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The recent development of more sophisticated spectroscopic methods allowsacqui- sition of high dimensional datasets from which valuable information maybe extracted using multivariate statistical analyses, such as dimensionalityreduction and automatic classification (supervised and unsupervised). In thiswork, a supervised classification through a partial least squares discriminantanalysis (PLS-DA) is performed on the hy- perspectral data. The obtainedresults are compared with those obtained by the most commonly usedclassification approaches.

Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data