Wang X, Bouzembrak Y, Oude Lansink A G J M, van der Fels-Klerx H J
Business Economics, Wageningen University, Wageningen, The Netherlands.
Wageningen Food Safety Research, Wageningen, The Netherlands.
NPJ Sci Food. 2022 Sep 1;6(1):40. doi: 10.1038/s41538-022-00154-2.
Agricultural commodities used for feed and food production are frequently contaminated with mycotoxins, such as Aflatoxin B1 (AFB1). In Europe, both the government and companies have monitoring programs in place for the presence of AFB1. With limited resources and following risk-based monitoring as prescribed in EU Regulation 2017/625, these monitoring programs focus on batches with the highest probability of being contaminated. This study explored the use of machine learning algorithms (ML) to design risk-based monitoring programs for AFB1 in feed products, considering both monitoring cost and model performance. Historical monitoring data for the presence of AFB1 in feed products (2005-2018; 5605 records in total) were used. Four different ML algorithms, including Decision tree, Logistic regression, Support vector machine and Extreme gradient boosting (XGB), were applied and compared to predict the high-risk feed batches to be considered for further AFB1 sampling and analysis. The monitoring cost included the cost of: sampling and analysis, disease burden, storage, and of recalling and destroying contaminated feed batches. The ML algorithms were able to predict the high-risk batches, with an AUC, recall, and accuracy higher than 0.8, 0.6, and 0.9, respectively. The XGB algorithm outperformed the other three investigated ML. Its incorporation would result into up to 96% reduction in monitoring cost in 2016-2018, as compared to the official monitoring program. The proposed approach for designing risk based monitoring programs can support authorities and industries to reduce the monitoring cost for other food safety hazards as well.
用于饲料和食品生产的农产品经常被霉菌毒素污染，如黄曲霉毒素B1（AFB1）。在欧洲，政府和企业都有针对AFB1存在情况的监测计划。由于资源有限且遵循欧盟法规2017/625规定的基于风险的监测，这些监测计划侧重于污染可能性最高的批次。本研究探讨了使用机器学习算法（ML）来设计基于风险的饲料产品中AFB1监测计划，同时考虑监测成本和模型性能。使用了饲料产品中AFB1存在情况的历史监测数据（2005 - 2018年；共5605条记录）。应用并比较了四种不同的ML算法，包括决策树、逻辑回归、支持向量机和极端梯度提升（XGB），以预测需要进一步进行AFB1采样和分析的高风险饲料批次。监测成本包括：采样和分析成本、疾病负担、储存成本以及召回和销毁受污染饲料批次的成本。ML算法能够预测高风险批次，其曲线下面积（AUC）、召回率和准确率分别高于0.8、0.6和0.9。XGB算法的表现优于其他三种研究的ML算法。与官方监测计划相比，在2016 - 2018年将其纳入可使监测成本降低多达96%。所提出的基于风险的监测计划设计方法也可以支持当局和行业降低其他食品安全危害的监测成本。
