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- Title
Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.
- Authors
Yamada, Shunji; Chikayama, Eisuke; Kikuchi, Jun; Corsaro, Carmelo; Mallamace, Domenico
- Abstract
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.
- Subjects
TOPOGRAPHIC maps; EUGLENA gracilis; NUCLEAR magnetic resonance; CRYSTALS; MACROMOLECULAR dynamics; MATRIX decomposition; POLYLACTIC acid; MAGIC angle spinning
- Publication
International Journal of Molecular Sciences, 2021, Vol 22, Issue 3, p1086
- ISSN
1661-6596
- Publication type
Article
- DOI
10.3390/ijms22031086