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

Fatty acid degradation driven by heat during ripening contributes to the formation of the "Keemun aroma"

Ripening refers to the process of chemical change during the refinement of Keemun black tea (KBT) and is crucial in the formation of Keemun Congou black tea's quality. In this study, the aroma composition of KBT during the ripening was analyzed. Sensomics indicated that ripening strengthened the coconut and fatty aroma of KBT and contributed to the decrease of green aroma substances, resulting in a shift of the overall aroma type of KBT to an integrated aroma profile, which was consistent with sensory evaluation. Changes in fatty acid content and the results of in vitro addition simulation tests confirmed that heat causes highly degradation of fatty acids into fatty aroma volatiles, which is a key driver of the formation of "Keemun aroma" quality. This study revealed the mechanism behind the formation of KBT's integrated "Keemun aroma" quality and the mode of thermal degradation of major fatty acids.

The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes

Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Investigation of the effect of over-fired drying on the taste and aroma of Lu'an Guapian tea using metabolomics and sensory histology techniques

Lu'an Guapian (LAGP) tea, a representative Chinese roasted green tea, undergoes significant changes in taste and aroma during over-fired drying. However, limited studies have been conducted on these effects. This study employed metabolomics and sensory histology techniques to analyze non-volatile and volatile compounds the second drying and pulley liquefied gas drying (PLD) samples. The results revealed that after PLD, the samples exhibited lower umami, bitterness, and astringency; whereas floral, sweet, roasted, cooked corn-like, and cooked chestnut-like aromas became stronger. Among them, the content of (-)-epigallocatechin gallate, glutamic acid, and theogallin, which were closely related to taste, decreased by 4.5 %, 12.3 %, and 10.4 %, respectively. Eight key aroma components were identified as the main contributors to the sample aroma changes: (E)-β-ionone, dimethyl sulfide, (E,E)-2,4-heptadienal, geraniol, linalool, benzeneacetaldehyde, 2-ethyl-3,5-dimethylpyrazine, and hexanal. This study provides a theoretical basis for enhancing the quality of LAGP teas.

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Improving flavor of summer Keemun black tea by solid-state fermentation using Cordyceps militaris revealed by LC/MS-based metabolomics and GC/MS analysis

Cordyceps militaris (C. militaris) has been approved and widely used in healthy food. The present study aimed to improve the flavor of summer Keemun black tea (KBT) using C. militaris solid-state fermentation. Combined with sensory evaluation, the volatile and non-volatile components of solid-state fermentation of KBT (SSF-KBT) and KBT were analyzed. The results showed that after the solid-state fermentation, the contents of total polyphenol, total flavonoid, and total free amino acids were significantly reduced. Further non-targeted metabolomics analysis revealed that the contents of non-galloylated catechins and d-mannitol increased, while the galloylated catechins and flavonoid glycosides decreased as did the bitterness and astringency of KBT. Dihydro-β-ionone and β-ionone (OAV = 59321.97 and 8154.17) were the aroma-active compounds imparting woody and floral odors in SSF-KBT, respectively. Current study provides a new avenue to develop summer-autumn KBT.

Targeted HPLC-UV Polyphenolic Profiling to Detect and Quantify Adulterated Tea Samples by Chemometrics

Tea can be found among the most widely consumed beverages, but it is also highly susceptible to fraudulent practices of adulteration with other plants such as chicory to obtain an illicit economic gain. Simple, feasible and cheap analytical methods to assess tea authentication are therefore required. In the present contribution, a targeted HPLC-UV method for polyphenolic profiling, monitoring 17 polyphenolic and phenolic acids typically described in tea, was proposed to classify and authenticate tea samples versus chicory. For that purpose, the obtained HPLC-UV polyphenolic profiles (based on the peak areas at three different acquisition wavelengths) were employed as sample chemical descriptors for principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) studies. Overall, PLS-DA demonstrated good sample grouping and discrimination of chicory against any tea variety, but also among the five different tea varieties under study, with classification errors below 8% and 10.5% for calibration and cross-validation, respectively. In addition, the potential use of polyphenolic profiles as chemical descriptors to detect and quantify frauds was evaluated by studying the adulteration of each tea variety with chicory, as well as the adulteration of red tea extracts with oolong tea extracts. Excellent results were obtained in all cases, with calibration, cross-validation, and prediction errors below 2.0%, 4.2%, and 3.9%, respectively, when using chicory as an adulterant, clearly improving on previously reported results when using non-targeted HPLC-UV fingerprinting methodologies.

