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Metadados | Descrição | Idioma |
---|---|---|
Autor(es): dc.contributor | Faculty of Exact and Natural Science and Surveying National University of the Northeast – UNNE | - |
Autor(es): dc.contributor | Universidade Estadual Paulista (Unesp) | - |
Autor(es): dc.contributor | UNNE | - |
Autor(es): dc.creator | Pérez-Rodríguez, Michael [UNESP] | - |
Autor(es): dc.creator | Dirchwolf, Pamela Maia | - |
Autor(es): dc.creator | Silva, Tiago Varão [UNESP] | - |
Autor(es): dc.creator | Vieira, Alan Lima [UNESP] | - |
Autor(es): dc.creator | Neto, José Anchieta Gomes [UNESP] | - |
Autor(es): dc.creator | Pellerano, Roberto Gerardo | - |
Autor(es): dc.creator | Ferreira, Edilene Cristina [UNESP] | - |
Data de aceite: dc.date.accessioned | 2022-02-22T00:34:47Z | - |
Data de disponibilização: dc.date.available | 2022-02-22T00:34:47Z | - |
Data de envio: dc.date.issued | 2020-12-11 | - |
Data de envio: dc.date.issued | 2020-12-11 | - |
Data de envio: dc.date.issued | 2020-11-29 | - |
Fonte completa do material: dc.identifier | http://dx.doi.org/10.1016/j.foodchem.2020.127051 | - |
Fonte completa do material: dc.identifier | http://hdl.handle.net/11449/201882 | - |
Fonte: dc.identifier.uri | http://educapes.capes.gov.br/handle/11449/201882 | - |
Descrição: dc.description | A 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.description | Institute 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.description | Chemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55 | - |
Descrição: dc.description | Faculty of Agricultural Sciences UNNE, Sgto. Cabral, 1213 | - |
Descrição: dc.description | Chemistry Institute of Araraquara São Paulo State University – UNESP, R. Prof. Francisco Degni 55 | - |
Idioma: dc.language | en | - |
Relação: dc.relation | Food Chemistry | - |
???dc.source???: dc.source | Scopus | - |
Palavras-chave: dc.subject | Botanical origin | - |
Palavras-chave: dc.subject | Rice | - |
Palavras-chave: dc.subject | SD-LIBS | - |
Palavras-chave: dc.subject | Support vector machine | - |
Título: dc.title | Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination | - |
Tipo de arquivo: dc.type | livro digital | - |
Aparece nas coleções: | Repositório Institucional - Unesp |
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