通过深度学习分析复杂的拉曼光谱来量化日和精油混合物的比例量化
Jia-Wei Tang1, Yong-Xuan Hong2, Jie Chen3
1The Marshall Centre for Infectious Diseases Research and Training, Division of Microbiology and Immunology, School of Biomedical Sciences, The University of Western Australia, Perth, WA 6009, Australia.
ACS measurement science au
|December 22, 2025
概括
拉曼光谱可以检测到精油 (EO) 伪造. 一个新的CARFENet模型使用光谱分析准确量化混合精油 (EO) 中的纯成分.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 精油 (EOs) 在经济上有价值,但容易被改.
- 拉曼光谱为EO分析提供了分子特异性.
- 由于潜在的改,EO的质量控制具有挑战性.
研究的目的:
- 开发一种可靠的方法来分析精油 (REO) 和精油 (GEO) 混合物.
- 确定用于区分EO混合比的关键光谱标记.
- 创建一个深度学习模型,将混合EO光谱分解为纯成分.
主要方法:
- 从REO,GEO及其混合物中收集了2700个拉曼光谱.
- 用于比率分析的卷积神经网络 (CNN) 的构建.
- 开发一个频道注意力残余特征提取网络 (CARFENet) 用于光谱解构.
- 使用内部数据集和外部测试集进行验证.
主要成果:
- 确定了关键的光谱峰值 (例如,800,1000,1668 cm-1) 作为歧视性标记.
- CNN模型有效地区分了不同的EO混合比率.
- 在预测纯成分光谱方面,CARFENet取得了很高的准确性,相似度指数> 0.99.
结论:
- 特定的光谱特征可以作为EO成分的可靠标记.
- CARFENet提供了混合物中纯EO成分的有效定量分析.
- 这种方法可以通过拉曼光谱来加强精油的质量控制.
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