Abstract
Chun Mee green tea (CMGT), one of the major export teas from China, has a comprehensive classification (grading) system. However, the chemical constituents underlying grade differentiation, as well as their associations with astringency and bioactivity, remain unclear. In this study, we integrated quantitative descriptive analysis (QDA), tea-mucin complex analysis, in vitro bioactivity evaluations, and metabolomics, supported by Random Forest (RF) modeling and correlation analysis. QDA demonstrated that higher-grade samples exhibited superior overall sensory quality and stronger astringency. Tea-mucin complex analysis (turbidity, particle size, SEM) confirmed reduced aggregation in lower grades, consistent with weaker astringency. Quantitative analysis revealed a significant decrease in major flavor compounds (EGCG, caffeine, and amino acids) as the grade declined. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics further classified samples into high-grade (T1, T2, T3) and low-grade (T4, T5, T6, 1, 2) clusters, identifying 68 grade-differentiating markers. High-grade teas exhibited stronger antioxidant activity and inhibition effects on α-amylase and α-glucosidase. RF and correlation analysis revealed that catechins, phenolic acids, hydrolysable tannins, and acylated quercetin glycosides were critical for grading, with trans-p-coumaroylquinic acid and its derivatives contributing significantly to both astringency and antioxidant capacity. In addition, a grade estimation model based on four compounds (5-galloylquinic acid, EGCG, ECG, GCG) was constructed using elastic net and ridge regression, achieving high accuracy (R2 = 0.995, RMSE = 0.168). Commercial samples verification confirmed the model’s reliability for ranking CMGT grades from the same factory. These findings provide an objective approach for grade evaluation and may facilitate the refinement of CMGT grading standards.
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