Feature reduction by Fourier transform in pattern recognition of NIR data

A Fourier transform (FT) was used as a tool to reduce the number of variables in pattern recognition of NIR data. Five procedures were designed to select the FT coefficients as the input of the classifier of regularized discriminant analysis (RDA). 11 data sets were analysed and the results were also compared with other dimensionality reduction methods of Principal component analysis (PCA) and univariate feature selection method. Our results demonstrate that FT is a fast and powerful feature reduction method and that its results are comparable to those of PCA as a feature reduction method before classification. It has the additional advantage that feature reduction is applied to individual spectra instead of to a set of spectra, as in the case of PCA.