The application of 1H and 13C 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 1H NMR spectra using unsupervised principle component analysis (PCA) models revealed no significant clustering reflecting the geographical origin. In comparison, PCA carried out on 13C 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 1H and 13C 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 13C NMR was found to be more effective in the discrimination of Maltese EVOOs when compared to 1H NMR. Furthermore, results showed that different 1H NMR pulse methods can greatly affect the discrimination of EVOOs. In the case of 1H 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.

Achieving economic sustainability in the olive oil production sector is a challenge. This is particularly so for small scale producers who are faced with pressing, production and marketing costs that relative to overall sales, minimise profits. In this study we aimed to describe the phenolic profile of extra virgin olive oils (EVOOs) derived from the Maltese islands. The polar fractions from EVOOs from nine indigenous (six Bidni and three Malti), one historically acclimatized tree (Bajda), 12 locally-grown but foreign cultivars and 32 foreign EVOOs were extracted using SPE (solid phase extraction), separated using HPLC analysis at 280 nm and 320 nm and identified using mass spectrometry. Application of ANOVA and Tukey post hoc hypothesis testing for analysis of variance on the peak areas identified a significantly higher concentration of p-coumaric acid, tyrosol acetate, DHPEA-EDA and oleocanthal in EVOOs derived from indigenous or historically acclimatized cultivars. Imported but locally grown cultivars showed differences when compared to the same cultivar grown in other countries, confirming that pedo-climatic conditions modulate genetic factors.

Fluorescence spectrometry, combined with principle component analysis, partial least-squares regression (PLSR) and artificial neural network (ANN), was applied for the analysis of Maltese extra virgin olive oil (EVOO) adulterated by blending with vegetable oil (corn oil, soybean oil, linseed oil, or sunflower oil). The novel results showed that adjusted PLSR models based on synchronised spectra for detecting the % amount of EVOO in vegetable oil blends had a lower root mean square error (0.02-6.27%) and higher R (0.983-1.000) value than those observed when using PLSR on the whole spectrum. This study also highlights the use of ANN as an alternative chemometric tool for the detection of olive oil adulteration. The performance of the model generated by the ANN is highly dependent both on the type of data input and the mode of cross validation; for spectral data which had a variable importance plot value > 0.8 the excluded row cross validation was more appropriate while for complete spectral analysis -fold or CV-10 was more appropriate.

Maltese honey has been produced, marketed, and sold as an exclusive local gourmet food product for countless years. Yet, thus far, no study has evaluated the individuality of this local food product. The evaluation of the parameters and properties which characterise the provenance and floral source of honey have been the subject of various studies worldwide, owing to the price and potential beneficial properties of this food product. Models analysing the potential of attenuated total reflection mid-infrared (ATR-FT-MIR) spectroscopy in discriminating and classifying local honey from that of foreign origin were investigated using 21 Maltese honey samples and 49 honey samples collected from abroad (Sicily, Greece, Sweden, Italy, France, Estonia and other samples of mixed geographical origin). Through a combination of spectroscopic techniques, spectral transformations, variable selection and partial least squares discriminant analysis (PLS-DA), chemometric models which successfully classified the provenance of local and non-local honey were developed. The results of these models were also corroborated with other classification and pattern recognition techniques, such as linear discriminate analysis (LDA), support vector machines (SVM) and feed-forward artificial neural networks (FF-ANN).

Elemental analysis using energy-dispersive X-ray fluorescence on extra virgin olive oils and seed oils revealed the presence of two major concentration related clusters, one containing elements of pedological origin, whilst the other consisted of heavy metals. Seed oils were found to contain a higher concentration of titanium when compared to extra virgin olive oils, whilst extra virgin olive oils derived from the Maltese Islands had a significantly higher concentration of barium and phosphorus on using the Kruskal–Wallis one-way ANOVA (p-value < 0.05 for both elements). Application of stepwise linear canonical discriminate analysis proved to be highly superior to PCA, as it was able to distinguish between seed oils from extra virgin olive oils and distinguish between foreign and locally produced extra virgin olive oils.