A two-stage regression approach for spectroscopic quantitative analysis

作者：Douak F; Benoudjit N; Melgani F^*

来源：Chemometrics and Intelligent Laboratory Systems, 2011, 109(1): 34-41.

DOI：10.1016/j.chemolab.2011.07.007

• Spectrometry
• Residual-based correction (RBC)
• Boosting
• Support vector machines (SVM)
• Radial basis function neural network (RBFN)
• Partial least squares regression (PLSR)
• Feature selection

In this paper, we propose a two-stage regression approach, which is based on the residual correction concept. Its underlying idea is to correct any given regressor by analyzing and modeling its residual errors in the input space. We report and discuss results of experiments conducted on three different datasets in infrared spectroscopy and designed in such a way to test the proposed approach by: 1) varying the kind of adopted regression method used to approximate the chemical parameter of interest. Partial least squares regression (PLSR), support vector machines (SVM) and radial basis function neural network (RBF) methods are considered; 2) adopting or not a feature selection strategy to reduce the dimension of the space where to perform the regression task. A comparative study with another approach which exploits differently estimation errors, namely adaptive boosting for regression (AdaBoost.R), is also included. The obtained results point out that the residual-based correction approach (RBC) can improve the accuracy of the estimation process. Not all the improvements are statistically significant but, at the same time, no case of accuracy decrease has been observed.

• 出版日期2011-11-15