| 光谱学与光谱分析 |
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| Genetic Algorithm Based Multi-Objective Least Square Support Vector Machine for Simultaneous Determination of Multiple Components by Near Infrared Spectroscopy |
| XU Bing1, WANG Xing1, 3, Dhaene Tom2, SHI Xin-yuan1*, Couckuyt Ivo2, BAI Yan3, QIAO Yan-jiang1* |
1. Research Center of TCM Information Engineering, Beijing University of Chinese Medicine, Beijing 100029, China
2. Department of Information Technology, Ghent University- iMINDS, B-9050 Gent, Belgium
3. Henan College of Traditional Chinese Medicine, Zhengzhou 450008, China |
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Abstract 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.
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Received: 2013-04-16
Accepted: 2013-07-15
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Corresponding Authors:
SHI Xin-yuan, QIAO Yan-jiang
E-mail: yjqiao@263.net;shixinyuan01@163.com
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