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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
04/01/2023 |
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Data da última atualização: |
24/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
NOVOTNY, E. H.; GARCIA, R. H. S.; AZEVEDO, E. R. de. |
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Afiliação: |
ETELVINO HENRIQUE NOVOTNY, CNPS; RODRIGO H. S. GARCIA, UNIVERSIDADE DE SÃO PAULO; EDUARDO R. DE AZEVEDO, UNIVERSIDADE DE SÃO PAULO. |
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Título: |
Pulse sequence induced variability combined with multivariate analysis as a potential tool for 13C solid-state NMR signals separation, quantification, and classification. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Journal of Magnetic Resonance Open, v. 14/15, 100089, Jun. 2023. |
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DOI: |
https://doi.org/10.1016/j.jmro.2022.100089 |
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Idioma: |
Inglês |
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Conteúdo: |
Multivariate Curve Resolution (MCR) is a multivariate analysis procedure commonly used to analyze spectroscopic data providing the number of components coexisting in a chemical system, the pure spectra of the components as well as their concentration profiles. Usually, this procedure relies on the existence of distinct systematic variability among spectra of the different samples, which is provided by different sources of variation associated to differences in samples origin, composition, physical chemical treatment, etc. In solid-state NMR, MCR has been also used as a post-processing method for spectral denoising or editing based on a given NMR property. In this type of use, the variability is induced by the incrementation of a given parameter in the pulse-sequence, which encodes the separation property in the acquired spectra. In this article we further explore the idea of using a specific pulse sequence to induce a controlled variability in the 13C solid-state NMR spectra and then apply MCR to separate the pure spectra of the components according to the properties associated to the induced variability. We build upon a previous study of sugarcane bagasse where a series of 13C solid-state NMR spectra acquired with the Torchia-T1 CPMAS pulse sequence, with varying relaxation periods, was combined with different sample treatments, to estimate individual 13C solid-state NMR spectra of different molecular components (cellulose, xylan and lignin). Using the same pulse sequence, we show other application examples to demonstrate the potentiality, parameter optimization and/or establish the limitations of the procedure. As a first proof of principle, we apply the approach to commercial semicrystalline medium density polyethylene (MDPE) and polyether ether ketone (PEEK) providing the estimation of the individual 13C ssNMR spectra of the polymer chains in the amorphous (short ) and crystalline (long ) domains. The analysis also provided the relative intensities of each estimated pure spectra, which are related to the characteristic decays of the amorphous and crystalline domain fractions. We also apply the analysis to isotactic poly (1-butene) (iPB-I) as an example in which the induced variability occurs due to the mobility difference between the polymer backbone and side-chains. A jack-knifing procedure and a student t text allow us to stablish the minimum number of spectra and the range of relaxation periods that need to be used to achieve a precise estimation of the individual pure spectra and their relative intensities. A detail discussion about possible drawbacks, applications to more complex systems, and potential extensions to other type of induced variability are also presented. MenosMultivariate Curve Resolution (MCR) is a multivariate analysis procedure commonly used to analyze spectroscopic data providing the number of components coexisting in a chemical system, the pure spectra of the components as well as their concentration profiles. Usually, this procedure relies on the existence of distinct systematic variability among spectra of the different samples, which is provided by different sources of variation associated to differences in samples origin, composition, physical chemical treatment, etc. In solid-state NMR, MCR has been also used as a post-processing method for spectral denoising or editing based on a given NMR property. In this type of use, the variability is induced by the incrementation of a given parameter in the pulse-sequence, which encodes the separation property in the acquired spectra. In this article we further explore the idea of using a specific pulse sequence to induce a controlled variability in the 13C solid-state NMR spectra and then apply MCR to separate the pure spectra of the components according to the properties associated to the induced variability. We build upon a previous study of sugarcane bagasse where a series of 13C solid-state NMR spectra acquired with the Torchia-T1 CPMAS pulse sequence, with varying relaxation periods, was combined with different sample treatments, to estimate individual 13C solid-state NMR spectra of different molecular components (cellulose, xylan and lignin). Using the same pulse sequence, ... Mostrar Tudo |
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Palavras-Chave: |
13C Solid-state NMR; Análise multivariada de dados; Multivariate curve resolution; Multivariate data analysis; Polímeros semicristalinos; Semicrystalline polymers; Spectral editing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150609/1/Pulse-sequence-induced-variability-combined-with-multivariate-analysis-2023.pdf
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Marc: |
LEADER 03628naa a2200241 a 4500
001 2150609
005 2023-03-24
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jmro.2022.100089$2DOI
100 1 $aNOVOTNY, E. H.
