The analysis of high dimensional dataset is recurrently used in chemometrics where the data are presented in
the form of digitized spectra (NIR). Statistical tool, as Discriminant Analysis, is frequently used in this field to classify object
in predefined categories. But, by the fact that this kind of dataset presents the number of statistic units relatively small
in comparison to the number of variables, the classical Discriminant Analysis can not be applied. In this paper, the
authors, present a strategy to choose an optimal subset of predictors to perform Discriminant Analysis on NIR data in partial
least squares framework.
Keywords: Spectroscopic data, Linear Discriminant Analysis, Discriminant Partial least squares, Guttman-Raveh DisCo index
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