您好，欢迎访问中国水产科学研究院 机构知识库！
中国水产科学研究院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国水产科学研究院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台