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dc.creatorde Almeida, Valber Elias-
dc.creatorde Araújo Gomes, Adriano-
dc.creatorde Sousa Fernandes, David Douglas-
dc.creatorGoicoechea, Hector Casimiro-
dc.creatorGalvão, Roberto Kawakami Harrop-
dc.creatorAraújo, Mario Cesar Ugulino-
dc.date2018-09-07T13:58:38Z-
dc.date2018-09-07T13:58:38Z-
dc.date2018-05-
dc.date2018-09-06T18:44:29Z-
dc.date.accessioned2019-04-29T15:27:49Z-
dc.date.available2019-04-29T15:27:49Z-
dc.date.issued2018-09-07T13:58:38Z-
dc.date.issued2018-09-07T13:58:38Z-
dc.date.issued2018-05-
dc.date.issued2018-09-06T18:44:29Z-
dc.identifierde Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Hector Casimiro; Galvão, Roberto Kawakami Harrop; et al.; Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm; Elsevier Science; Talanta; 181; 5-2018; 38-43-
dc.identifier0039-9140-
dc.identifierhttp://hdl.handle.net/11336/58681-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/294606-
dc.descriptionThis paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.-
dc.descriptionFil: de Almeida, Valber Elias. Universidade Federal da Paraíba; Brasil-
dc.descriptionFil: de Araújo Gomes, Adriano. Universidade Federal do Sul e Sudoeste do Pará; Brasil-
dc.descriptionFil: de Sousa Fernandes, David Douglas. Universidade Federal da Paraíba; Brasil-
dc.descriptionFil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina-
dc.descriptionFil: Galvão, Roberto Kawakami Harrop. Instituto Tecnológico de Aeronáutica; Brasil-
dc.descriptionFil: Araújo, Mario Cesar Ugulino. Universidade Federal da Paraíba; Brasil-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherElsevier Science-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.talanta.2017.12.064-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0039914017312699-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.subjectKERNEL PARTIAL LEAST SQUARES-
dc.subjectNEAR INFRARED SPECTROMETRY-
dc.subjectNONLINEAR MULTIVARIATE CALIBRATION-
dc.subjectSUCCESSIVE PROJECTIONS ALGORITHM-
dc.subjectSUGAR-
dc.subjectVARIABLE SELECTION-
dc.subjectOtras Ciencias Químicas-
dc.subjectCiencias Químicas-
dc.subjectCIENCIAS NATURALES Y EXACTAS-
dc.titleVis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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