使用自编码器和与便携式拉曼光谱相结合的基于残留的模型,对米粉中朗格莱特改的快速定量分析
Shiwen Li1, Tian Li1, Yaoyi Cai1
1College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 19, 2023
概括
一种新方法使用便携式拉曼光谱和机器学习模型 (SARNet) 准确检测米粉中的致癌性隆加. 这种快速,非破坏性的技术为食品安全分析提供了可靠的工具.
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:
- 米粉是常见的食品原料,但可以非法改Rongalite,一种致癌添加剂.
- 目前的检测方法,如HPLC和MS是复杂的,昂贵的,并有局限性.
- 拉曼光谱提供了一个非破坏性的替代方案,但面临着光和非线性偏差的挑战.
研究的目的:
- 开发一种快速,准确和非破坏性的方法来量化米粉中的隆加.
- 克服现有分析技术的局限性,以检测食品改.
- 通过先进的数据处理和机器学习,提高拉曼光谱对定量分析的性能.
主要方法:
- 使用Savitzky-Golay光滑过和VTPspline进行光谱数据预处理.
- 开发了SARNet,这是一个结合自编码器和残余网络进行定量分析的新型模型.
- 使用便携式拉曼光谱仪进行样本分析.
主要成果:
- 该SARNet模型实现了最先进的定量分析,R2为0.9703和RMSEP为0.0075.
- 通过使用便携式拉曼光谱仪,成功检测到低至0.49%的隆加利特度.
- 结合线性和非线性模型有效地解决了光谱数据中的非线性偏差.
结论:
- 拟议的方法整合了便携式拉曼光谱和机器学习 (SARNet),提供了一个高度准确和低偏差的分析工具.
- 这种方法可以快速而非破坏性地对米粉中的朗加利特改进行定量分析.
- 这项研究为提高食品安全和检测能力提供了有价值的解决方案.
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