近红外光谱法分析油页岩含油率中波长选择方法的研究

波长选择是光谱建模分析的重要步骤。研究了近红外光谱法分析油页岩含油率过程中的波长选择方法，用以剔除光谱数据中的冗余信息和干扰信息，提高分析模型的建模效率和预测能力。分别采用相关系数法(CC)、移动窗口偏最小二乘法(MWPLS)和无信息变量消除法（UVE）对油页岩近红外漫反射光谱数据的波长区间进行了选择，研究了不同阈值、窗口宽度和噪声矩阵对上述方法的影响，建立了所选择波长处的反射率数据和样品含油率标准值间的偏最小二乘（PLS）分析模型，比较了上述方法的选择效果。结果表明：与使用全谱数据建模相比，采用上述方法筛选过的光谱数据均能提高模型的建模效率和预测能力，其中经UVE法筛选后的光谱数据仅占全谱数据总数的22.8%，模型的RMSECV却降低了9.3%，RMSEP降低了4.5%。

Research on Wavelength Variates Selection Methods for Determination of Oil Yield in Oil Shales using Near-Inf rared Spectroscopy

The wavelength selection is an important step in the spectra modeling analysis. In the present paper, three wavelength selection methods, including correlation coefficient (CC), moving window partial least squares (MWPLS) and uninformative variables elimination (UVE), were studied for the determination of oil yield in oil shale using near-infrared (NIR) diffuse reflection spectroscopy. The above methods were used to eliminate the redundant and irrelevant variables in spectral data for enhancing the analytic efficiency and predictive ability of calibration model. The effects of thresholds of CC, window width of MWPLS and noise matrix of UVE were studied. Partial least squares regression was used to build prediction model for predicting oil yield in oil shale, and the performance of PLS models constructed with and without the using of wavelength selection methods were compared. The results show that any of the three methods can simplify the calibration model and improve the performance of model. By using UVE, the total number of wavelength variables of spectral data, the RMSECV of calibration model and the RMSEP of prediction model were decreased by 22.8%, 9.3% and 4.5%, respectively.