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Predicting aflatoxin M₁ in raw milk using machine learning and basic measurements.

Haohan Ding^1,2, Long Wang¹, Xiaodong Song³

¹School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.

Current Research in Food Science

|March 2, 2026

Summary

This summary is machine-generated.

This study introduces a machine learning method to predict aflatoxin M1 (AFM1) in raw milk using routine tests. This approach offers a cost-effective way to screen for AFM1, ensuring dairy product safety.

Keywords:

Aflatoxin M1 Machine learning Prediction model Raw milk

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Area of Science:

Food Science
Analytical Chemistry
Computational Biology

Background:

Aflatoxin M1 (AFM1) is a harmful mycotoxin found in raw milk, necessitating continuous monitoring for dairy safety.
Traditional lab methods for AFM1 detection are accurate but costly and time-consuming, limiting their use in high-volume screening.

Purpose of the Study:

To develop a cost-effective, qualitative method for predicting AFM1 presence in raw milk.
To evaluate machine learning algorithms for prescreening AFM1 levels using routine physicochemical indicators.

Main Methods:

Five machine learning models were assessed for binary classification of AFM1 levels against regulatory thresholds.
Physicochemical indicators routinely measured in raw milk were used as input features.

Main Results:

The multilayer perceptron model demonstrated over 80% accuracy and negative-sample recall.
Machine learning shows potential as an effective tool for AFM1 prescreening.

Conclusions:

Machine learning offers a feasible, rapid, and economical approach for large-scale raw milk safety monitoring.
This method complements traditional techniques, enhancing dairy safety surveillance.