近红外光谱(NIRS)分析方法用于梨的硬度和表面色泽的无损快速定量分析, 提高了分析方法的预测精度, 消除无信息建模变量对模型稳定性的影响。 分别采用蒙特卡罗无信息变量消除(Monte Carlo uninformative variables elimination, MC-UVE)和基于小波变换(wavelet transform, WT)的蒙特卡罗无信息变量消除(WT-MC-UVE)方法对梨的硬度和表面色泽的建模变量进行筛选。 结果表明, 对于硬度模型, 采用WT-MC-UVE方法, 210个变量可以得到和原始光谱(1 451个变量)近似的预测结果； 对于表面色泽的预测模型, 采用WT-MC-UVE方法后, 建模变量减少为220, 模型的预测均方根误差从1.06减小为0.90, 预测相关系数从0.975提高为0.981。 因此, WT-MC-UVE方法可以有效地选择建模变量, 既能提高模型的稳定性, 又能提高多元校正的预测精度。

In the present study, NIRS was applied to nondestructive and rapid measurement of firmness and surface color of pear. In order to improve the prediction precision and eliminate the influence of uninformative variables on model robustness, Monte Carlo uninformative variables elimination (MC-UVE) and Monte Carlo uninformative variables elimination based on wavelet transform (WT-MC-UVE) methods were proposed for variable selection in firmness and surface color NIR spectral modeling. Results show that WT-MC-UVE can reduce the modeling variables from 1 451 to 210, and get similar prediction results for firmness. WT-MC-UVE improved the prediction precision for surface color, the root mean square error of prediction (RMSEP) and calibration variables were reduced from 1.06 and 1 451 to 0.90 and 220 respectively, and the correlation coefficient (r) was improved from 0.975 to 0.981. The proposed method is able to select important wavelength from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis of firmness and surface color.