title: Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk.
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.
authors.affiliation: Instituto de Química, Universidade Estadual de Campinas, C.P. 6154, CEP 13083-970 Campinas, SP, Brazil.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Borin
authors.forename: Marco Flôres
authors.initials: MF
authors.lastname: Ferrão
authors.forename: Cesar
authors.initials: C
authors.lastname: Mello
authors.forename: Danilo Althmann
authors.initials: DA
authors.lastname: Maretto
authors.forename: Ronei Jesus
authors.initials: RJ
authors.lastname: Poppi