Fermentation is a cornerstone of the food system, offering several benefits in nutrition, food safety, sustainability, and sensory quality. Historically rooted in food preservation and cultural practices, fermentation has evolved into a dynamic biotechnological tool, spanning diverse applications from dairy, meat, vegetable, and plant-based foods to by-product valorization. The process leverages microbial metabolisms (primarily lactic acid bacteria, yeasts, and molds) to enhance shelf life and sensory properties, improve digestibility, and generate bioactive compounds. Advances in precision fermentation and strain engineering have further extended fermentation potential to address emerging food challenges. Traditional fermentation monitoring relies on manual, off-line techniques that lack real-time responsiveness. The integration of advanced sensor technologies, artificial intelligence (AI), and process analytical technology (PAT) enables real-time, non-invasive process control. Among these tools, near-infrared (NIR) spectroscopy stands out due to its speed, low cost, minimal sample preparation, and compatibility with multivariate data analysis. NIR spectroscopy, combined with machine learning models, has demonstrated robust performance in predicting fermentation parameters across multiple food matrices, including alcoholic beverages, dairy, plant and meat fermented products, bread, kombucha, and vinegar. However, challenges such as spectral complexity, calibration transferability, and model interpretability persist. Future perspectives emphasize the convergence of NIR spectroscopy with digital twins, hybrid modeling, and explainable AI, enabling self-optimizing, adaptive fermentation systems. Emerging NIR devices offer portability and scalability, democratizing access to smart fermentation control for both industrial and artisanal producers. This paradigm shift lays the groundwork for intelligent, sustainable, and precision-driven food fermentation.

Food fermentations: NIR spectroscopy as a tool for process analytical technology / C. Alamprese, S. Grassi (ADVANCES IN FOOD AND NUTRITION RESEARCH). - In: Spectroscopy and Machine Learning Tools for Food Quality and Safety / [a cura di] D. Cozzolino. - Prima edizione. - [s.l] : Academic Press : Elsevier, 2025. - ISBN 9780443345937. - pp. 391-430 [10.1016/bs.afnr.2025.06.002]

Food fermentations: NIR spectroscopy as a tool for process analytical technology

C. Alamprese
;
S. Grassi
2025

Abstract

Fermentation is a cornerstone of the food system, offering several benefits in nutrition, food safety, sustainability, and sensory quality. Historically rooted in food preservation and cultural practices, fermentation has evolved into a dynamic biotechnological tool, spanning diverse applications from dairy, meat, vegetable, and plant-based foods to by-product valorization. The process leverages microbial metabolisms (primarily lactic acid bacteria, yeasts, and molds) to enhance shelf life and sensory properties, improve digestibility, and generate bioactive compounds. Advances in precision fermentation and strain engineering have further extended fermentation potential to address emerging food challenges. Traditional fermentation monitoring relies on manual, off-line techniques that lack real-time responsiveness. The integration of advanced sensor technologies, artificial intelligence (AI), and process analytical technology (PAT) enables real-time, non-invasive process control. Among these tools, near-infrared (NIR) spectroscopy stands out due to its speed, low cost, minimal sample preparation, and compatibility with multivariate data analysis. NIR spectroscopy, combined with machine learning models, has demonstrated robust performance in predicting fermentation parameters across multiple food matrices, including alcoholic beverages, dairy, plant and meat fermented products, bread, kombucha, and vinegar. However, challenges such as spectral complexity, calibration transferability, and model interpretability persist. Future perspectives emphasize the convergence of NIR spectroscopy with digital twins, hybrid modeling, and explainable AI, enabling self-optimizing, adaptive fermentation systems. Emerging NIR devices offer portability and scalability, democratizing access to smart fermentation control for both industrial and artisanal producers. This paradigm shift lays the groundwork for intelligent, sustainable, and precision-driven food fermentation.
Alcoholic beverages; Artificial intelligence; Dairy fermented products; Dough; Dry fermented sausages; Fermented plant materials; Machine learning; Process analytical technology; Vibrational spectroscopy; Vinegar;
Settore AGRI-07/A - Scienze e tecnologie alimentari
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1203685
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