使用Vis-NIR-SWIR光谱学评估热处理对功能地板中的化合物的影响:一种机器学习方法
Achilleas Panagiotis Zalidis1, Nikolaos Tsakiridis2, George Zalidis2
1Laboratory of Consumer and Sensory Perception of Food & Drinks, Department of Food Science and Nutrition, University of the Aegean, Metropolite Ioakeim 2, 81400 Myrina, Greece.
Foods (Basel, Switzerland)
|August 14, 2025
概括
功能性面粉在加热下保留了化合物. 可见,近波和短波红外光谱与机器学习准确地分类面粉,帮助食品配方和质量控制.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 越来越多的消费者对替代成分的需求和小麦面粉的营养限制.
- 功能性面粉含有丰富的生物活性化合物,特别是化合物.
- 了解这些化合物的热稳定性对于食品应用至关重要.
研究的目的:
- 探索各种功能性粉末中的化合物的热稳定性.
- 开发一种非破坏性光谱方法来分类功能性粉末.
- 为了评估烤温度对含量的影响.
主要方法:
- 可见,近距离和短波红外 (Vis-NIR-SWIR) 光谱 (350-2500纳米).
- 机器学习算法,特别是随机森林模型,用于分类.
- 带有和没有可见区域的光谱数据分析,以评估颜色的影响.
主要成果:
- 高分类精度 (0.98-0.99) 面粉类型,烤温度和使用全谱范围的烯酸度.
- 在温和的温度下,豆类和小麦面粉保留了较高的总含量 (TPC).
- 葡萄粉 (GSF) 和橄石粉 (OSF) 显示出显著的TPC热稳定性,即使在高温下.
结论:
- 与ML集成的Vis-NIR-SWIR光谱学提供了一种快速,非破坏性的方法,用于功能性面粉分类和质量评估.
- 该方法是稳健的,由于排除可见光谱区域,对分类准确度的影响最小.
- 这些发现支持精确的食品配方和功能性面粉的质量控制.
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