Machine Learning in Cheese-Making: Methods, Applications, and the Future

Abstract

Cheese-making is a complex process involving numerous stages, with multiple factors contributing and complex interactions occurring among the physicochemical elements involved. Understanding the process and optimizing its stages has attracted the attention of numerous investigations. In recent years, Machine Learning (ML) has established itself as one of the most advanced tools for data analysis and modeling thanks to its ability to capture complex and non-linear patterns. In the area of food science and engineering, these algorithms have started to be used as an alternative to more traditional statistical and mathematical prediction models. This paper explores the main research on ML applied to the study of cheese, from its production stages (i.e., fermentation or coagulation process) to the final product (i.e., detection of adulterations or food fraud). Particularly, we review 42 papers published between January 2014 and January 2025, with the aim of identifying common approaches. First, we present an explanation of the main concepts required to bring these approaches closer to researchers who are not experienced in applying ML. Then, we analyze the selected publications to detail the tasks of interest and the algorithms proposed to solve them. Finally, we detect gaps and opportunities to incorporate ML into future cheese research.