Predicting Aflatoxin M₁ in Raw Milk Using Machine Learning and Basic Measurements
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Ding, Haohan
Wang, Long
Song, Xiaodong
Cui, Xiaohui
Wilson, David I
Yu, Wei
Zhang, Cheng
Dong, Guanjun
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Elsevier
Abstract
Aflatoxin 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.
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Aflatoxin M₁, Machine learning, Prediction model, Raw milk, 30 Agricultural, Veterinary and Food Sciences, 3006 Food Sciences, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering, 3006 Food sciences
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Current Research in Food Science, ISSN: 2665-9271 (Print); 2665-9271 (Online), Elsevier, 12, 101353-. doi: 10.1016/j.crfs.2026.101353
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© 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.
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Except where otherwise noted, this item's license is described as © 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.