discriminate tea samples from chicory independently of the tea product variety as well as to classify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases-i.e., each tea product variety versus chicory-by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adulteration cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors below 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.

Potential to Reduce Chemical Fertilizer Application in Tea Plantations at Various Spatial Scales

Tea is the main commercial crop grown in China, and excessive application of chemical fertilizers in tea plantations is common. However, the potential to reduce chemical fertilizer use in tea plantations is unclear. In this study, Zhejiang Province was selected as the research object to systematically analyze the potential for tea plantation chemical-fertilizer reduction at different spatial scales. The geographic information system-based analytic hierarchy process method and Soil and Water Assessment Tool model were used to determine the chemical fertilizer reduction potential at the province scale and watershed scale, respectively. At the field scale, two consecutive years of field experiments were conducted on a tea plantation. Province-level analysis showed that 51.7% of the area had an average total fertilization intensity greater than 350 kg/hm² and a high reduction potential. Watershed analysis revealed that chemical fertilizer reduction had better potential in reducing total nitrogen and total phosphorus inputs to runoff in the short term, whereas 50% organic fertilizer substitution was the best strategy to achieve long-term effects. The field experiments further proved that organic fertilizer substitution balanced tea growth and environmental protection. This study provides a useful method to investigate strategies to reduce chemical fertilizer use in tea-growing areas.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.

Gibberellin Increases the Bud Yield and Theanine Accumulation in Camellia sinensis (L.) Kuntze

Tea (Camellia sinensis) is one of the most important cash crops in the world. Theanine, as an important amino acid component in tea, is a key quality index for excellent tea quality and high economic value. People increase theanine accumulation in tea mainly through the application of nitrogen fertilizer, shading and pruning. However, these methods are not effective. In this study, we treated tea buds with a 100 μM solution of GA₃ containing 1‰ tween-20, investigated the effects of GA₃ on theanine accumulation, bud yield, chlorophyll fluorescence parameters and expression level of theanine biosynthesis pathway genes in tea plant by qPCR, LC-MS/MS etc. Results showed that change trends of theanine and GA₃ was extremely positively correlated with each other. Exogenous GA₃ upregulated the expression level of theanine biosynthesis pathway genes, caused an increase of theanine content (mg·g^-1) by 27% in tea leaves compared with Mock, and accelerated the germination of buds and elongation of shoots, which lead to a significant increase of tea yield by 56% (w/w). Moreover, the decrease of chlorophyll contents, photochemical quenching coefficient (qP) and relative electron transport rate (rETR) under GA₃ treatment suggested that GA₃ reduced photosynthesis in the tender tea leaves, indicating that the decline of carbon assimilation in tea plants was conducive to the nitrogen metabolism, and it was beneficial to the accumulation of theanine. This study provided a new technical and theoretical support for the precise control of tea quality components and phenophase.

Region identification of Xinyang Maojian tea using UHPLC-Q-TOF/MS-based metabolomics coupled with multivariate statistical analyses

Xinyang Maojian tea is a kind of famous roasted green tea produced in the middle of China. Ultra-high performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-Q-TOF/MS)-based metabolomics coupled with multivariate statistical analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were carried out in XMMJTs collected from Luoshan, Shangcheng, and Shihe Counties, respectively. Additionally, seven catechins, four flavonoids, two purine alkaloids, and gallic acid contents were determined by HPLC. Differential metabolites were selected by p-value <0.05, and fold change >1.50 or < 0.66 among 745 detected metabolites in metabolomics analysis. The results showed significant (p < 0.05) differences of three catechins including (-)-epicatechin, (-)-epicatechin gallate, and (-)-gallocatechin gallate, four flavonoids (i.e. quercetin, kaempferol, myricetin, and rutin), and theobromine among three various regions, and significant (p < 0.05) differences of (-)-epicatechin gallate, (-)-epigallocatechin, (+)-catechin, gallic acid, and kaempferol between Shuchazao and Group cultivar. The HCA showed that, except for two samples (i.e. LS 2 and SH 2) of Shuchazao cultivar clustered together, others could be clustered completely according to production place. The 63 relevant differential metabolites could achieve the purpose of region identification through PCA. Kyoto encyclopedia of genes and genomes (KEGG) metabolic pathway analysis elaborated the impact of geographical origin and tea cultivar on physiological metabolism in tea tree. PRACTICAL APPLICATION: Ultra-high performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-Q-TOF/MS)-based liquid chromatography-tendem mass spectrometry (LC-MS/MS) metabolomics coupled with multivariate statistical analyses, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), revealed 63 differential metabolites related to production place, which contributed to the region identification of Xinyang Maojian teas.

