用机器学习的UV-Vis-NIR光谱检测和量化牛奶和水牛奶中的甲杂物
Delinka Genoveva Rosa1, Vishant V Malik1, Lalchand B Patle2
1School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa 403206, India.
Food chemistry
|July 18, 2025
概括
这项研究开发了一种使用光谱学和机器学习的快速,非破坏性方法,用于检测和量化牛奶和水牛奶中的甲伪造,以确保牛奶的安全.
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:
- 甲是用于保存牛奶的有害杂物,对公众健康构成重大风险.
- 准确和快速检测牛奶中的甲素的方法对于食品安全法规至关重要.
研究的目的:
- 开发和验证一种非破坏性的方法来识别和量化牛奶和水牛奶中的甲.
- 为了评估UV-Vis-NIR光谱学与化学测量结合用于牛奶改分析的疗效.
主要方法:
- 使用UV-Vis-NIR光谱 (200-1700nm) 来获取光谱数据.
- 应用光谱预处理,主要组件分析 (PCA) 和机器学习 (ML) 算法.
- 采用回归和分类模型与交叉验证用于定量和定性分析.
主要成果:
- 实现了高回归模型性能 (R2 0.998-0.999,RPD>80) 用于甲素量化.
- 证明了优异的分类准确性 (高达100%),具有5倍的交叉验证 (平均82.2-92.9%).
- 使用交叉验证 (R2 = 0.999) 验证了该方法.
结论:
- 与PCA和ML相结合的UV-Vis-NIR光谱学提供了一种可靠和敏感的方法来检测牛奶中的甲.
- 开发的方法为非破坏性牛奶质量评估提供了实用,实时的解决方案.
- 这种技术可以帮助监管机构和乳制品行业确保乳制品的安全性和真实性.
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