An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile

文献类型： 外文期刊

第一作者： Sun, Yong

作者： Sun, Yong;Sun, Yong;Zhao, Yanfang;Liu, Nan;Kang, Xuming;Wang, Shanshan;Zhou, Deqing;Wu, Jifa

作者机构：

关键词： Geographical origins; Sea cucumber; Multi-element profile; Explainable machine learning; SHAP; XGBoost

期刊名称：FOOD CONTROL （ 影响因子：6.652； 五年影响因子：6.498 ）

ISSN： 0956-7135

年卷期： 2022 年 134 卷

页码：

收录情况： SCI

摘要： The geographical origin of sea cucumber Apostichopus japonicas plays an important role in determining its market value. This study investigated the feasibility of using multi-element profile combined with explainable machine learning to trace the origin of sea cucumber in China. Multi-element profile (23 elements) of 167 sea cucumber samples was determined with ICP-OES and ICP-MS, and used for construction and evaluation of 4 ensemble learning models. Extreme gradient boosting (XGBoost) model achieved superior performance with an overall accuracy, precision, recall, F1 score and AUC as 0.95, 0.93, 0.91 and 1, respectively. The Shapley Additive Explanations (SHAP) algorithm was subsequently applied to interpret the XGBoost model output for desirable geographical information. Se was identified as the most important elemental marker for discriminating sea cu-cumber origins. Therefore, with clarified scientific support, multi-element profile combined with machine learning model could serve as a powerful tool for identifying the provenance of sea cucumber.

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