title: Classification of modified starches by fourier transform infrared spectroscopy using support vector machines.
abstract: The aim of this study was to compare the performance of different supervised discrimination methods based on IR data for the classification of starches according to the type of chemical modification undergone. The goal of the supervised classification methods is to develop classification rules. Representative samples of each group (known beforehand) were available, from which the relevant characteristics (chemical modification) were known. On the basis of a training data set, classification rules are determined, which can then be applied to classify new (unknown) samples.
authors.affiliation: Statistics and Computer Sciences Department, University of Agronomical Sciences, Avenue de la Faculté 8, 5030 Gembloux, Belgium.
authors.forename: J A
authors.initials: JA
authors.lastname: Fernández Pierna
authors.forename: P
authors.initials: P
authors.lastname: Volery
authors.forename: R
authors.initials: R
authors.lastname: Besson
authors.forename: V
authors.initials: V
authors.lastname: Baeten
authors.forename: P
authors.initials: P
authors.lastname: Dardenne
mesh_terms: D017550:Spectroscopy, Fourier Transform Infrared; D013213:Starch / Q000737:chemistry* / Q000145:classification*
chemical_list: D013213:Starch