A Study on the Clustering of Extra Virgin Olive Oils Extracted from Cultivars Growing in Four Ionian Islands (Greece) by Multivariate Analysis of Their Phenolic Profile, Antioxidant Activity and Genetic Markers
2021
Iliana Kalaboki | Dionysios Koulougliotis | Dimitra Kleisiari | Eleni Melliou | Prokopios Magiatis | Adamantia Kampioti | Effimia Eriotou | Aspasia Destouni
Background: The phenolic fraction of extra virgin olive oil (EVOO) has disease preventive and health-promoting properties which are supported by numerous studies. As such, EVOO is defined as a functional food. The aim of the present study was to characterize the phenolic profile of olive oil from cultivars farmed in the Ionian Islands (Zakynthos, Kefalonia, Lefkada, and Kerkyra) and to investigate the association of phenols to antioxidant activity, which is central to its functionality. Furthermore, the study investigates whether multivariate analyses on the concentration of individual biophenolic compounds and genetic population diversity could classify the olive oil samples based on their geographic origin. Methods: Phenols were determined in 103 samples from different Ionian Island tree populations by 1H nuclear magnetic resonance (NMR), and sample antioxidant activity was measured by their capacity to reduce the free radical 2,2-diphenyl-1-picrylhydrazyl) (DPPH). Genetic diversity was measured by estimating Nei&rsquo:s population genetic distance using 15 reproducible bands from random amplified polymorphic DNA (RAPD) genotyping. Results: Principal component analysis (PCA) of the secoiridoid concentrations clustered samples according to cultivar. Clustering based on genetic distances is not concordant with phenolic clustering. A cultivar effect was also demonstrated in the association between the concentration of individual phenols with DPPH reducing activity. Conclusions: Taken together, the study shows that the olive oil phenolic content defines &ldquo:cultivar-specific phenolic profiles&rdquo: and that environmental factors other than agronomic conditions contribute more to phenotype variance than genetics.
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