Six Tunisian virgin olive oil (VOO) varieties, Chemlali Sfax, Chetoui, Chemchali, Oueslati, Zarrazi and Zalmati, were characterised by two analytical methods. The gas chromatography allowed the determination of 14 fatty acids and squalene amounts. With fatty acids of each variety, a characteristic "morphotypes" for each oil variety was established. Chemlali Sfax and Zalmati showed strong similarities. Gas chromatography of fatty acid methyl esters (FAME) and near infrared (NIR) spectra of oils, associated to chemometric treatment, allowed the study of the inter-varietal variability and the verification of the variety origins of some Tunisian commercial VOOs. The specificity of Tunisian VOOs was evaluated by comparing the samples to Algerian, Moroccan and French Protected Designation of Origin VOOs. Classification in varietal origins by SIMCA used the FAME compositions and NIR spectra of the most represented varieties (Chemlali Sfax, Chetoui and Oueslati) showed a high potential to authenticate the varietal origin of Tunisian VOOs.

PLS-DA performed for the two most representative varieties (Chemlali Sfax and Chetoui). The obtained percentages of correct classification (superior to 97 %) proved the potential of the used method in varietal origin authentication.

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To discriminate samples from three varieties of Tunisian extra virgin olive oils, weighted and non-weighted multiblock partial least squares-discriminant analysis (MB-PLS1-DA) models were compared to PLS1-DA models using data obtained by gas chromatography (GC), or global composition through mid-infrared spectra (MIR). Models performances were determined using percentages of sensitivity, specificity and total correct classification. The choice of threshold level for the interpretation of PLS1-DA results was considered. PLS1-DA models using GC data gave better results than those using MIR data. Even with the most conservative threshold, PLS1-DA on GC data allowed very good predictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate Chetoui and Oueslati samples (95% and 84% correct classification respectively). Non-weighted MB-PLS1-DA models benefiting from the synergy between the two sources of data were more discriminative than simple PLS1-DA, yielding better prediction for Chetoui and Oueslati varieties (98% and 90% correct classification respectively).