Combination of vintage and new-fashioned analytical approaches for varietal and geographical traceability of olive oils. (August 2019) - Sayago, Ana; González-Domínguez, Raúl; Urbano, Juan; Fernández-Recamales, Ángeles
Abstract: There is a great need for having accurate analytical methods able to guarantee the authenticity and traceability of foods, especially for those of high quality and economic value such as extra-virgin olive oil. In the present work, we assessed the potential of combining traditional analytical techniques, based on the characterization of the unsaponifiable fraction, together with novel nuclear magnetic resonance fingerprinting with the aim to investigate the effect of variety and geographical origin on olive oils collected from different locations across the province of Huelva (Spain). Various complementary supervised pattern recognition procedures and machine learning algorithms were then applied to build classification and predictive models. Extra-virgin olive oils were characterized by high concentrations of apparent β-sitosterol (93% of total sterol content), α-tocopherol (representing almost 91% of the total tocopherol fraction), squalene (90% of the total hydrocarbon content), heptacosanol and eicosane (the most abundant aliphatic alcohol and n-alkane, respectively). Furthermore, olive oil classes could be clearly differentiated on the basis of a characteristic chemical pattern, comprising tocopherols, squalene, sterols (campesterol, stigmasterol, β-sitosterol), aliphatic alcohols (heptacosanol, octacosanol) and some nuclear magnetic resonances related to fatty acid chains. Graphical abstract: Image 1 Highlights: 1 H NMR and UF profiling were applied for EVOO traceability. UF discriminant variables were tocopherols, squalene, sterols and alcohols. NMR signals related to fatty acids classify EVOOs according to origin and variety. Predictive models were built by using different machine learning tools.