Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/197094
Type: Artigo de periódico
Title: Least-squares Support Vector Machines And Near Infrared Spectroscopy For Quantification Of Common Adulterants In Powdered Milk.
Author: Borin, Alessandra
Ferrão, Marco Flôres
Mello, Cesar
Maretto, Danilo Althmann
Poppi, Ronei Jesus
Abstract: This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.
Citation: Analytica Chimica Acta. v. 579, n. 1, p. 25-32, 2006-Oct.
Rights: fechado
Identifier DOI: 10.1016/j.aca.2006.07.008
Address: http://www.ncbi.nlm.nih.gov/pubmed/17723723
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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