Advances and Emerging Trends in Monitoring and Predictive Technologies for Yoghurt Fermentation

Yoghurt fermentation is a dynamic and multiscale process in which microbial activity, mineral equilibrium, and protein reorganization collectively govern acidification kinetics, gel formation, texture, and product consistency. Conventional monitoring approaches, predominantly based on intermittent pH or titratable acidity measurements, provide limited insight into early biochemical, ionic, and microstructural transitions that critically influence fermentation outcomes. In response to these limitations, a wide range of advanced, non-destructive, and real-time monitoring techniques has emerged in recent years. This review systematically consolidates and evaluates developments across acoustic and mechanical sensing, optical spectroscopy and imaging, electrochemical and dielectric methods, molecular tools, and artificial intelligence–enabled hybrid systems for yoghurt fermentation monitoring and prediction. The comparative analysis links each technique to the specific physicochemical or biological phenomena it interrogates, while assessing practical considerations such as invasiveness, cost, data complexity, and industrial readiness. The review highlights how different methods complement one another in capturing acidification dynamics, casein micelle destabilization, gelation, and microbial performance, and emphasizes the growing role of data fusion and machine learning in enabling predictive and automated process control. By integrating dispersed advances into a single framework, this work addresses the lack of a unified perspective on yoghurt fermentation monitoring and provides a structured foundation for selecting, combining, and deploying sensing technologies in research, pilot, and industrial dairy processing environments. Overall, this review serves as a practical reference to rationally select and integrate advanced sensing and data-driven tools for yoghurt fermentation across laboratory and industrial scales.