Application of LogitBoost Classifier for Traceability Using SNP Chip Data

Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.

受动物相关疫病影响，消费者对食品安全的关注度快速攀升，因此出于安全管控需求，明确动物源性食品的原产地（Place of Origin, POO）至关重要。然而，目前针对该问题且聚焦于机器学习方法的相关研究仍较为匮乏。本研究采用定制化单核苷酸多态性（Single Nucleotide Polymorphism, SNP）芯片开展分类分析，以实现原产地预测。为完成该研究目标，本研究利用该SNP芯片对来自104个养殖场的4122头生猪进行了基因分型。研究团队基于上述基因分型数据，综合考量多项影响因素以构建最优预测模型。同时，本研究还通过亲缘系数筛选方法，对所提出的预测模型的适用性进行了评估。研究结果表明，在多数实验条件下，基于LogitBoost的预测模型在分类性能上均优于其他分类器。具体来看，当采用更高的亲缘系数截断值时，模型的预测准确率会显著提升。上述结果证实了基于SNP芯片数据的机器学习方法可有效应用于实际的溯源场景中。