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机器学习集成的表面增强拉曼光谱分析多元成分染料混合物的分析

Yan Yu¹, Wenjing Lu², Xiaobin Yao³

¹School of Energy Materials and Chemical Engineering, Hefei University, Hefei 230601, China; Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|February 1, 2025

概括

这项研究引入了一种使用机器学习分析复杂的表面增强拉曼光谱 (SERS) 数据的新方法. 一个卷积神经网络 (CNN) 模型在识别染料混合物方面取得了高精度,克服了信号重叠的挑战.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
材料科学材料科学材料科学

背景情况:

表面增强拉曼光谱 (SERS) 为材料分析提供了高灵敏度和特异性.
来自混合物的重叠光谱信号对准确的分析物识别构成重大挑战.
开发用于分析复杂SERS数据的强大方法对于推进其应用至关重要.

研究的目的:

为了比较各种机器学习模型来分析复杂的SERS光谱.
使用SERS开发一种有效的方法来识别和分类染料混合物的成分.
优化SERS基板和分析工作流程,以提高检测能力.

主要方法:

优化银涂黄金核心外纳米立方体 (Au@AgNCs) 作为SERS基材.
使用独立主要成分分析 (ICA) 来从混合物光谱中分离单个染料信号.
使用K近邻 (KNN),支持矢量机 (SVM),随机森林 (RF) 和卷积神经网络 (CNN) 的孤立信号的分类.

主要成果:

与其他机器学习算法相比,卷积神经网络 (CNN) 模型表现出卓越的性能.
在分类单个染料方面,CNN的准确率达到了98%,在分类染料混合物方面,准确率达到了97%.
ICA有效地隔离了重叠的信号,通过CNN模型实现了准确的分类.

结论:

独立主要组件分析 (ICA) 与卷积神经网络 (CNN) 结合,为SERS提供了一种强大的分析方法.
这种综合方法显著提高了分析复杂染料混合物的准确性.
拟议的工作流程增强了SERS光谱作为定量和定性分析工具的实用性.

关键词:

染料混合物染料混合物机器学习机器学习卷积神经网络 (CNN) 是一种神经网络.独立的主要组件分析 (ICA)表面增强的拉曼光谱 (SERS)

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