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