Machine Learning for Detoxification of Aflatoxin M1 by Lactococcus Lactis Probiotic in Kashk Production Publisher



M Jafari MARYAM ; Rr Nazari Roshanak RAFIEI ; Mj Rezaei Mohammad JAVAD ; M Moazzen MOJTABA ; N Shariatifar NABI
Source: Food Safety and Risk Published:2025

Abstract

This study investigates the potential of Lactococcus lactis in reducing the concentration of aflatoxin M1 (AFM1) in Iranian dairy products (kashk). First, different samples were prepared and then inoculated with a certain dose of L. lactis, and then AFM1 levels were evaluated during storage periods. The maximum activity of L. lactis in the removal of AFM1 toxin was observed at the level of 109 CFU/mL after 30 days of exposure and the lowest level of AFM1 removal was observed at the level of 107 CFU/mL after 5 days of exposure. The results showed that L. lactis, especially at higher concentrations, had a more significant potential in reducing the level of AFM1 in the samples. A model of ANN (Artificial Neural Network) and PCA (principal component analysis) are employed to efficiently address the intricate challenge of detoxifying AFM1 with the aid of L. lactis. The optimized neural network architecture (3-10-1) featuring 10 neurons demonstrates impressive correlation coefficient values (0.98). The findings offer valuable insights that highlight the ANN model’s ability to make accurate predictions. Therefore, the detoxification of AFM 1 using probiotics combined with machine learning methods presents a practical, feasible, and simple method for predicting detoxification processes based on various parameters related to the probiotic application in managing aflatoxin in dairy products. © 2025 Elsevier B.V., All rights reserved.