Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning

Abstract

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, two studies have reviewed ML applications on foodborne disease and deep learning applications on food.This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, and CAB abstracts, and IEEE. It includes studies that were published in English in the period January 2011-1 April 2021. Results show that most studies applied Bayesian Networks (BN), Neural Networks (NN), or Support Vector Machines (SVM). Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.