基于特征投影图的小麦近红外光谱变量选择方法研究

为了简化模型，提高模型预测精度，利用特征投影图(LPG)进行变量选择。对原始光谱进行连续小波变换(CWT)，利用主成分分析(PCA)得到LPG，假定LPG中共线性光谱变量对建模作用相同，选出少数特征光谱变量建立预测模型，所得模型预测均方根误差(RMSEP)为0.345 4，优于其他建模方法，研究结果表明，LPG变量选择可有效简化近红外光谱模型，提高模型预测精度。

Research on Variable Selection of Wheat Near-Infrared Spectroscopy Based on Latent Projective Graph

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.