A Raman spectroscopy algorithm based on convolutional neural networks and multilayer perceptrons: qualitative and quantitative analyses of chemical warfare agent simulants.

Rapid and reliable detection of chemical warfare agents (CWAs) is essential for military defense and counter-terrorism operations. Although Raman spectroscopy provides a non-destructive method for on-site detection, existing methods show difficulty in coping with complex spectral overlap and concentration changes when analyzing mixtures containing trace components and highly complex mixtures. Based on the idea of convolutional neural networks and multi-layer perceptrons, this study proposes a qualitative and quantitative analysis algorithm of Raman spectroscopy based on deep learning (RS-MLP). The reference feature library is built from pure substance spectral features, while multi-head attention adaptively captures mixture weights. The MLP-Mixer then performs hierarchical feature matching for qualitative identification and quantitative analysis. The recognition rate of spectral data for the four types of combinations used for validation reached 100%, with an average root mean square error (RMSE) of less than 0.473% for the concentration prediction of three components. Furthermore, the model exhibited robust performance even under conditions of highly overlapping spectra. At the same time, the interpretability of the model is also enhanced. The model has excellent accuracy and robustness in component identification and concentration identification in complex mixtures and provides a practical solution for rapid and non-contact detection of persistent chemicals in complex environments.