Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg

文献类型： 外文期刊

第一作者： Bai, Lu

作者： Bai, Lu;Zhang, Zixuan;Chen, Chengqun;Liang, Kehong;Bai, Lu;Zhang, Zixuan;Li, Yalan;Zhao, Shanshan;Yang, Xiaoting;Chen, Chengqun;Zhao, Shilin;Zhao, Yan;Zhang, Ping;Peng, Xin

作者机构：

关键词： Origin traceability; Stable isotope ratio; Multi-elements analysis; Discriminant analysis; Climatic factors

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 469 卷

页码：

收录情况： SCI

摘要： Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is an edible plant with considerable medicinal properties, the quality of which varies depending on its origin. Therefore economically motivated adulteration has emerged. So there is an urgent need to develop effective techniques for determining the origin of T. hemsleyanum. This study combined stable isotopes and multiple elements with machine learning chemometric models (SVM, RF and FNN models) for T. hemsleyanum origin traceability. The results showed that this approach successfully distinguished T. hemsleyanum form different regions with the SVM, RF and FNN models all displaying a 100 % prediction accuracy and the FNN model exhibiting superior performance. This study provides a technical and theoretical basis for research on the origin traceability of T. hemsleyanum.

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