数字农科院2.0

An efficient method for tracing the geographic origin of Enshi Yulu fresh tea leaves based on near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

文献类型： 外文期刊

作者： Shengpeng Wang;Lin Feng;Panpan Liu;Anhui Gui;Shiwei Gao;Jing Teng;Fei Ye;Zixiang Jiang;Xueping Wang;Jinjin Xue;Pengcheng Zheng;Zhiyu Song

作者机构：

关键词： Fresh tea leaves;Genetic algorithm;Geographic origin;Near infrared spectroscopy;Partial least squares

期刊名称： LWT

ISSN： 0023-6438

年卷期： 2024 年 203 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry.

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