Data-Driven Machine Learning and Deep Learning Approaches as Practical AI Models in Predictive Food Microbiology

Predictive food microbiology helps to ensure food safety by predicting development, detecting diseases, and assessing hazards. However, traditional prediction models have limitations in effectively capturing the complex interactions of microbes in dynamic environments. In contrast, AI-based models can automatically learn complex relationships, uncover non-linear patterns, and generate more accurate risk predictions from high-dimensional data using data-driven approaches such as machine learning (ML), deep learning (DL), fuzzy logic, evolutionary algorithms, and expert systems. Machine learning (ML) is a subsystem of AI that provides model learning from data, while deep learning (DL) is a subsystem of machine learning (ML). Stochastic Gradient Boosting (SGB), Random Forest (RF), classification and regression trees (CART), and Support Vector Machines (SVM) are the machine learning (ML) models. This review focuses on the role of data-driven approaches, particularly machine learning (ML) and deep learning (DL), in advancing predictive food microbiology, where these models have increasingly been adopted for forecasting microbial growth, spoilage dynamics, and contamination risks due to their superior ability to learn complex, nonlinear relationships from experimental data. The incorporation of AI models promises to deliver disruptive solutions for reducing foodborne hazards and enhancing global food safety standards.