Regression models based on new local strategies for near infrared spectroscopic data

dc.creatorAllegrini, Franco
dc.creatorFernández Pierna, J. A.
dc.creatorFragoso, W. D.
dc.creatorOlivieri, Alejandro Cesar
dc.creatorBaeten, V.
dc.creatorDardenne, P.
dc.date.accessioned2018-07-19T17:35:08Z
dc.date.accessioned2018-11-06T14:05:59Z
dc.date.available2018-07-19T17:35:08Z
dc.date.available2018-11-06T14:05:59Z
dc.date.created2018-07-19T17:35:08Z
dc.date.issued2016-08
dc.identifierAllegrini, Franco; Fernández Pierna, J. A.; Fragoso, W. D.; Olivieri, Alejandro Cesar; Baeten, V.; et al.; Regression models based on new local strategies for near infrared spectroscopic data; Elsevier Science; Analytica Chimica Acta; 933; 8-2016; 50-58
dc.identifier0003-2670
dc.identifierhttp://hdl.handle.net/11336/52648
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1882874
dc.description.abstractIn this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.
dc.languageeng
dc.publisherElsevier Science
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.aca.2016.07.006
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267016308273
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectLOCAL REGRESSION MODELS
dc.subjectNEAR INFRARED SPECTROSCOPY
dc.subjectPARTIAL LEAST SQUARES REGRESSION
dc.titleRegression models based on new local strategies for near infrared spectroscopic data
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.typeArtículos de revistas


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