农业食品行业振动光谱仪器的发展趋势
Candela Melendreras1, Jesús Montero2, José M Costa-Fernández2
1Department of Physical and Analytical Chemistry, University of Oviedo, Avda. Julián Clavería 8, 33006, Oviedo, Spain. melendrerascandela@uniovi.es.
Analytical and bioanalytical chemistry
|October 18, 2025
概括
非破坏性光谱传感器 (NDSS) 提供快速的现场食品分析. 先进的人工智能和机器学习增强了这些振动光谱法,用于在整个食品供应链中实时进行质量控制.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 随着全球需求的增加和监管压力,需要加强食品安全和质量保证.
- 传统的分析方法往往缺乏现代食品供应链所需的速度,选择性或非破坏性能力.
- 非破坏性光谱传感器 (NDSS) 正在成为解决这些分析挑战的关键工具.
研究的目的:
- 审查非破坏性光谱传感器 (NDSS) 在食品安全和质量控制方面的潜力.
- 突出仪器仪表方面的进步,包括小型化和便携性.
- 讨论先进的数据分析技术,如化学测量和人工智能 (AI) 在光谱数据解释中的作用.
主要方法:
- 使用振动光谱,特别是近红外 (NIR) 和拉曼光谱,进行非破坏性样本分析.
- 开发小型化和具有成本效益的光谱仪器仪表,用于便携式实时应用.
- 应用先进的化学测量技术和机器学习算法用于光谱数据处理和预测建模.
主要成果:
- NDSS,特别是NIR和拉曼光谱,显示出对食品矩阵的快速,现场分析而没有样本降解的显著前景.
- 微型化的努力正在导致便携式设备在整个供应链中实时监控食品.
- 人工智能和机器学习正在彻底改变光谱数据解释,改善模型校准,可转移性和可靠性.
结论:
- 先进的振动光谱与人工智能相结合,为食品生产和分销提供智能,实时的决策系统.
- 这些技术将在现代农业食品系统中重新定义食品质量控制标准.
- 整合NDSS支持可持续,高效和安全的食品生产.
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