| 光谱学与光谱分析 |
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| Research on Variable Selection of Wheat Near-Infrared Spectroscopy Based on Latent Projective Graph |
| HUAN Ke-wei1, ZHENG Feng1, LIU Xiao-xi2, CAI Xiao-long1, CAI Hong-xing1, WANG Rui3, SHI Xiao-guang1 |
1. College of Science, Changchun University of Science and Technology, Changchun 130022, China
2. Institute of Scientific and Technical Information in Jilin Province, Changchun 130000, China
3. Beijing Oriental Info-Technology Development Center, Beijing 100037, China |
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Abstract To simplify the model and improve the precision of prediction model, latent projective graph (LPG) was used for variable selection. The original spectrum was processed by continuous wavelet transform (CWT), LPG was obtained by principal component analysis (PCA), and based on the assumption that collinear wavelengths might have the same contribution to the modeling, a few latent spectral variables were selected for establishing prediction model. The root mean square error of prediction (RMSEP) model was 0.3454, better than other modeling methods. This work proved that variable selection with LPG could simplify the near-infrared spectral model effectively, and improve the precision of prediction model.
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Received: 2012-05-02
Accepted: 2012-08-02
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Corresponding Authors:
HUAN Ke-wei
E-mail: huankewei@126.com
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