Application of Bioinformatics and Machine Learning Tools in Food Safety.

Abstract

Purpose of review: Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early and prevent disease outbreaks, while also optimizing food production and distribution processes.

Recent findings: This article discusses the role of new bioinformatics and machine learning technologies in promoting food safety and contamination control, along with various related articles in this field. By analyzing genetic and proteomic data, bioinformatics helps to quickly and accurately identify pathogens and sources of contamination. Machine learning, as a powerful tool for massive data processing, also can discover hidden patterns in the food production and distribution chain, which helps to improve risk prediction and control processes. By reviewing previous research and providing new solutions, this article emphasizes the role of these technologies in identifying, preventing, and improving decisions related to food safety. This study comprehensively shows how the integration of bioinformatics and machine learning can help improve food quality and safety and prevent foodborne disease outbreaks.