  The recent development of more sophisticated spectroscopic methods allows
acqui- sition of high dimensional datasets from which valuable information may
be extracted using multivariate statistical analyses, such as dimensionality
reduction and automatic classification (supervised and unsupervised). In this
work, a supervised classification through a partial least squares discriminant
analysis (PLS-DA) is performed on the hy- perspectral data. The obtained
results are compared with those obtained by the most commonly used
classification approaches.
