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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: |
24/03/2022 |
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Data da última atualização: |
24/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
NICOLODELLI, G.; HERCULANO, R. D.; MARANGONI, B. S.; RIBEIRO, M. C. S.; MILORI, D. M. B. P.; MENEGATTI, C. R. |
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Afiliação: |
DEBORA MARCONDES BASTOS PEREIRA, CNPDIA. |
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Título: |
Differentiation of latex biomembrane with collagen and non-collagen using laser induced breakdown spectroscopy. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Materials Today Communications, v. 30, 103099, 2022. |
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Páginas: |
1 - 7 |
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ISSN: |
2352-4928 |
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DOI: |
https://doi.org/10.1016/j.mtcomm.2021.103099 |
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Idioma: |
Inglês |
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Conteúdo: |
Studies on the interaction of a biomaterial with other components are important to enhance its positive effects and resolve its limitations. Therefore, the search for fast and low-cost techniques is essential for the analysis, characterization and differentiation of these biomaterials. Laser-induced breakdown spectroscopy (LIBS) is a multielemental, fast, with reduced analytical cost and environmentally clean technique that does not require the use of reagents for sample preparation. In this work, an elemental characterization of collagen and non-collagen latex samples was performed by LIBS technique. Multivariate analyzes, such as principal component analysis (PCA) and machine learning (ML) algorithms were applied on LIBS data in order to differentiate the classes. The main elements detected in the LIBS spectra examined were due to C, Fe, Mg, Ca, Na, H, N and K. The best results were achieved using LIBS spectral data from the specific range: 656.15–656.55 nm combined with 744.08–744.48 nm. The elements H and N were identified as the main discriminating factors between the samples studied. The leave one out cross-validation tests indicates that collagen latex biomembrane can be differentiated from non-collagen samples with 94.44% accuracy using the Weighted K-Nearest Neighbor algorithm. |
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Palavras-Chave: |
Chemometric analysis; LIBS; Machine learning algorithms; Natural latex. |
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Categoria do assunto: |
-- |
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Marc: |
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001 2141291
005 2022-06-24
008 2022 bl uuuu u00u1 u #d
022 $a2352-4928
024 7 $ahttps://doi.org/10.1016/j.mtcomm.2021.103099$2DOI
100 1 $aNICOLODELLI, G.
245 $aDifferentiation of latex biomembrane with collagen and non-collagen using laser induced breakdown spectroscopy.$h[electronic resource]
260 $c2022
300 $a1 - 7
520 $aStudies on the interaction of a biomaterial with other components are important to enhance its positive effects and resolve its limitations. Therefore, the search for fast and low-cost techniques is essential for the analysis, characterization and differentiation of these biomaterials. Laser-induced breakdown spectroscopy (LIBS) is a multielemental, fast, with reduced analytical cost and environmentally clean technique that does not require the use of reagents for sample preparation. In this work, an elemental characterization of collagen and non-collagen latex samples was performed by LIBS technique. Multivariate analyzes, such as principal component analysis (PCA) and machine learning (ML) algorithms were applied on LIBS data in order to differentiate the classes. The main elements detected in the LIBS spectra examined were due to C, Fe, Mg, Ca, Na, H, N and K. The best results were achieved using LIBS spectral data from the specific range: 656.15–656.55 nm combined with 744.08–744.48 nm. The elements H and N were identified as the main discriminating factors between the samples studied. The leave one out cross-validation tests indicates that collagen latex biomembrane can be differentiated from non-collagen samples with 94.44% accuracy using the Weighted K-Nearest Neighbor algorithm.
653 $aChemometric analysis
653 $aLIBS
653 $aMachine learning algorithms
653 $aNatural latex
700 1 $aHERCULANO, R. D.
700 1 $aMARANGONI, B. S.
700 1 $aRIBEIRO, M. C. S.
700 1 $aMILORI, D. M. B. P.
700 1 $aMENEGATTI, C. R.
773 $tMaterials Today Communications$gv. 30, 103099, 2022.
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