Chilean wine varietal classification using quadratic Fisher transformation

Abstract

This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction technique called quadratic Fisher transformation. Classification is based on liquid chromatograms of polyphenolic compounds present in wine samples, obtained from a high performance liquid chromatograph with diode alignment detector. For comparison purposes three other feature extraction methods are studied: linear Fisher transformation, Fourier transform and wavelet transform, maintaining QDA as classification scheme. From experimental results it is possible to conclude that when using quadratic discriminant analysis as classification method, the percentage of correct classification was improved from 91% (obtained for the case of wavelet extraction) to 99% when employing quadratic Fisher transformation as feature extraction method.