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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| Vydáno v: | The journal of physical chemistry letters Ročník 13; číslo 25; s. 5787 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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30.06.2022
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| ISSN: | 1948-7185, 1948-7185 |
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| Abstract | 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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| AbstractList | 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. |
| Author | MacFarlane, Hugh Quintal, Jonathan D'Amico, Joseph Grossutti, Michael Quirk, Amanda Dutcher, John R |
| Author_xml | – sequence: 1 givenname: Michael surname: Grossutti fullname: Grossutti, Michael – sequence: 2 givenname: Joseph surname: D'Amico fullname: D'Amico, Joseph – sequence: 3 givenname: Jonathan surname: Quintal fullname: Quintal, Jonathan – sequence: 4 givenname: Hugh surname: MacFarlane fullname: MacFarlane, Hugh – sequence: 5 givenname: Amanda surname: Quirk fullname: Quirk, Amanda – sequence: 6 givenname: John R surname: Dutcher fullname: Dutcher, John R |
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| CitedBy_id | crossref_primary_10_1038_s41467_024_49381_z crossref_primary_10_1063_5_0271206 crossref_primary_10_1016_j_chemolab_2023_105029 crossref_primary_10_1016_j_diamond_2025_112352 crossref_primary_10_1039_D3EW00043E crossref_primary_10_1016_j_trac_2024_117612 crossref_primary_10_3390_s22249764 crossref_primary_10_1021_acssensors_4c03260 crossref_primary_10_1016_j_fmre_2025_09_014 crossref_primary_10_1016_j_progpolymsci_2024_101828 crossref_primary_10_1016_j_matdes_2025_114788 crossref_primary_10_1038_s43588_023_00550_y crossref_primary_10_1016_j_artmed_2024_103053 |
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| Title | Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder |
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