Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis
文献类型： 外文期刊
作者： Kang, Xuming 1 ; Zhao, Yanfang 1 ; Yao, Lin 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： Oyster; Elemental profile; Explainable machine learning; SHAP; Geographical origin
期刊名称：CURRENT RESEARCH IN FOOD SCIENCE （ 影响因子：6.3； 五年影响因子：6.3 ）
ISSN：
年卷期： 2024 年 8 卷
页码：
收录情况： SCI
摘要： The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.
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