Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials Article Swipe
Shunji Yamada
,
Eisuke Chikayama
,
Jun Kikuchi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijms22031086
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijms22031086
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.
Related Topics
Concepts
Deconvolution
Biological system
Fourier transform
Solid-state nuclear magnetic resonance
Materials science
Magic angle spinning
Two-dimensional nuclear magnetic resonance spectroscopy
Short-time Fourier transform
Nuclear magnetic resonance
Chemistry
Nuclear magnetic resonance spectroscopy
Analytical Chemistry (journal)
Fourier analysis
Computer science
Physics
Algorithm
Chromatography
Biology
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijms22031086
- https://www.mdpi.com/1422-0067/22/3/1086/pdf?version=1611330245
- OA Status
- gold
- Cited By
- 12
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3122978436
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3122978436Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijms22031086Digital Object Identifier
- Title
-
Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component MaterialsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-22Full publication date if available
- Authors
-
Shunji Yamada, Eisuke Chikayama, Jun KikuchiList of authors in order
- Landing page
-
https://doi.org/10.3390/ijms22031086Publisher landing page
- PDF URL
-
https://www.mdpi.com/1422-0067/22/3/1086/pdf?version=1611330245Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1422-0067/22/3/1086/pdf?version=1611330245Direct OA link when available
- Concepts
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Deconvolution, Biological system, Fourier transform, Solid-state nuclear magnetic resonance, Materials science, Magic angle spinning, Two-dimensional nuclear magnetic resonance spectroscopy, Short-time Fourier transform, Nuclear magnetic resonance, Chemistry, Nuclear magnetic resonance spectroscopy, Analytical Chemistry (journal), Fourier analysis, Computer science, Physics, Algorithm, Chromatography, Biology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1, 2022: 5, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(CP)–magic | 109 |
| abstract_inverted_index.applications | 89 |
| abstract_inverted_index.degradation, | 96, 106 |
| abstract_inverted_index.demonstrated | 87 |
| abstract_inverted_index.non-negative | 66 |
| abstract_inverted_index.spectroscopy | 5 |
| abstract_inverted_index.deconvolution | 43, 56, 181 |
| abstract_inverted_index.factorization | 68 |
| abstract_inverted_index.tensor/matrix | 67 |
| abstract_inverted_index.STFT-processed | 169 |
| abstract_inverted_index.macromolecular | 91, 199 |
| abstract_inverted_index.macromolecules | 189 |
| abstract_inverted_index.multi-component | 22 |
| abstract_inverted_index.characterization | 200 |
| abstract_inverted_index.cross-polarization | 108 |
| abstract_inverted_index.poly-ε-caprolactone | 147 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5014945517 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210100240, https://openalex.org/I60134161, https://openalex.org/I89630735 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.77477253 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |