Abstract
ABSTRACT
The constituents of tea leaves are crucial determinants of the quality and nutritional value of tea. Traditional evaluation methods, which depend on sensory assessment or isolated indicators, have certain limitations. Consequently, this project aimed to develop a multidimensional and systematic comprehensive evaluation system for assessing tea nutrition and quality. In this study, the quality of five tea varieties from Guizhou, China, namely, Qingyu tiny kuding tea, Guiding yunwu tribute tea, Suiyang mountain silver flower tea, Shiqian moss tea, and Pu'an black tea, was comprehensively evaluated by integrating combustion heat, combustibility/combustion stability, ash, crude fiber, fat, trace element (ICP–OES), amino acid (A300 analyzer), and thermogravimetric parameter information. The results indicated that Qingyu tiny kuding tea was the highest quality, followed by Shiqian moss tea, according to the results of the multi‐indicator evaluation (
F
= −0.5711–0.9953). Gray pattern recognition (GPR) revealed that the most stable combustion occurred for Suiyang Mountain silver flower tea, followed by Guiding Yunwu tribute tea, Shiqian moss tea, Qingyu tiny kuding tea, and Pu'an black tea (
F
= 0.8102–0.8023). EFCA revealed that the tea samples could be categorized into three distinct groups, while the 38 variables could be classified into three clusters. OPLS–DA achieved strong discrimination (
R
2
X
= 0.887) and identified 21 key differentiators (VIP > 1) as key differentiators among the five tea types. This study provides a solid scientific foundation for the large‐scale development of tea resources, research on tea classification, and the comprehensive evaluation of tea quality in Guizhou, China.