Work

Distribution characteristics of catechins, alkaloids and gallic acid in Chinese famous green tea and its application in authenticity identification

Year: 2026

Type: article

Abstract: This study investigated the distribution of signature components in renowned Chinese green teas to develop a method for authenticity identification. By employing high-performance liquid chromatography-diode array detection (HPLC-DAD) coupled with the alternating trilinear decomposition (ATLD) algorithm, a second-order calibration model was constructed that effectively addressed the challenge of co-elution peaks in complex tea matrices. The method demonstrated satisfactory accuracy, with spiked recoveries ranging from 93.5% to 119.6% and relative standard deviations (RSDs) below 1.10% for all analytes. Using the established ATLD-enhanced HPLC method, a quantitative analysis of ten major components in 37 Chinese green teas was conducted, and their distribution patterns were visualized. Based on these quantitative results, multivariate statistical analysis methods, principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were subsequently employed to effectively utilize the data for authenticity discrimination concerning the geographical origin, variety, and grade of the teas. The results revealed that characteristic components varied significantly with tea origin, variety, and grade, enabling the authenticity identification of green tea. These findings not only confirm the efficacy of key catechins for authenticity identification but also establish an analytical strategy for quality evaluation, providing a powerful framework for industrial quality control and origin verification. • Second-order calibration enhanced HPLC-DAD method was used for quantification. • Characterizing major component distribution across renowned Chinese green tea. • Study confirmed the viability of tea authentication based on key components. • Key classified substances in grade classification vary among different green tea.

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2026.108889

Authors: Zheng-Yun He, Jun Xiang, Xing-Zi Ding, Qing-Qing Luo, Hui-Wen Gu, Xiao-Li Yin

Institutions: Yangtze University, Enshi Tujia and Miao Autonomous Prefecture Academy of Agricultural Sciences

Language: English

Topic: Tea Polyphenols and Effects

Subfield: Pathology and Forensic Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Estimated APC paid: $3,450

Funders: National Natural Science Foundation of China, Key Technologies Research and Development Program, National Key Research and Development Program of China