用NIR光谱学和高光谱成像技术对小麦面粉具有不同水平的亚二碳胺化学物质进行比较定量和差别分析
Hongju He1, Yuling Wang2, Shengqi Jiang3
1School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.
Foods (Basel, Switzerland)
|November 27, 2024
概括
近红外 (NIR) 和高光谱成像 (HSI) 有效量化小麦粉 (WF) 中的亚二碳胺 (ADA). 当可视化不需要时,NIR 足以用于 ADA 量化.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 亚二碳胺 (ADA) 是一个有争议的面粉处理剂.
- 需要快速和非破坏性的方法来检测小麦面粉 (WF) 中的ADA.
- 近红外 (NIR) 和高光谱成像 (HSI) 为光谱分析提供了潜力.
研究的目的:
- 为了比较NIR和HSI,以量化WF中的ADA含量 (0-100 mg/kg).
- 开发用于识别过度ADA (>45 mg/kg) 的WF的模型.
- 评估光谱预处理和波长选择以优化模型.
主要方法:
- 获取NIR和HSI光谱 (950-1660nm). 获得NIR和HSI光谱 (950-1660nm). 获得NIR和HSI光谱 (950-1660nm). 获得NIR和HSI光谱 (950-1660nm). 获得NIR和HSI光谱 (950-1660nm).
- 应用14种光谱预处理技术.
- 部分最小平方 (PLS) 回归用于定量化,PLS-区分分析 (PLS-DA) 用于分类.
- 使用五个数据集进行验证,采用不同的培训/验证样本大小.
主要成果:
- 无论是NIR (萨维茨基-戈莱第1导数) 还是HSI (原始光谱) PLS模型都准确量化了ADA.
- 优化的模型实现了高的确定系数 (R2V>0.98) 和低误差 (RMSEV <3.87 mg/kg).
- PLS-DA模型在识别过度ADA的WF时显示出100%的准确性.
结论:
- 在WF中,NIR和HSI对于快速,非破坏性的ADA量化和识别是有效的.
- 当可视化不是一个要求时,NIR光谱是一种可行的HSI替代方案.
- 优化的光谱预处理和波长选择提高了模型性能.
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