Near-Infrared Wavelength-Selection Method Based on Joint Mutual Information and Weighted Bootstrap Sampling
Kai Wang, Wenli Du, Jian Long
Abstract
Near-infrared (NIR) spectroscopy is widely used to estimate product quality and other key variables. Eliminating redundant variables is very important in constructing a high-quality NIR model. This article proposes a new wavelength-selection method for NIR spectroscopy based on joint mutual information and weighted bootstrap sampling (WBS). The method considers the combination effect of variables and involves the dynamic selection of wavelength in each iteration to increase the model prediction accuracy. The index based on joint mutual information is used to determine the importance of variables and thus accurately reflects the variable-combination effect. WBS is further used to dynamically adjust the importance of candidate variables, i.e., to increase the weights of samples with poor prediction results and decrease those of samples with accurate prediction. This process ensures that the subsequently selected wavelength focuses on inaccurately estimated samples. The performance of this method is demonstrated through three NIR datasets of gasoline, shootout, and diesel fuels. The proposed method is found to have better accuracy than the traditional partial-least-squares method, variable iterative space shrinkage approach, and several other wavelength-selection methods.