食品科学2025，Vol.46Issue(19)：1-9,9.DOI:10.7506/spkx1002-6630-20250303-013
基于Transformer架构的原奶中黄曲霉毒素的定性预测
Qualitative Prediction of Aflatoxin in Raw Milk Based on Transformer Architecture
摘要
Abstract
In this study,using machine learning and deep learning techniques,we collected raw milk composition data from different regions and seasons in China during the period of 2022-2024 and proposed a method for qualitative prediction of aflatoxin M1(AFM1)based on easy-to-measure data,aiming to reduce the cost of batch testing in dairy factories.Based on the 16 selected classes of feature datasets,we conducted prediction experiments using various machine learning methods such as linear regression(LR),random forest(RF),support vector machine(SVM)and a method based on Transformer architecture,and analyzed the prediction performance and variance stability of these models on negative samples and positive samples through comparative experiments.The experimental results confirmed that the prediction method based on Transformer architecture had the best overall performance.Meanwhile,we also explored the effect of location coding and attention mechanism on model performance under Transformer architecture through ablation experiments.Overall,the new method based on deep learning enabled efficient qualitative prediction of AFM1,which can meet the demand for high throughput and significantly reduce the detection cost by eliminating redundant detection steps when compared with the traditional method,providing a solution of digital transformation and a theoretical basis for model optimization for dairy product safety detection.关键词
食品安全/机器学习/深度学习/黄曲霉毒素/定性预测Key words
food safety/machine learning/deep learning/aflatoxin/qualitative prediction分类
轻工纺织引用本文复制引用
王龙,宋晓东,丁浩晗,董冠军,崔晓晖,黄骅迪,张程,乌日娜..基于Transformer架构的原奶中黄曲霉毒素的定性预测[J].食品科学,2025,46(19):1-9,9.基金项目
"十四五"国家重点研发计划重点专项(2024YFE0199500 （）
2022YFF1101100) （）
