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Discriminating geographical origins of green tea based on amino acid polyphenol and caffeine content through high‐performance liquid chromatography: Taking Lu’an guapian tea as an example

机译:通过高效液相色谱鉴别基于氨基酸多酚和咖啡因含量的绿茶的地理起源:以六安瓜片茶为例

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摘要

Seventy‐three Lu'an guapian tea (LAGP) samples were collected from 11 growing locations in the city of Lu'an, Anhui Province, China. Through high‐performance liquid chromatography, 18 amino acids, along with gallic acid, caffeine, and five catechins, were quantitatively detected. Hierarchical cluster, correlation and principal component analysis, and a support vector machine were used for geographical discrimination. The findings suggested that the differences in tea quality between the inner and outer mountain regions are related to isoleucine, leucine, phenylalanine, and valine contents, with a correlation coefficient of more than 0.85. Principal component analysis combining with support vector machine was a feasible method. The identification rates for the inner and outer mountains were 97.96% in the training set and 95.83% in the prediction set. Furthermore, the identification rates for the three counties were 91.84% and 95.83% in the training and prediction sets, respectively.
机译:从中国安徽省六安市的11个生长地点收集了73份六安瓜茶(LAGP)样品。通过高效液相色谱法,定量检测了18种氨基酸以及没食子酸,咖啡因和5种儿茶素。使用层次聚类,相关性和主成分分析以及支持向量机进行地理区分。研究结果表明,内山区和外山区的茶叶质量差异与异亮氨酸,亮氨酸,苯丙氨酸和缬氨酸含量有关,相关系数大于0.85。主成分分析与支持向量机相结合是一种可行的方法。训练集中内外山的识别率分别为97.96%和预测集中95.83%。此外,在训练集和预测集中三个县的识别率分别为91.84%和95.83%。

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