| |
|
|
| Registros recuperados : 4 | |
| 4. |  | KILLNER, M. H. M. CARVALHO, A. S. ROHWEDDER, J. J. R.; COLNAGO, L. A. Desenvolvimento de métodos simples e rápidos para o controle de qualidade de blendas de combustíveis fósseis e biocombustíveis empregando RMN de baixo campo. In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 2014, São Carlos, SP Anais do SIAGRO: ciência, inovação e mercado 2014. São Carlos, SP: Embrapa Instrumentação, 2014. p. 225-228. Editores: Carlos Manoel Pedro Vaz, Débora Marcondes Bastos Pereira Milori, Silvio Crestana.| Biblioteca(s): Embrapa Instrumentação. |
|    |
| Registros recuperados : 4 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Instrumentação. |
|
Data corrente: |
16/03/2022 |
|
Data da última atualização: |
16/03/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
AGUIAR, L. M. de; GALVAN, D.; BONA, E.; COLNAGO, L. A.; KILLNER, M. H. M. |
|
Afiliação: |
LUIZ ALBERTO COLNAGO, CNPDIA. |
|
Título: |
Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration. |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
Talanta, v. 236, 122838, 2022. |
|
Páginas: |
1 - 10 |
|
ISSN: |
0039-9140 |
|
DOI: |
https://doi.org/10.1016/j.talanta.2021.122838 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents. |
|
Palavras-Chave: |
Linear discriminant analysis; Low-field proton nuclear magnetic resonance; Multi-block analysis; Multiple linear regression. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02109naa a2200253 a 4500
001 2140969
005 2022-03-16
008 2022 bl uuuu u00u1 u #d
022 $a0039-9140
024 7 $ahttps://doi.org/10.1016/j.talanta.2021.122838$2DOI
100 1 $aAGUIAR, L. M. de
245 $aData fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration.$h[electronic resource]
260 $c2022
300 $a1 - 10
520 $aMedium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.
653 $aLinear discriminant analysis
653 $aLow-field proton nuclear magnetic resonance
653 $aMulti-block analysis
653 $aMultiple linear regression
700 1 $aGALVAN, D.
700 1 $aBONA, E.
700 1 $aCOLNAGO, L. A.
700 1 $aKILLNER, M. H. M.
773 $tTalanta$gv. 236, 122838, 2022.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Instrumentação (CNPDIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
