Deep Learning and Infrared Spectroscopy: Representation Learning with a ß-Variational Autoencoder.

Infrared (IR) spectra contain detailed and extensive information about the chemical composition and bonding environment in a sample. However, this information is difficult to extract from complex heterogeneous systems because of overlapping absorptions due to different generative factors. We implement a deep learning approach to study the complex spectroscopic changes that occur in cross-linked polyethylene (PEX-a) pipe by training a ß-variational autoencoder (ß-VAE) on a database of PEX-a pipe spectra. We show that the ß-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the ß-VAE encoder to a hyperspectrum of a crack in the wall of a pipe to evaluate the spatial distribution of these learned representations. This study shows how deep learning architectures like ß-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems.