Work

Development of Machine-Learning Models for Predicting Escherichia coli O157:H7 Inactivation on Fresh-Cut Lettuce during Chlorine Washing

Year: 2025

Type: article

Abstract: 0.836) showing the greatest gains. All ML models maintained stability across validation folds, demonstrating strong generalizability. A user-friendly MATLAB application integrating the fitted models was released as open-source software to facilitate industry and regulatory uptake. These findings confirm the value of data-driven methods for modeling chlorine-wash performance and offer an accessible decision-support tool for optimizing process parameters, thereby helping to reduce the public-health burden associated with leafy-green-related outbreaks.

Source: Journal of Food Protection

DOI: 10.1016/j.jfp.2025.100686

Authors: Fatih Tarlak, Arícia Possas, Guiomar Denisse Posada-Izquierdo, Francisco Jiménez-Jiménez, Fernando Pérez-Rodríguez

Institutions: Gebze Technical University, University of Córdoba, Universitat de Miguel Hernández d'Elx

Language: English

Topic: Listeria monocytogenes in Food Safety

Subfield: Biotechnology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDGs: Clean water and sanitation, Zero hunger, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,300