M. Fordellone, A. Bellincontro, F. Mencarelli
The recent development of more sophisticated spectroscopic methods allows acquisition 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 hyperspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches.
PLS-DA; hyperspectral data; high dimensional data; NIR; PLSR
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