title: [Research on variable selection of wheat near-infrared spectroscopy based on latent projective graph].
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.
authors: College of Science, Changchun University of Science and Technology, Changchun 130022, China. huankewei@126.com
authors: Ke-Wei
authors: KW
authors: Huan
authors: Feng
authors: F
authors: Zheng
authors: Xiao-Xi
authors: XX
authors: Liu
authors: Xiao-Long
authors: XL
authors: Cai
authors: Hong-Xing
authors: HX
authors: Cai
authors: Rui
authors: R
authors: Wang
authors: Xiao-Guang
authors: XG
authors: Shi
mesh_terms: D005544:Forecasting; D008962:Models, Theoretical; D010940:Plant Proteins / Q000737:chemistry; D025341:Principal Component Analysis / Q000379:methods; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D060388:Support Vector Machine; D014908:Triticum / Q000737:chemistry*; D058067:Wavelet Analysis
chemical_list: D010940:Plant Proteins