  Prediction problems from spectra are largely encountered in chemometry. In
addition to accurate predictions, it is often needed to extract information
about which wavelengths in the spectra contribute in an effective way to the
quality of the prediction. This implies to select wavelengths (or wavelength
intervals), a problem associated to variable selection. In this paper, it is
shown how this problem may be tackled in the specific case of smooth (for
example infrared) spectra. The functional character of the spectra (their
smoothness) is taken into account through a functional variable projection
procedure. Contrarily to standard approaches, the projection is performed on a
basis that is driven by the spectra themselves, in order to best fit their
characteristics. The methodology is illustrated by two examples of functional
projection, using Independent Component Analysis and functional variable
clustering, respectively. The performances on two standard infrared spectra
benchmarks are illustrated.