245 $aPulse sequence induced variability combined with multivariate analysis as a potential tool for 13C solid-state NMR signals separation, quantification, and classification.$h[electronic resource]
260 $c2023
520 $aMultivariate Curve Resolution (MCR) is a multivariate analysis procedure commonly used to analyze spectroscopic data providing the number of components coexisting in a chemical system, the pure spectra of the components as well as their concentration profiles. Usually, this procedure relies on the existence of distinct systematic variability among spectra of the different samples, which is provided by different sources of variation associated to differences in samples origin, composition, physical chemical treatment, etc. In solid-state NMR, MCR has been also used as a post-processing method for spectral denoising or editing based on a given NMR property. In this type of use, the variability is induced by the incrementation of a given parameter in the pulse-sequence, which encodes the separation property in the acquired spectra. In this article we further explore the idea of using a specific pulse sequence to induce a controlled variability in the 13C solid-state NMR spectra and then apply MCR to separate the pure spectra of the components according to the properties associated to the induced variability. We build upon a previous study of sugarcane bagasse where a series of 13C solid-state NMR spectra acquired with the Torchia-T1 CPMAS pulse sequence, with varying relaxation periods, was combined with different sample treatments, to estimate individual 13C solid-state NMR spectra of different molecular components (cellulose, xylan and lignin). Using the same pulse sequence, we show other application examples to demonstrate the potentiality, parameter optimization and/or establish the limitations of the procedure. As a first proof of principle, we apply the approach to commercial semicrystalline medium density polyethylene (MDPE) and polyether ether ketone (PEEK) providing the estimation of the individual 13C ssNMR spectra of the polymer chains in the amorphous (short ) and crystalline (long ) domains. The analysis also provided the relative intensities of each estimated pure spectra, which are related to the characteristic decays of the amorphous and crystalline domain fractions. We also apply the analysis to isotactic poly (1-butene) (iPB-I) as an example in which the induced variability occurs due to the mobility difference between the polymer backbone and side-chains. A jack-knifing procedure and a student t text allow us to stablish the minimum number of spectra and the range of relaxation periods that need to be used to achieve a precise estimation of the individual pure spectra and their relative intensities. A detail discussion about possible drawbacks, applications to more complex systems, and potential extensions to other type of induced variability are also presented.
653 $a13C Solid-state NMR
653 $aAnálise multivariada de dados
653 $aMultivariate curve resolution
653 $aMultivariate data analysis
653 $aPolímeros semicristalinos
653 $aSemicrystalline polymers
653 $aSpectral editing
700 1 $aGARCIA, R. H. S.
700 1 $aAZEVEDO, E. R. de
773 $tJournal of Magnetic Resonance Open$gv. 14/15, 100089, Jun. 2023.
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Registro original: |
Embrapa Solos (CNPS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria Tropical. |
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Data corrente: |
04/03/2011 |
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Data da última atualização: |
23/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
SOUSA, A. B. O. de; BEZERRA, M. A.; FARIAS, F. C. |
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Afiliação: |
Alan Bernar Oliveira de Sousa, UFC; MARLOS ALVES BEZERRA, CNPAT; Fábio Costa Farias, UFC. |
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Título: |
Desenvolvimento inicial do clone BRS 275 de cajueiro sob irrigação com diferentes níveis salinos. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Revista Brasileira de Agricultura Irrigada, Fortaleza, v. 4, n. 3, p. 166-171, 2010. |
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Idioma: |
Português |
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Thesagro: |
Anacardium Occidentale; Irrigação; Salinidade. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/34295/1/PG10007.pdf
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Marc: |
LEADER 00574naa a2200169 a 4500
001 1880057
005 2018-05-23
008 2010 bl uuuu u00u1 u #d
100 1 $aSOUSA, A. B. O. de
245 $aDesenvolvimento inicial do clone BRS 275 de cajueiro sob irrigação com diferentes níveis salinos.$h[electronic resource]
260 $c2010
650 $aAnacardium Occidentale
650 $aIrrigação
650 $aSalinidade
700 1 $aBEZERRA, M. A.
700 1 $aFARIAS, F. C.
773 $tRevista Brasileira de Agricultura Irrigada, Fortaleza$gv. 4, n. 3, p. 166-171, 2010.
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Embrapa Agroindústria Tropical (CNPAT) |
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