Machine learning-based prediction of microbial growth and acidification in yogurt fermentation at industrial temperatures
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Issued Date
2025-09-01
Resource Type
ISSN
00236438
Scopus ID
2-s2.0-105013155910
Journal Title
Lwt
Volume
231
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lwt Vol.231 (2025)
Suggested Citation
Saetae D. Machine learning-based prediction of microbial growth and acidification in yogurt fermentation at industrial temperatures. Lwt Vol.231 (2025). doi:10.1016/j.lwt.2025.118326 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111733
Title
Machine learning-based prediction of microbial growth and acidification in yogurt fermentation at industrial temperatures
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Abstract
Accurate prediction of microbial growth and acidification is essential for optimizing yogurt fermentation processes. This study presents a machine learning-based framework that integrates Long Short-Term Memory (LSTM) neural networks with classical primary growth models for microbial prediction, and Support Vector Regression (SVR) for modeling pH acidification dynamics. The framework was applied to Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus fermentations in reconstituted skim milk at 37 °C, 41 °C, and 45 °C. The hybrid LSTM model showed superior accuracy at 37 °C and 41 °C in capturing strain-specific, temperature-dependent growth patterns, while the Gompertz model remained most effective at 45 °C. For acidification prediction, SVR outperformed Gompertz, Weibull, and ensemble models under all conditions. Validation using ten biological replicates revealed RMSE reductions of up to 86 % compared to classical models. These results highlight the potential of hybrid machine learning frameworks to improve real-time fermentation monitoring, reduce batch variability, and support intelligent control of industrial yogurt production. Future work will explore integration with sensor data and expansion to mixed cultures and functional dairy formulations.