%A XU Bing;WANG Xing;;Dhaene Tom;SHI Xin-yuan*;Couckuyt Ivo;BAI Yan;QIAO Yan-jiang* %T Genetic Algorithm Based Multi-Objective Least Square Support Vector Machine for Simultaneous Determination of Multiple Components by Near Infrared Spectroscopy %0 Journal Article %D 2014 %J SPECTROSCOPY AND SPECTRAL ANALYSIS %R 10.3964/j.issn.1000-0593(2014)03-0638-05 %P 638-642 %V 34 %N 03 %U {https://www.gpxygpfx.com/CN/abstract/article_6881.shtml} %8 2014-03-01 %X The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different properties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multi-objective 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 allows the potential application of MLS-SVM to multicomponent NIR analysis.