Tea quality evaluation by applying E-nose combined with chemometrics methods

Tea is one of the most popular beverage with distinct flavor consumed worldwide. It is of significance to establish evaluation method for tea quality controlling. In this work, electronic nose (E-nose) was applied to assess tea quality grades by detecting the volatile components of tea leaves and tea infusion samples. The "35th s value", "70th s value" and "average differential value" were extracted as features from E-nose responding signals. Three data reduction methods including principle component analysis (PCA), multi-dimensional scaling (MDS) and linear discriminant analysis (LDA) were introduced to improve the efficiency of E-nose analysis. Logistic regression (LR) and support vector machine (SVM) were applied to set up qualitative classification models. The results indicated that LDA outperformed original data, PCA and MDS in both LR and SVM models. SVM had an advantage over LR in developing classification models. The classification accuracy of SVM based on the data processed by LDA for tea infusion samples was 100%. Quantitative analysis was conducted to predict the contents of volatile compounds in tea samples based on E-nose signals. The prediction results of SVM based on the data processed by LDA for linalool (training set: R2 = 0.9523; testing set: R2 = 0.9343), nonanal (training set: R2 = 0.9617; testing set: R2 = 0.8980) and geraniol (training set: R2 = 0.9576; testing set: R2 = 0.9315) were satisfactory. The research manifested the feasibility of E-nose for qualitatively and quantitatively analyzing tea quality grades.

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics

BACKGROUND

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds.

RESULTS

Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples.

CONCLUSION

The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses

BACKGROUND

Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head-space solid phase microextraction approach and gas chromatography-mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas.

RESULTS

Thirty-one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k-nearest neighbor (k-NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross-validation prediction abilities (84.6%) was obtained for the PCA-k-NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability.

CONCLUSION

The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry.

Transcriptional profiling of catechins biosynthesis genes during tea plant leaf development

A total of 299,113 unigenes were generated and 15,817 DEGs were identified. We identified candidate genes associated with the regulation of catechins biosynthesis during leaf development in tea plant. The tea plant (Camellia sinensis (L.) O. Kuntze) is one of the most economically significant crops worldwide because of its positive effects on human health. The health benefits of tea are mainly attributed to catechins, which are the predominant polyphenols that accumulate in tea. Catechins are products of the phenylpropanoid and flavonoid biosynthetic pathways. Although catechins were identified in tea leaves long ago, the molecular mechanisms regulating catechins biosynthesis remain unclear. To identify candidate genes involved in catechins biosynthesis, we analyzed the transcriptomes of tea leaves during five different leaf stages of development using RNA-seq. Approximately 809 million high-quality reads were obtained, trimmed, and assembled into 299,113 unigenes with an average length of 565 bp. A total of 15,817 unigenes were differentially expressed during the different stages of leaf development. These differentially expressed genes were enriched in a variety of processes such as the regulation of the cell cycle, starch and sucrose metabolism, photosynthesis, phenylpropanoid biosynthesis, phenylalanine metabolism, and flavonoid biosynthesis. Based on their annotations, 51 of these differentially expressed unigenes are involved in phenylpropanoid and flavonoid biosynthesis. Furthermore, transcription factors such as MYB, bHLH and MADS, which may involve in the regulation of catechins biosynthesis, were identified through co-expression analysis of transcription factors and structural genes. Real-time PCR analysis of candidate genes indicated a good correlation with the transcriptome data. These findings increase our understanding of the molecular mechanisms regulating catechins biosynthesis in the tea plant.

Evaluation of Macro- and Microelement Levels in Black Tea in View of Its Geographical Origin

The aim of this study was to evaluate the elemental composition of black tea samples and their infusions in view of their geographical origin. In total, 14 elements were analyzed, 13 (Ca, K, Mg, Na, Mn, Fe, Zn, Cu, Cr, Ni, Co, Cd, and Pb) by flame atomic absorption spectrometry, and P by UV-Vis spectrometry, after mineralization of samples. It was found that K was the most abundant macroelement in the analyzed samples, whereas among microelements, the highest concentration was found for Mn. Based on the obtained data, the percentage of elements leached into the infusions as well as the daily elemental intake from tea were calculated. The daily intake from tea was compared to the recommended daily allowances (RDAs), and the highest percentages of the RDAs were found for Mn (15 %) and Co (10 %). To study the relations between elemental composition and country of origin of samples, factor analysis and cluster analysis were applied. These multivariate techniques proved to be efficient tools able to differentiate samples according to their provenance as well as plantation within the common regions.

