Deep Learning and Infrared Spectroscopy: Representation Learning with a β-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 impleme...
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| Published in: | The journal of physical chemistry letters Vol. 13; no. 25; p. 5787 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
30.06.2022
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| ISSN: | 1948-7185, 1948-7185 |
| Online Access: | Get more information |
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| Summary: | 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 β-variational autoencoder (β-VAE) on a database of PEX-a pipe spectra. We show that the β-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the β-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 β-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems.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 β-variational autoencoder (β-VAE) on a database of PEX-a pipe spectra. We show that the β-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the β-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 β-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1948-7185 1948-7185 |
| DOI: | 10.1021/acs.jpclett.2c01328 |