Hao Yong, Sun Xu-dong, Pan Yuan-yuan, Gao Rong-jie, Liu Yan-de
College of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang 330013, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 May;31(5):1225-9.
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 1451 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 1451 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.
在本研究中，近红外光谱（NIRS）被应用于梨的硬度和表面颜色的无损快速测量。为了提高预测精度并消除无信息变量对模型稳健性的影响，提出了蒙特卡罗无信息变量消除法（MC-UVE）和基于小波变换的蒙特卡罗无信息变量消除法（WT-MC-UVE）用于硬度和表面颜色近红外光谱建模中的变量选择。结果表明，WT-MC-UVE可将建模变量从1451个减少到210个，并在硬度预测方面获得相似的结果。WT-MC-UVE提高了表面颜色的预测精度，预测均方根误差（RMSEP）和校准变量分别从1.06和1451降低到0.90和220，相关系数（r）从0.975提高到0.981。所提出的方法能够从近红外光谱中选择重要波长，并且在硬度和表面颜色的定量分析中使预测更加稳健和准确。
