Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen

文献类型： 外文期刊

第一作者： Yu, Daixin

作者： Yu, Daixin;Qu, Cheng;Wen, Pei;Yan, Hui;Wu, Qinan;Nie, Jing;Yuan, Yuwei;Zhao, Yuyang;Dai, Caiyan

作者机构：

期刊名称：NPJ SCIENCE OF FOOD （ 影响因子：7.8； 五年影响因子：7.0 ）

ISSN：

年卷期： 2025 年 9 卷 1 期

页码：

收录情况： SCI

摘要： Euryales Semen (ES, Euryale ferox Salisb.) is a valuable aquatic food in Asia. Its quality and price depend on its geographical origins. To ensure the authenticity of ES, a tracing strategy using stable isotopes, elements, and starch composition with interpretable algorithms was successfully developed. Results indicated that ES from different regions exhibited different chemical fingerprinting profiles. Tree-based intelligent algorithms were introduced for classification, and light gradient boosting machine (LightGBM) achieved the highest accuracy of 97.67%. The SHapley Additive exPlanation (SHAP) interpreted the LightGBM output for feature impact. Notably, the top 10 significant variables, encompassing Na, V, Ba, Sb, Cu, Ti, Mn, %N, amylose, and ratio of amylose to amylopectin (SHAP value >1.0), were selected as the key factors. Moreover, environmental factors were found to be significantly related to these key variables (p < 0.05). Overall, this study offers an effective strategy for the geographical origin traceability of ES or other aquatic crops.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序