基于NIR和SERS的食品农药残留物检测模型
Fuchao Yan1, Rui Zhang2, Shuqi Wang3
1Harbin Children Pharmaceutical Factory, Harbin, Heilongjiang, China.
PloS one
|April 8, 2025
概括
这项研究整合了近红外光谱 (NIR) 和表面增强拉曼光谱 (SERS) 以准确检测食品中的农药残留物. 组合的光谱聚变模型显著提高了检测准确性,并减少了矩阵干扰.
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:
- 准确检测农药残留物对于食品安全至关重要.
- 传统的方法可能耗时,对复杂的矩阵缺乏灵敏度.
- 结合多种光谱技术,有可能提高检测能力.
研究的目的:
- 开发一种多变量校准模型,以高效准确地检测食品中的农药残留物.
- 整合近红外光谱 (NIR) 和表面增强拉曼光谱 (SERS) 的光谱信息,以进行改进的分析.
- 与单一技术相比,评估光谱聚变模型的性能.
主要方法:
- 使用近红外光谱 (NIR) 和表面增强拉曼光谱 (SERS) 技术.
- 采用基于希尔伯特-施密特独立标准的可变空间代优化算法 (HSIC-VSIO) 进行特征选择.
- 使用部分最小平方回归 (PLSR) 开发了一种光谱融合定量模型.
主要成果:
- NIR和SERS特征层融合模型实现了0.988的预测设定确定系数 (R2) 和8.290.0的相对百分比偏差 (RPD).
- 聚变模型在检测复杂的食品基质中的农药残留物方面显著优于单个光谱技术.
- 该方法有效地抑制了矩阵干扰,并增强了模型的概括能力.
结论:
- 频谱特征层融合方法为快速准确检测食品中的农药残留物提供了可靠的工具.
- 该研究强调了SERS在低度农药检测方面的高灵敏度.
- 这种方法为应用光谱分析用于食品安全提供了新的见解.
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