食品发酵:NIR光谱作为过程分析技术的工具
Cristina Alamprese1, Silvia Grassi1
1Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Milan, Italy.
Advances in food and nutrition research
|August 24, 2025
概括
近红外 (NIR) 光谱与人工智能 (AI) 结合,可以实时监测食品发酵. 这项技术提高了各种发酵产品的食品安全,可持续性和质量控制.
科学领域:
- 食品科学与技术
- 生物技术
- 分析化学
背景情况:
- 发酵是食品系统的重要过程,改善营养,安全,可持续性和感官质量.
- 它利用微生物的新陈代谢来保存,增强消化能力,并在各种食物类型中产生生物活性化合物.
- 传统的监控方法是手动的,缺乏实时数据,阻碍了流程优化.
研究的目的:
- 探索先进的传感器技术的应用,特别是近红外 (NIR) 光谱,以实时监测和控制食品发酵.
- 评估NIR光谱与人工智能 (AI) 和智能发酵系统的过程分析技术 (PAT) 的整合.
- 解决传统发酵监测技术的局限性.
主要方法:
- 使用近红外光谱 (NIR) 进行快速的,非侵入性的发酵过程分析.
- 使用多变量数据分析和机器学习模型来解释光谱数据并预测发酵参数.
- 这些方法适用于各种食品,包括饮料,乳制品,植物性和肉制品.
主要成果:
- 与机器学习相结合的NIR光谱在预测关键发酵参数方面表现强.
- 这项技术在酒精饮料,乳制品,植物性食品,肉制品,面包,和等各种食品中被证明是有效的.
- 成功预测发酵参数表明增强过程控制的潜力.
结论:
- 与人工智能和PAT集成的NIR光谱为食品发酵提供了实时智能控制的强大工具.
- 通过可解释的人工智能和数字双胞胎等先进技术来解决光谱复杂性和模型可解释性等挑战.
- 这种技术进步为更加可持续,精确和高效的食品发酵工艺铺平了道路,
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