Production regions discrimination of Huangguanyin oolong tea by using the content of chemical components and rare earth elements

Oolong tea is one of the most popular tea beverages in China. Tea cultivars, processing technology and origin of production affect the quality and price of oolong teas. To investigate the differences in Huangguanyin oolong tea from different production regions, the chemical components, mineral elements and rare earth elements of Huangguanyin oolong tea produced in Yunxiao (YX) and Wuyishan (WY) were analyzed by using spectrophotometry methods, targeted metabolomics and inductive plasma coupled mass spectrometry (ICP-MS). The results of spectrophotometry methods revealed that there were significant differences in thearubigin, tea polyphenols and water extract between Huangguanyin oolong teas from different production regions. Targeted metabolomics identified a total of 31 chemical components in Huangguanyin oolong teas from the two production regions, of which 14 chemical components were significantly different and contributed to the regional differentiation of Huangguanyin oolong tea. Yunxiao Huangguanyin had relatively higher contents of (-)-Epigallocatechin-3-O-(3-O-methylgallate) (EGCG3″Me), ornithine (Orn) and histidine (His), while Wuyishan Huangguanyin had relatively higher contents of glutamic acid (Glu), γ-aminobutyric acid (GABA), β-aminobutyric acid (β-ABA) and other components. Moreover, ICP-MS identified a total of 15 mineral elements and 15 rare earth elements in Huangguanyin oolong tea from the two production regions, of which 15 elements were significantly different between YX and WY, and contributed to the regional differentiation of Huangguanyin oolong tea. K had a relatively higher content in Yunxiao Huangguanyin, while rare earth elements had relatively higher contents in Wuyishan Huangguanyin. The classification results by the production region showed that the discrimination rate of the support vector machine (SVM) model based on the 14 different chemical components reached 88.89%, while the SVM model based on the 15 elements reached 100%. Therefore, we used targeted metabolomics and ICP-MS techniques to screen and explore the chemical components, mineral elements and rare earth elements differences among two production regions, which indicated the feasibility of Huangguanyin oolong tea classification by production regions in the study. The results will provide some reference for the distinction between the two production regions of Huangguanyin oolong tea.

Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method

BACKGROUND

Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy.

RESULTS

First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.

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.

Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy

Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing.

Identification of 4-O-p-coumaroylquinic acid as astringent compound of Keemun black tea by efficient integrated approaches of mass spectrometry, turbidity analysis and sensory evaluation

Hydroxycinnamoyl quinic acids are important phenolic acids in tea, particularly fermented teas. However, there have been fewer studies that have confirmed their taste properties. The aim of this study was to investigate the astringent compounds in Keemun congou black tea (KBT) using a combination of mass spectrometry, turbidity analysis, and sensory evaluation. Turbidity analysis determined that p-coumaroylquinic acids were the astringent contributing compounds in KBT. Moreover, the separated compound D16 was identified as trans-4-O-p-coumaroylquinic acid (trans-4-O-pCoQA) by nuclear magnetic resonance spectroscopy and first confirmed to be the astringent contributing compound in KBT by sensory evaluation. Its astringent threshold concentration was tested to be 38 µM. The trans-4-O-pCoQA content in eight KBT samples of various grades ranged from 40.20 ± 0.15 ~ 65.53 ± 0.22 µM. Turbidity analysis combined with sensory evaluation could be a powerful tool for identifying critical compounds responsible for the astringent taste.

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.

Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

Background

Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements.

Methods

We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ¹³C, δ¹⁵N, δ¹⁸O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin.

Results

K, Ca/Al, δ¹³C, δ¹⁵N and δ¹⁸O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes.

Conclusions

The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

Use of headspace GC/MS combined with chemometric analysis to identify the geographic origins of black tea

Some black teas demand high market prices. Black tea samples (306) collected from 10 geographic origins, including China (Guxi, Likou, Jinzipai, Guichi, Dongzhi, Changning, Wuyishan, Shaowu), India (Darjeeling), and Sri Lanka (Kandy), were analyzed using headspace volatilization followed by GC/MS (HS-GC/MS). Forty-eight volatile compounds were identified. The aroma compounds were mainly identified as alcohols, aldehydes, ketones, and esters. Analysis of either full-spectrum data or 22 tea compounds shared among the samples with k-Nearest Neighbor (k-NN) and Random Forest (RF) models discriminated all origins at 100% using KNN and 95% with RF using either data set. The discrimination rates using 2 key aroma compounds (linalool and geraniol) by k-NN were 100% for nine origins, with the rate for Guxi area at 89%, because 3 samples were classified to Jinzipai. The findings support the use of HS-GC/MS combined with chemometrics as a tool to identify the origin of black tea.

