Liu Ke, Chen Xiaojing, Li Limin, Chen Huiling, Ruan Xiukai, Liu Wenbin
College of Physics and Electronic Engineering Information, Wenzhou University, Chashan University Town, Wenzhou, Zhejiang Province, People's Republic of China.
College of Physics and Electronic Engineering Information, Wenzhou University, Chashan University Town, Wenzhou, Zhejiang Province, People's Republic of China.
Anal Chim Acta. 2015 Feb 9;858:16-23. doi: 10.1016/j.aca.2014.12.033. Epub 2014 Dec 20.
The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.
逐次投影算法（SPA）被广泛用于为多元线性回归（MLR）建模选择变量。然而，仅使用一次SPA可能无法获取全光谱的所有有用信息，因为在SPA算法中所选变量的数量不能超过校准样本的数量。因此，SPA-MLR方法存在丢失有用信息的风险。为了充分利用光谱中的有用信息，本文提出了一种名为“一致性SPA-MLR”（C-SPA-MLR）的新方法。该方法是一致性策略与SPA-MLR方法的结合。在C-SPA-MLR方法中，SPA-MLR用于构建具有不同变量子集的成员模型，这些变量子集是从剩余变量中迭代选择的。通过组合成员模型的预测结果获得一致性预测。通过分析玉米和柴油的近红外（NIR）光谱对所提出的方法进行了评估。C-SPA-MLR方法的结果显示出比SPA-MLR和全光谱PLS方法更好的预测性能。此外，这些结果可为在分析近红外光谱和其他光谱技术时将一致性策略与其他变量选择方法相结合提供参考。
