A Review on Modeling the Fermentation Process of Dairy Products Using Multi-Omics and Artificial Intelligence Approaches

Abstract

In dairy production, the fermentation process is a complex biochemical system that plays a significant role in determining the quality criteria of the final product. Traditional methods for controlling fermentation rely on limited and non-standard process parameters. In recent years, omics technologies have come to the forefront, enabling the monitoring of fermentation dynamics at the molecular level with their current, efficient, and reliable approaches. Thanks to omics approaches such as metabolomics, metagenomics, proteomics, and lipidomics, starter culture behavior, metabolite formation, aroma–texture formation, and microbial interactions in the fermentation process can be characterized more comprehensively. On the other hand, evaluating or calculating high-dimensional omics data using traditional statistical methods presents a challenge. Artificial intelligence applications are overcoming this challenge, offering significant opportunities for the accurate and reliable evaluation of data. Artificial intelligence-powered models hold promise in areas such as predicting fermentation kinetics, process control, optimizing quality criteria, and revealing the therapeutic potential of products through metabolites. This compilation aims to comprehensively address current approaches to modeling the fermentation process and quality parameters of dairy products using multi-omics technologies and artificial intelligence applications. In this respect, it will provide current and important perspectives for industrial applications and future studies.