Targeted and nontargeted metabolomics analysis for determining the effect of storage time on the metabolites and taste quality of keemun black tea

The black tea could be stored for a long time, and subsequently affects the flavor characteristics. In the present study, the effects of storage years (1, 2, 3, 4, 5, 10, 17 and 20 years) on the chemical profiling and taste quality of keemun black tea (KBT) were compared by metabolomics and quantitative sensory evaluation. The main polyphenols were degraded during the storing, especially 10-year storage, but caffeine and theobromine were stable. The intensity of bitterness, astringency, umami was negatively correlated to storage years, with correlation coefficient at -0.95, -0.91 and -0.83 respectively, whereas sweetness had positive correlation coefficient at 0.74. Quinic acid, galloylated catechins, linolenic acid, linoleic acid, malic acid, palamitic acid, and theaflavin-3́-gallate were marker compounds which were responsible for distinguishing short and long time preserved KBT. The contents of fatty acids were positively correlated to storage time and sweet intensity.

Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system

Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.

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.

Metabolite signatures of diverse Camellia sinensis tea populations

The tea plant (Camellia sinensis) presents an excellent system to study evolution and diversification of the numerous classes, types and variable contents of specialized metabolites. Here, we investigate the relationship among C. sinensis phylogenetic groups and specialized metabolites using transcriptomic and metabolomic data on the fresh leaves collected from 136 representative tea accessions in China. We obtain 925,854 high-quality single-nucleotide polymorphisms (SNPs) enabling the refined grouping of the sampled tea accessions into five major clades. Untargeted metabolomic analyses detect 129 and 199 annotated metabolites that are differentially accumulated in different tea groups in positive and negative ionization modes, respectively. Each phylogenetic group contains signature metabolites. In particular, CSA tea accessions are featured with high accumulation of diverse classes of flavonoid compounds, such as flavanols, flavonol mono-/di-glycosides, proanthocyanidin dimers, and phenolic acids. Our results provide insights into the genetic and metabolite diversity and are useful for accelerated tea plant breeding.

Analytical strategy coupled to chemometrics to differentiate Camellia sinensis tea types based on phenolic composition, alkaloids, and amino acids

Catechins, amino acids, and alkaloids are primary chemical components of tea and play a crucial role in determining tea quality. Their composition and content largely vary among different types of tea. In this study, a convenient chemical classification method was developed for six Camellia sinensis tea types (white, green, oolong, black, dark, and yellow) based on the quantification of their major components. Twenty-one free amino acids, 6 catechins, 2 alkaloids, and gallic acid in 24 teas were quantified using ultra-high-performance liquid chromatography (UHPLC). The total catechin contents in these tea samples ranged from 10.96 to 95.67 mg/g, while total free amino acid content ranged from 2.63 to 25.89 mg/g. Theanine (Thea) was the most abundant amino acid in all tea varieties. Catechin and amino acid levels in tea were markedly reduced upon fermentation of tea. Furthermore, high-temperature processing (roasting) during tea production induced degradation and epimerization of catechins, yielding epimerized catechins, simple catechins, and gallic acid. Principal component analysis revealed that major ester-catechins (EGCG and ECG), major amino acids (Thea), and major alkaloids (caffeine) are potential factors for distinguishing different types of tea. Linear discriminant analysis showed that 100% of teas were correctly classified in which (+)-catechin, ECG, EGC, gallic acid, GABA, cysteine, lysine, and threonine were the most discriminating compounds. This study shows that quantification of the major tea components combined with chemometric analysis, can serve as a simple, convenient, and reliable approach for classifying tea according to fermentation level. PRACTICAL APPLICATION: Different Camellia sinensis tea types can be produced worldwide but it is still challenging to know which chemical markers can be used to trace their production. in this paper we used a targeted methodology to classify six tea types (white, green, oolong, black, dark, and yellow) based on phenolic composition, alkaloids, and amino acids. The main chemical markers responsible for the discrimination were pinpointed with the use of chemometric tools.