Geographic classification of extra virgin olive oils from the eastern Mediterranean by chemometric analysis of visible and near-infrared spectroscopic data

Visible and near-infrared reflectance spectra have been examined for their ability to classify extra virgin olive oils from the eastern Mediterranean on the basis of their geographic origin. Classification strategies investigated were partial least-squares regression, factorial discriminant analysis, and k-nearest neighbors analysis. Discriminant models were developed and evaluated using spectral data in the visible (400-750 nm), near-infrared (1100-2498 nm), and combined (400-2498 nm) wavelength ranges. A variety of data pretreatments was applied. Best results were obtained using factorial discriminant analysis on raw spectral data over the combined wavelength range; a correct classification rate of 93.9% was obtained on a prediction sample set. Though the overall sample set was limited in numbers, these results demonstrate the potential of near-infrared spectroscopy to classify extra virgin olive oils on the basis of their geographic origin.