Arijit Ray, Chitranayak Sinha, Sheetal Berry, P. S. Minz, Kaushik Khamrui, Robin Kaura, Shaik Abdul Hussain, A. K. Sharma, J. K. Dabas
{"title":"Advances and Emerging Trends in Monitoring and Predictive Technologies for Yoghurt Fermentation","authors":"Arijit Ray, Chitranayak Sinha, Sheetal Berry, P. S. Minz, Kaushik Khamrui, Robin Kaura, Shaik Abdul Hussain, A. K. Sharma, J. K. Dabas","doi":"10.1007/s12393-026-09452-x","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":565,"journal":{"name":"Food Engineering Reviews","volume":"18 2","pages":""},"PeriodicalIF":13.5000,"publicationDate":"2026-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Engineering Reviews","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s12393-026-09452-x","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Food Engineering Reviews publishes articles encompassing all engineering aspects of today’s scientific food research. The journal focuses on both classic and modern food engineering topics, exploring essential factors such as the health, nutritional, and environmental aspects of food processing. Trends that will drive the discipline over time, from the lab to industrial implementation, are identified and discussed. The scope of topics addressed is broad, including transport phenomena in food processing; food process engineering; physical properties of foods; food nano-science and nano-engineering; food equipment design; food plant design; modeling food processes; microbial inactivation kinetics; preservation technologies; engineering aspects of food packaging; shelf-life, storage and distribution of foods; instrumentation, control and automation in food processing; food engineering, health and nutrition; energy and economic considerations in food engineering; sustainability; and food engineering education.