Stepwise strategy based on untargeted metabolomic 1H NMR fingerprinting and pattern recognition for the geographical authentication of virgin olive oils

Abstract

1H NMR fingerprinting of virgin olive oils (VOOs) and a collection of binary classificationmodels arranged in a decision tree are presented as a stepwise strategy to determine thegeographical origin of a VOO at four levels, i.e. provenance from an EU member state oroutside the EU, country and region of origin, and compliance with a geographicalindication scheme. This approach supports current EU regulation that makes labelling ofthe geographical origin mandatory for olive oil. Currently, official methods for its controlare still lacking. Partial least squares discriminant analysis (PLS-DA) and random forestfor classification afforded robust and stable binary classification models to verify thegeographical origin of VOOs; however, the former outperformed the latter in terms ofaccuracy and robustness. The prediction abilities of the best binary PLS-DA model foreach case study were between 80% and 100% for both classes in cross-validation and inexternal validation. The satisfactory results achieved for the verification of thegeographical origin of VOOs, together with those of our previous studies on thediscrimination of olive oil categories, the detection of olive oils blended with vegetableoils (Alonso-Salces et al., 2022), and the determination of the stability, freshness, storagetime and conditions, and olive oil best−before date (Alonso-Salces et al., 2021), confirmthat a single H NMR analysis of an olive oil sample can provide useful information tocontrol several EU regulations related to olive oil marketing standards (Regulation (EU)2022/2104 and Regulation (EU) 2024/1143).