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.

Accumulation of catechins and expression of catechin synthetic genes in Camellia sinensis at different developmental stages

BACKGROUND

Catechins are the main polyphenol compounds in tea (Camellia sinensis). To understand the relationship between gene expression and product accumulation, the levels of catechins and relative expressions of key genes in tea leaves of different developmental stages were analyzed.

RESULTS

The amounts of catechins differed significantly in leaves of different stages, except for gallocatechin gallate. Close correlations between the expression of synthesis genes and the accumulation of catechins were identified. Correlation analysis showed that the expressions of chalcone synthase 1, chalcone synthase 3, anthocyanidin reductase 1, anthocyanidin reductase 2 and leucoanthocyanidin reductase genes were significantly and positively correlated with total catechin contents, suggesting their expression may largely affect total catechin accumulation. Anthocyanidin synthase was significantly correlated with catechin. While both ANRs and LAR were significantly and positively correlated with the contents of (-)-epigallocatechin gallate and (-)-epicatechin gallate.

CONCLUSION

Our results suggest synergistic changes between the expression of synthetic genes and the accumulation of catechins. Based on our findings, anthocyanidin synthase may regulate earlier steps in the conversion of catechin, while the anthocyanidin reductase and leucoanthocyanidin reductase genes may both play important roles in the biosynthesis of galloylated catechins.

The classification and prediction of green teas by electrochemical response data extraction and fusion approaches based on the combination of e-nose and e-tongue

Aroma and taste are the most important attributes that influence the pleasantness of tea infusion.

The study of fingerprint characteristics of Dayi Pu-Erh tea using a fully automatic HS-SPME/GC-MS and combined chemometrics method

The quality of tea is presently evaluated by the sensory assessment of professional tea tasters, however, this approach is both inconsistent and inaccurate. A more standardized and efficient method is urgently needed to objectively evaluate tea quality. In this study, the chemical fingerprint of 7 different Dayi Pu-erh tea brands and 3 different Ya'an tea brands on the market were analyzed using fully automatic headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC–MS). A total of 78 volatiles were separated, among 75 volatiles were identified by GC–MS in seven Dayi Pu-erh teas, and the major chemical components included methoxyphenolic compounds, hydrocarbons, and alcohol compounds, such as 1,2,3-trimethoxybenzene, 1,2,4-trimethoxybenzene, 2,6,10,14-tetramethyl-pentadecane, linalool and its oxides, α-terpineol, and phytol. The overlapping ratio of peaks (ORP) of the chromatogram in the seven Dayi Pu-erh tea samples was greater than 89.55%, whereas the ORP of Ya'an tea samples was less than 79.10%. The similarity and differences of the Dayi Pu-erh tea samples were also characterized using correlation coefficient similarity and principal component analysis (PCA). The results showed that the correlation coefficient of similarity of the seven Dayi Pu-erh tea samples was greater than 0.820 and was gathered in a specific area, which showed that samples from different brands were basically the same, despite have some slightly differences of chemical indexes was found. These results showed that the GC-MS fingerprint combined with the PCA approach can be used as an effective tool for the quality assessment and control of Pu-erh tea.

Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds

This study aimed to construct objective and accurate analytical models of tea categories based on their polyphenols and caffeine. A total of 522 tea samples of 4 commonly consumed teas with different fermentation degrees (green tea, white tea, oolong tea, and black tea) were analyzed by high-performance liquid chromatography (HPLC) coupled with spectrophotometry, utilizing ISO 14502, as analytical tools. The content of polyphenols and caffeine varied significantly according to differently fermented teas, indicating that these active constituents may discriminate fermentation degrees effectively. By principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA), the vast majority of tea samples could be successfully differentiated according to their chemical markers. This study yielded three discriminant functions with the capacity to simultaneously discriminate the four tea categories with a 97.8% correct rate. In classification of oolong and other teas, there were one discriminant function and two equations with best discriminant capacity. Furthermore, the classification of different degrees of fermentation of oolong and external validation achieved the desired results. It is suggested that polyphenols and caffeine are the distinct variables to establish internationally recognized models of teas.