基于卷积神经网络和多层感知子的拉曼光谱算法:对化学战剂模拟剂的定性和定量分析
Jie Wu1, Fei Li2, Jing-Wen Zhou3
1College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China. plianghust@gmail.com.
The Analyst
|April 7, 2025
概括
本研究介绍了拉曼光谱 (RS-MLP) 的深度学习算法,用于检测化学战剂 (CWA). 该模型在识别混合物时达到100%的准确性,并可稳定地预测度,为复杂的环境提供实用解决方案.
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:
- 准确检测化学战剂 (CWA) 对安全至关重要.
- 拉曼光谱技术提供非破坏性的现场分析,但在混合物中的光谱重叠和度变化方面存在困难.
- 现有的方法对于含有微量成分的复杂样品缺乏稳定性.
研究的目的:
- 开发一种深度学习算法,用于对拉曼光谱的定性和定量分析.
- 解决复杂混合物中光谱重叠和度变化的挑战.
- 用拉曼光谱来提高CWA检测的准确性和稳定性.
主要方法:
- 一个深度学习算法,拉曼光谱多层感知器 (RS-MLP),是使用卷积神经网络和多层感知器开发的.
- 从纯物质光谱创建了一个参考功能库.
- 多头注意力和MLP-Mixer用于适应性混合物重量捕获和层次特征匹配.
主要成果:
- RS-MLP模型在四种验证组合中实现了光谱数据100%的识别率.
- 三组分度预测的平均根平均平方误差 (RMSE) 低于0.473%.
- 该模型表现出强大的性能,具有高度重叠的光谱和更好的可解释性.
结论:
- RS-MLP算法在复杂的混合物中提供了准确而可靠的成分和度识别.
- 这种深度学习方法为在具有挑战性的环境中快速,非接触式检测持久化学品提供了实用的解决方案.
- 该研究促进了拉曼光谱技术在安全和国防方面的应用.
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