Cheng Zhong, Zhang Li-Qing, Liu He-Yang, Zhu Ai-Shi
Department of Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Apr;30(4):949-52.
Successive projections algorithm combined with partial least squares regression, termed as SPA-PLS approach, was implemented as a novel variable selection approach to multivariate calibration. The proposed approach was applied to near-infrared reflectance data for analyzing moisture in wheat. The number of variables selected from 701 spectral variables was reduced to 16 by SPA, and the root mean squared error of prediction set (RMSEP) of the corresponding partial least squares regression models was decreased to 0.205 5% as well. The result indicates that the SPA-PLS approach by performing SPA prior to calibration not only can improve the model accuracy, but also decreases the number of spectral variables, so its resulting model becomes more concise. Moreover, as compared with genetic algorithm for wavelength selection, SPA is a deterministic search technique whose results are reproducible and it is more robust with respect to the choice of the validation set.
将连续投影算法与偏最小二乘回归相结合，称为SPA - PLS方法，作为一种新的多变量校正变量选择方法得以实现。该方法应用于近红外反射率数据以分析小麦中的水分。通过连续投影算法，从701个光谱变量中选择的变量数量减少到了16个，相应偏最小二乘回归模型预测集的均方根误差（RMSEP）也降至0.205 5%。结果表明，在校准前执行连续投影算法的SPA - PLS方法不仅可以提高模型精度，还能减少光谱变量数量，因此其所得模型更加简洁。此外，与用于波长选择的遗传算法相比，连续投影算法是一种确定性搜索技术，其结果具有可重复性，并且在验证集的选择方面更具稳健性。
