地理来源的可追溯性对于保证质量至关重要,安全,和保护牡蛎品牌。然而,当前可追溯性的结果缺乏可信度,因为它们不能充分解释模型的预测。因此,我们进行了一项研究,以评估利用可解释的机器学习结合矿质元素分析的功效。研究结果表明,有18个元素具有确定区域方向的能力。同时,由于牡蛎所含的必需和有毒元素的地区差异,个人应更加关注与牡蛎消费相关的潜在风险。光梯度增压机(LightGBM)模型表现出难以区分的性能,实现完美的准确性,精度,召回,F1评分和AUC,值为96.77%,96.43%,98.53%,分别为97.32%和0.998。Shapley加法扩张(SHAP)方法用于评估LightGBM模型的输出,揭示不同省份牡蛎特征相互作用的差异。具体来说,Na的特征,Zn,V,Mg,和K对模型的预测过程有显著影响。与现有研究一致,使用可解释的机器学习技术可以深入了解重要产品属性和相关地理信息之间的复杂联系。
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.