dc.creatorLima, SLT
dc.creatorMello, C
dc.creatorPoppi, RJ
dc.date2005
dc.date46813
dc.date2014-11-16T16:12:38Z
dc.date2015-11-26T17:25:37Z
dc.date2014-11-16T16:12:38Z
dc.date2015-11-26T17:25:37Z
dc.date.accessioned2018-03-29T00:12:51Z
dc.date.available2018-03-29T00:12:51Z
dc.identifierChemometrics And Intelligent Laboratory Systems. Elsevier Science Bv, v. 76, n. 1, n. 73, n. 78, 2005.
dc.identifier0169-7439
dc.identifierWOS:000228149100008
dc.identifier10.1016/j.chemolab.2004.09.007
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/70579
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/70579
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/70579
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1284358
dc.descriptionIn this article, a new approach called partial least squares (PLS) pruning is described for variable selection in PLS modeling. The aim of the method is the deletion of unimportant PLS coefficients of regression by using information from all second derivatives of the error function. The proposed approach was applied to Brix determination in sugar cane juice by near infrared spectroscopy. The results obtained were promising, leading to a meaningful variable reduction of 96% without loss of model prediction capability. (c) 2004 Elsevier B.V. All rights reserved.
dc.description76
dc.description1
dc.description73
dc.description78
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationChemometrics And Intelligent Laboratory Systems
dc.relationChemometrics Intell. Lab. Syst.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectpartial least squares
dc.subjectvariable selection
dc.subjectHessian matrix of errors
dc.subjectWavelength Selection
dc.titlePLS pruning: a new approach to variable selection for multivariate calibration based on Hessian matrix of errors
dc.typeArtículos de revistas


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