Ding, HaohanWang, LongSong, XiaodongCui, XiaohuiWilson, David IYu, WeiZhang, ChengDong, Guanjun2026-05-182026-05-182026-02-16Current Research in Food Science, ISSN: 2665-9271 (Print); 2665-9271 (Online), Elsevier, 12, 101353-. doi: 10.1016/j.crfs.2026.1013532665-92712665-9271http://hdl.handle.net/10292/21112Aflatoxin M₁ (AFM₁) is a carcinogenic and teratogenic mycotoxin that may be present in raw milk. Therefore, continuous monitoring of AFM₁ levels is essential to ensure dairy safety and regulatory compliance. Although laboratory-based analytical techniques such as ELISA and LC-MS/MS offer high accuracy, their cost, sample preparation requirements, and dependence on specialized personnel make them less practical for high-frequency or large-volume screening in dairy processing facilities. This creates a need for complementary, cost-effective prescreening approaches. This study proposed a qualitative AFM₁ prediction method based on routinely measured physicochemical indicators of raw milk, combined with machine learning algorithms. Five classical machine learning models were evaluated under a binary classification framework to determine whether AFM₁ levels exceed the regulatory threshold. Experimental results show that the multilayer perceptron achieves an accuracy and negative-sample recall rate above 80%, demonstrating the potential of machine learning as an effective prescreening tool for AFM₁. The findings provide a feasible direction for supporting rapid, economical, and large-scale monitoring of raw milk safety.© 2026 The Authors. Published by Elsevier B.V. Note: This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.https://creativecommons.org/licenses/by/4.0/Aflatoxin M₁Machine learningPrediction modelRaw milk30 Agricultural, Veterinary and Food Sciences3006 Food SciencesNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceBioengineering3006 Food sciencesJournal ArticleOpenAccess10.1016/j.crfs.2026.101353