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

Area of Science:

• Spectroscopy
• Materials Science
• Machine Learning

Background:

• Infrared (IR) spectra offer rich chemical and bonding information but are challenging to interpret in complex systems due to overlapping absorptions.
• Heterogeneous materials like cross-linked polyethylene (PEX-a) pipes present significant challenges for traditional spectroscopic analysis.
• Understanding material degradation and composition in PEX-a pipes is crucial for infrastructure integrity.

Purpose of the Study:

• To develop and implement a deep learning approach for analyzing complex IR spectra from PEX-a pipes.
• To compare the effectiveness of a β-variational autoencoder (β-VAE) against principal component analysis (PCA) for spectral data interpretation.
• To demonstrate the capability of β-VAE in identifying and representing independent generative factors within spectroscopic data.

Main Methods:

• A β-variational autoencoder (β-VAE) was trained on a comprehensive database of PEX-a pipe IR spectra.
• The β-VAE model was used to learn interpretable and independent representations of spectral variations.
• Principal component analysis (PCA) was employed as a benchmark for comparison.
• The trained β-VAE encoder was applied to hyperspectral data of a pipe crack to map spatial distributions of learned features.

Main Results:

• The β-VAE model successfully learned distinct and interpretable representations of spectral variations in PEX-a.
• The β-VAE demonstrated superior performance compared to PCA in analyzing the complex spectroscopic data.
• The spatial distribution of generative factors within a pipe crack was successfully mapped using the β-VAE encoder.
• The study identified key spectral features related to material composition and degradation in PEX-a pipes.

Conclusions:

• Deep learning architectures, specifically β-VAE, offer a powerful tool for enhancing the analysis of complex spectroscopic data.
• β-VAE provides a more interpretable and effective method for deconstructing spectral variations in heterogeneous materials like PEX-a.
• This approach has significant potential for non-destructive evaluation and material characterization in various industrial applications.