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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
16/03/2022 |
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Data da última atualização: |
16/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
AGUIAR, L. M. de; GALVAN, D.; BONA, E.; COLNAGO, L. A.; KILLNER, M. H. M. |
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Afiliação: |
LUIZ ALBERTO COLNAGO, CNPDIA. |
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Título: |
Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Talanta, v. 236, 122838, 2022. |
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Páginas: |
1 - 10 |
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ISSN: |
0039-9140 |
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DOI: |
https://doi.org/10.1016/j.talanta.2021.122838 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Linear discriminant analysis; Low-field proton nuclear magnetic resonance; Multi-block analysis; Multiple linear regression. |
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Categoria do assunto: |
-- |
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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.
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| 3. |  | BONA, E. A. M. de; STEINMETZ, R. L. R.; SOMER, J. G.; LINS, L. P.; VIANCELLI, A.; KUNZ, A. Cama de frango como substrato para a produção de biogás após diferentes períodos de estocagem. In: SIMPÓSIO INTERNACIONAL SOBRE GERENCIAMENTO DE RESÍDUOS AGROPECUÁRIOS E AGROINDUSTRIAIS, 5., 2017, Foz do Iguaçu, Anais... Concórdia: Sbera: Embrapa Suínos e Aves, 2017. SIGERA. p. 451-455.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Suínos e Aves. |
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| 4. | | BONA, E. de A. M. de; STEINMETZ, R. L. R.; MILANI, L. de M.; SOMER, J. G.; MENEGOL, T.; TRINDADE, E. M.; KUNZ, A. Produção e aclimatização de inóculo para ensaio PME. In: SIMPÓSIO INTERNACIONAL SOBRE GERENCIAMENTO DE RESÍDUOS AGROPECUÁRIOS E AGROINDUSTRIAIS, 4., 2015, Rio de Janeiro, RJ. Anais... Brasília: Embrapa, 2015.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Suínos e Aves. |
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| 5. | | MACHADO, G. O.; TEIXEIRA, G. G.; GARCIA, R. H. S.; MORAES, T. B.; BONA, E.; SANTOS, P. M.; COLNAGO, L. A. Non-invasive method to predict the composition of requeijão cremoso directly in commercial packages using time domain NMR relaxometry and chemometrics. Molecules, v. 27, a4434, 2022. 10 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Instrumentação. |
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| Registros recuperados : 5 | |
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