The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.

中文翻译：

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移动窗口改进的蒙特卡洛无信息变量消除结合连续投影算法的近红外光谱（NIRS）

通常将MC-UVE-SPA方法作为变量选择方法进行多变量校准。但是，SPA倾向于选择在MC-UVE算法选择的变量的波长范围内稀疏分布的波长变量，而MC-UVE-SPA级联不能改善波长点不连续性的问题。本文通过提出一种改进的移动窗口（MW-）MC-UVE-SPA波长选择算法来解决此问题。该算法提高了所选波长变量的连续性，从而更好地利用了MC-UVE算法和SPA的优势，获得了具有较高预测精度的回归模型。MC-UVE，MC-UVE-SPA和MC-UVE-SPA-MW算法用于对玉米的NIR光谱吸收数据进行波长变量选择，柴油和乙烯。在此，使用MC-UVE算法进行波长选择后，建立了反映玉米油含量，柴油沸点和乙烯浓度的偏最小二乘回归（PLSR）模型，并建立了相应的多元线性回归（MLR）模型使用MC-UVE-SPA和MC-UVE-SPA-MW算法进行波长选择后，即可确定波长。实验结果表明，逐步消除不相关和共线性的变量会产生越来越简化的部分频谱模型，其预测准确度要高于全频谱模型。在这三种波长选择算法中，MC-UVE-SPA选择了最少数量的波长变量，