Geographical authentication of virgin olive oil by GC–MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration. (1st June 2022)

• Record Type: Journal Article
• Authors: Quintanilla-Casas, Beatriz; Torres-Cobos, Berta; Guardiola, Francesc; Servili, Maurizio; Alonso-Salces, Rosa Maria; Valli, Enrico; Bendini, Alessandra; Toschi, Tullia Gallina; Vichi, Stefania; Tres, Alba
• Is Part Of: Food chemistry. Volume 378(2022)
• Publication Date: 2022-06-01
• Subjects: Virgin Olive Oil -- Authenticity -- Geographical origin -- Sesquiterpene hydrocarbons -- Fingerprint -- SPME-GC–MS -- PLS-DA; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132104
• Languages: English
• ISSNs: 0308-8146

Abstract

Graphical abstract: Highlights: Sesquiterpene hydrocarbons (SH) are suitable virgin olive oil geographical markers. SH fingerprint and chemometrics allowed to discriminate EU from non-EU oils. The fingerprinting approach was successfully applied to verify the country of origin. This tool could serve to authenticate samples from different production areas. Abstract: According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC–MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).