Wavelet orthogonal signal correction-based discriminant analysis.

We report the use of wavelet orthogonal signal correction (WOSC) for multivariate classification. This new classification tool combines a wavelet prism decomposition of a spectral response and orthogonal signal correction to significantly improve the classification performance, reducing both classification errors and model complexity. Two spectroscopic data sets are examined in this paper. We show that a discriminant analysis based on WOSC effectively removes irrelevant classification information from spectral responses. WOSC-based discriminant analysis performs favorably as compared to a wavelength-domain filtering approach, such as that used in orthogonal partial least-squares discriminant analysis (OPLS-DA).