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<p>Fermentation is a key process in food manufacturing that determines product quality, safety, and nutrition. Traditional offline and manual monitoring methods are inefficient and lack real-time adaptability. Advances in intelligent food processing have made real-time monitoring and control essential for precise regulation, improved consistency, and higher production efficiency. This review summarizes recent developments in intelligent fermentation monitoring, focusing on spectroscopic and sensing technologies. Artificial intelligence (AI), data-driven modeling, and the Internet of Things (IoT) are highlighted as enabling tools for data integration and intelligent decision-making. Applications in beverages, tea, coffee, and dairy systems are also discussed. These technologies improve monitoring accuracy and reduce response time, enhancing process consistency and efficiency. However, challenges remain, including limited model generalization, sensor instability, and fragmented data systems. Future work should focus on robust miniaturized sensors, interoperable platforms, and integrated IoT–cloud frameworks for scalable real-time fermentation management.</p>

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Intelligent monitoring technologies for food fermentation processes: transformation from traditional experience to multimodal approaches and challenges

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Junchao Zhou,
  • Lei Gan,
  • Lin Du,
  • Xuan Liu,
  • Pan Song,
  • Xiaoxue Tang,
  • Ke Wang,
  • Bo Yang,
  • Tao Chen,
  • Yuancheng He,
  • Xianguo Tou,
  • Haiping Du

摘要

Fermentation is a key process in food manufacturing that determines product quality, safety, and nutrition. Traditional offline and manual monitoring methods are inefficient and lack real-time adaptability. Advances in intelligent food processing have made real-time monitoring and control essential for precise regulation, improved consistency, and higher production efficiency. This review summarizes recent developments in intelligent fermentation monitoring, focusing on spectroscopic and sensing technologies. Artificial intelligence (AI), data-driven modeling, and the Internet of Things (IoT) are highlighted as enabling tools for data integration and intelligent decision-making. Applications in beverages, tea, coffee, and dairy systems are also discussed. These technologies improve monitoring accuracy and reduce response time, enhancing process consistency and efficiency. However, challenges remain, including limited model generalization, sensor instability, and fragmented data systems. Future work should focus on robust miniaturized sensors, interoperable platforms, and integrated IoT–cloud frameworks for scalable real-time fermentation management.