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Article

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

by 1,2, 2,3 and 1,2,4,*
1
Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
2
Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
3
Department of Information Systems, Niigata University of International and Information Studies, 3-1-1 Mizukino, Nishi-ku, Niigata 950-2292, Japan
4
Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Carmelo Corsaro and Domenico Mallamace
Int. J. Mol. Sci. 2021, 22(3), 1086; https://doi.org/10.3390/ijms22031086
Received: 25 December 2020 / Revised: 15 January 2021 / Accepted: 17 January 2021 / Published: 22 January 2021
Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)–magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design. View Full-Text
Keywords: solid-state NMR; short-time Fourier transform; signal deconvolution; prediction; anisotropy; T2 relaxation; macromolecules; cellulose degradation; plastics; Euglena gracilis solid-state NMR; short-time Fourier transform; signal deconvolution; prediction; anisotropy; T2 relaxation; macromolecules; cellulose degradation; plastics; Euglena gracilis
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MDPI and ACS Style

Yamada, S.; Chikayama, E.; Kikuchi, J. Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials. Int. J. Mol. Sci. 2021, 22, 1086. https://doi.org/10.3390/ijms22031086

AMA Style

Yamada S, Chikayama E, Kikuchi J. Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials. International Journal of Molecular Sciences. 2021; 22(3):1086. https://doi.org/10.3390/ijms22031086

Chicago/Turabian Style

Yamada, Shunji, Eisuke Chikayama, and Jun Kikuchi. 2021. "Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials" International Journal of Molecular Sciences 22, no. 3: 1086. https://doi.org/10.3390/ijms22031086

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