title: [Genetic algorithm based multi-objective least square support vector machine for simultaneous determination of multiple components by near infrared spectroscopy].
abstract: The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different proper ties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multiobjective least square support vector machine (MLS-SVM), which was intended to simultaneously determine the concentrations of multiple components by NIR spectroscopy. Both the benchmark corn dataset and self-made Forsythia suspense dataset were used to test the proposed approach. Results show that a genetic algorithm combined with adaptive modeling allows to efficiently search the LS-SVM hyperparameter space. For the corn data, the performance of multi-objective LS-SVM was significantly better than models built with PLS1 and PLS2 algorithms. As for the Forsythia suspense data, the performance of multi-objective LS-SVM was equivalent to PLS1 and PLS2 models. In both datasets, the over-fitting phenomena were observed on RBFNN models. The single objective LS-SVM and MLS-SVM didn't show much difference, but the one-time modeling convenience al lows the potential application of MLS-SVM to multicomponent NIR analysis.
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.forename: Xing
authors.initials: X
authors.lastname: Wang
authors.forename: Tom
authors.initials: T
authors.lastname: Dhaene
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Shi
authors.forename: Ivo
authors.initials: I
authors.lastname: Couckuyt
authors.forename: Yan
authors.initials: Y
authors.lastname: Bai
authors.forename: Yan-Jiang
authors.initials: YJ
authors.lastname: Qiao