Hyperspectral images are typically acquired at high spatial and spectral resolutions, being essential the reduction of data for the implementation of this technology at industrial level. The aim of this work was the optimization and development of algorithms for the selection of the region of interest in oranges hyperspectral data. PLS and its multilinear version, NPLS, were used to model the internal quality of oranges. The results obtained in external validation enabled to carry out a screening of the product according to its flavour, confirming that the use of multilinear models could reduce the noise and data redundancy.
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