Work

Exploring Machine Learning Methods for Aflatoxin M1 Prediction in Jordanian Breast Milk Samples

Year: 2024

Type: article

Abstract: The presence of aflatoxin M1 in breast milk poses a serious risk to the health of infants because of its potential to cause cancer and have negative effects on development. Detecting AFM1 in milk samples using conventional methods is often time-consuming and may not provide real-time monitoring capabilities. The use of machine learning techniques to forecast aflatoxin M1 levels in breast milk samples is examined in this study. To develop predictive models of aflatoxin M1 in breast milk, we employed well-known supervised machine learning algorithms such as Random Forest and Gradient Boosting. The findings show that machine learning can be used for the identification of aflatoxin M1 in breast milk. By actively monitoring breast quality, this research highlights the significance of machine learning in protecting babies’ health and advances the prediction skills in food safety.

Source: Computers

DOI: 10.3390/computers13110288

Authors: Abdullah Aref, Eman Omar, Eman Alseidi, Nour Elhuda A. Alqudah, Sharaf Omar

Institutions: Princess Sumaya University for Technology, Hashemite University, Philadelphia University, Yarmouk University, Al-Balqa Applied University

Language: English

Topic: Mycotoxins in Agriculture and Food

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,046