化学计量学
线性判别分析
主成分分析
数学
层次聚类
儿茶素
绿茶
原材料
化学
红茶
食品科学
色谱法
人工智能
模式识别(心理学)
聚类分析
多酚
统计
计算机科学
有机化学
抗氧化剂
作者
Shao-Rong Zhang,Yu Shi,Jielin Jiang,Liyong Luo,Liang Zeng
出处
期刊:Foods
[MDPI AG]
日期:2022-02-25
卷期号:11 (5): 680-680
被引量:2
标识
DOI:10.3390/foods11050680
摘要
Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, alkaloid, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.
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