An explainable machine learning for geographical origin traceability of mussels Mytilus edulis based on stable isotope ratio and compositions of C, N, O and H
文献类型： 外文期刊
作者： Kang, Xuming 1 ; Zhao, Yanfang 1 ; Tan, Zhijun 1 ;
作者机构： 1.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Key Lab Testing & Evaluat Aquat Prod Safety & Qual, Minist Agr & Rural Affairs, Qingdao 266071, Peoples R China
2.Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao 266071, Peoples R China
3.Dalian Polytech Univ, Collaborat Innovat Ctr Seafood Deep Proc, Dalian 116034, Peoples R China
关键词： Mussel; Stable isotopes; Elemental composition; Explainable machine learning; SHAP; Traceability
期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）
ISSN： 0889-1575
年卷期： 2023 年 123 卷
页码：
收录情况： SCI
摘要： Geographical traceability of marine bivalves has been explored continually, however the traceability results have not been recognized owing to the absence of a reasonable explanation for a certain model prediction. To address this problem, we employed an explainable machine learning to identify the origin of mussels based on stable isotope ratio and compositions of carbon (C), nitrogen (N), oxygen (O), and hydrogen (H). Our findings proved that the Extreme gradient boosting (XGBoost) model may be the best model based upon its high accuracy (93.75 %), precision (93.75 %), recall (94.51 %), F1 score (93.72 %) and AUC (0.990). The feature impact for model out of XGBoost was interpreted by SHapley Additive exPlanations (SHAP) on global and local perspective. This study demonstrated the potential of explainable machine learning to elucidate the complex relationships between desirable geographical information of mussels and the selected features.
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