Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFaculty of Exact and Natural Science and Surveying National University of the Northeast – UNNE-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUNNE-
Autor(es): dc.creatorPérez-Rodríguez, Michael [UNESP]-
Autor(es): dc.creatorDirchwolf, Pamela Maia-
Autor(es): dc.creatorSilva, Tiago Varão [UNESP]-
Autor(es): dc.creatorVieira, Alan Lima [UNESP]-
Autor(es): dc.creatorNeto, José Anchieta Gomes [UNESP]-
Autor(es): dc.creatorPellerano, Roberto Gerardo-
Autor(es): dc.creatorFerreira, Edilene Cristina [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:34:47Z-
Data de disponibilização: dc.date.available2022-02-22T00:34:47Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-11-29-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.foodchem.2020.127051-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201882-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201882-
Descrição: dc.descriptionA simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92–100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.-
Descrição: dc.descriptionInstitute of Basic and Applied Chemistry of the Northeast of Argentina (IQUIBA-NEA) National Scientific and Technical Research Council (CONICET) Faculty of Exact and Natural Science and Surveying National University of the Northeast – UNNE, Av. Libertad 5470-
Descrição: dc.descriptionChemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55-
Descrição: dc.descriptionFaculty of Agricultural Sciences UNNE, Sgto. Cabral, 1213-
Descrição: dc.descriptionChemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55-
Idioma: dc.languageen-
Relação: dc.relationFood Chemistry-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBotanical origin-
Palavras-chave: dc.subjectRice-
Palavras-chave: dc.subjectSD-LIBS-
Palavras-chave: dc.subjectSupport vector machine-
Título: dc.titleFast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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