数字农科院2.0

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

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

作者机构：

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

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 469 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

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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