  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.
