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

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 Eug

SUBMITTER: Yamada S 

PROVIDER: S-EPMC7865946 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature