Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk
Author: Borin, A
Ferrao, MF
Mello, C
Maretto, DA
Poppi, RJ
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. (c) 2006 Elsevier B.V. All rights reserved.
Subject: powdered milk
multivariate calibration
support vector machines
Country: Holanda
Editor: Elsevier Science Bv
Citation: Analytica Chimica Acta. Elsevier Science Bv, v. 579, n. 1, n. 25, n. 32, 2006.
Rights: fechado
Identifier DOI: 10.1016/j.aca.2006.07.008
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
WOS000241229000004.pdf523.84 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.