| Summary: | The application of <sup>1</sup>H and <sup>13</sup>C nuclear magnetic resonance (NMR) in conjunction with chemometric methods was applied for the discrimination and authentication of Maltese extra virgin olive oils (EVOOs). A total of 65 extra virgin olive oil samples, consisting of 30 Maltese and 35 foreign samples, were collected and analysed over four harvest seasons between 2013 and 2016. A preliminary examination of <sup>1</sup>H NMR spectra using unsupervised principle component analysis (PCA) models revealed no significant clustering reflecting the geographical origin. In comparison, PCA carried out on <sup>13</sup>C NMR spectra revealed clustering approximating the geographical origin. The application of supervised methods, namely partial least squares discriminate analysis (PLS-DA) and artificial neural network (ANN), on <sup>1</sup>H and <sup>13</sup>C NMR spectra proved to be effective in discriminating Maltese and non-Maltese EVOO samples. The application of variable selection methods significantly increased the effectiveness of the different classification models. The application of <sup>13</sup>C NMR was found to be more effective in the discrimination of Maltese EVOOs when compared to <sup>1</sup>H NMR. Furthermore, results showed that different <sup>1</sup>H NMR pulse methods can greatly affect the discrimination of EVOOs. In the case of <sup>1</sup>H NMR, the Nuclear Overhauser Effect (NOESY) pulse sequence was more informative when compared to the zg30 pulse sequence, since the latter required extensive spectral manipulation for the models to reach a satisfactory level of discrimination.
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