The large number of spectral variables in most data sets encountered in spectral
chemometrics often renders the prediction of a dependent variable uneasy. The number of
variables hopefully can be reduced, by using either projection techniques or selection
methods; the latter allow for the interpretation of the selected variables. Since the optimal
approach of testing all possible subsets of variables with the prediction model is intractable,
an incremental selection approach using a nonparametric statistics is a good option, as it …