A discriminant analysis technique using wavelet transformation (WT) and in°uence matrix analysis (CAIMAN) method is proposed for the near infrared (NIR) spectroscopy classi¯-cation. In the proposed methodology, NIR spectra are decomposed by WT for data compression and a forward feature selection is further employed to extract the relevant information from the wavelet coe±cients, reducing both classi¯cation errors and model complexity. A discriminant-CAIMAN (D-CAIMAN) method is utilized to build the classi¯cation model in wavelet domain on the basis of reduced wavelet coe±cients of spectral variables. NIR spectra data set of 265 salviae miltiorrhizae radix samples from 9 di®erent geographical origins is used as an example to test the classi¯cation performance of the algorithm. For a comparison, k-nearest neighbor (KNN), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods are also employed. D-CAIMAN with wavelet-based feature selection (WD-CAIMAN) method shows the best performance, achieving the total classi¯cation rate of 100% in both cross-validation set and prediction set. It is worth noting that the WD-CAIMAN classi¯er also shows improved sensitivity, selectivity and model interpretability in the classi¯cations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.