基于深度学习的对物料中的胺和酸的定量评估,使用富里埃变换红外光谱学
Rahul Joshi1, Lakshmi Priya Gg2, Mohammad Akbar Faqeerzada1
1Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
一项新的研究表明,富里埃变换红外 (FT-IR) 光谱学与一维卷积神经网络 (1D CNN) 结合,可以快速检测物料中的胺和酸. 这种非破坏性方法为识别有毒添加剂提供了更高的准确性.
科学领域:
- 分析化学 分析化学
- 食品安全 食品安全
- 频谱学是一种光谱学.
背景情况:
- 胺和酸是富含的化合物,有时非法添加到物料中.
- 这些添加剂可能会导致物严重的健康问题.
- 需要有效的,非破坏性的检测方法来检测这些污染物.
研究的目的:
- 开发和评估物料中胺和酸的非破坏性定量测量技术.
- 将深度学习模型的性能与传统的化学测量方法进行比较.
主要方法:
- 福里埃变换红外光谱法 (FT-IR) 用于分析具有不同度的胺和酸的物料样本.
- 开发了一种一维卷积神经网络 (1D CNN) 模型,并与部分最小平方回归 (PLSR),主要组件回归 (PCR) 和混合线性分析 (HLA/GO) 进行比较.
主要成果:
- 1D CNN模型实现了高精度,相关系数为0.995的胺和0.994的酸.
- 与PLSR和PCR方法相比,1D CNN模型表现出优异的预测性能.
- 预测值的根平均平方误差为0.090%的胺和0.110%的酸.
结论:
- 与1D CNN模型相结合的FT-IR光谱学提供了一种快速而非破坏性的方法,用于检测物料中的胺和酸.
- 这种方法显示出通过识别有毒化学污染物来确保物食品安全的巨大潜力.
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