COS-DeformDeep:可适应的2T2D光谱特征提取方法,以提高基于手持拉曼技术的混合物中组件识别性能
Xin Zhao1, Ziyan Zhao1, Qibing Zhu1
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China.
Analytica chimica acta
|February 28, 2025
概括
一种名为COS-DeformDeep的新方法改善了手持式拉曼光谱技术,用于分析混合物成分. 这种技术增强了弱光谱信号,提高了识别物质的准确性,即使在低度.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 拉曼光谱对于分析混合物成分至关重要.
- 手持式拉曼光谱仪提供便携性和快速的现场数据采集.
- 使用拉曼光谱法对复杂混合物中的成分进行量化仍然具有挑战性.
研究的目的:
- 引入COS-DeformDeep,这是一种用于增强手持拉曼混合物分析中的光谱特征的新方法.
- 改进复杂混合物中单个成分的识别和量化.
主要方法:
- 使用同步双轨二维相关谱 (2T2D-COS) 来增强重叠峰值中的弱信号.
- 采用可变形卷曲 (DCN) 来提高深度学习模型适应性,用于光谱特征提取.
- 验证了COS-DeformDeep模型对含有乙醇,二醇和胺的三种混合物数据集的验证.
主要成果:
- 该COS-DeformDeep模型在混合物成分识别方面表现出卓越的性能.
- 实现了平均准确率为94.97%,精度为98.45%,回忆率为92.44%,F1得分为95.06%.
- 有效识别的组件体积-重量比率低至2%.
结论:
- COS-DeformDeep有效地从弱光谱信号中提取特征,提高低度组件的识别精度.
- 该方法简化了光谱分析,适用于手持设备,增加了可访问性.
- 显著提高了手持拉曼光谱仪用于复杂混合物分析的能力.
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