Feature visualization of Raman spectrum analysis with deep convolutional neural network

TL;DR

This paper presents a deep convolutional neural network-based method for Raman spectrum analysis that visualizes important spectral features, aiding in understanding and validating spectral recognition models.

Contribution

The study introduces a novel feature visualization technique for CNN-based Raman spectrum analysis, highlighting spectral regions and baseline correction effects.

Findings

Raman peaks are effectively visualized as recognition features.

Baseline correction is inferred from near-zero weights in background regions.

The method confirms common component extraction in mixed amino acid spectra.

Abstract

We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis. The visualization is achieved by calculating important regions in the spectra from weights in pooling and fully-connected layers. The method is first examined for simple Lorentzian spectra, then applied to the spectra of pharmaceutical compounds and numerically mixed amino acids. We investigate the effects of the size and number of convolution filters on the extracted regions for Raman-peak signals using the Lorentzian spectra. It is confirmed that the Raman peak contributes to the recognition by visualizing the extracted features. A near-zero weight value is obtained at the background level region, which appears to be used for baseline correction. Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra. High weight values at the common peaks and negative values at the distinctive peaks appear, even though the model is given one-hot vectors as the training labels (without a mix ratio). This proposed method is potentially suitable for applications such as the validation of trained models, ensuring the reliability of common component extraction from compound samples for spectral analysis.