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1H-NMR spectral data and H and C isotope abundances of virgin olive oils (VOOs) and their unsaponifiable fractions were analyzed by pattern recognition techniques, such as principal component analysis (PCA) and partial-least squares discriminant analysis (PLS-DA). The aim was to develop chemical tools for the authentication of VOOs according to their geographical origin or protected designation of origin (PDO), as well as to detect the mislabeling of the provenance of VOOs, at the regional or national level, or the mislabeling of non-PDO oils as PDO VOOs. The relationship between stable isotope abundances of the VOOs and their unsaponifiable fractions and the latitude of the VOO geographical origin was confirmed, but these criteria were not completely discriminant to differentiate VOOs according to their geographical origin. However, d H-NMR data and C isotope abundance of the bulk oil and its unsaponifiable fraction, outperformed the previously reported classification models. Moreover, the PLS-DA models to authenticate VOOs from Greece and detect non-Greek VOOs achieved over 93% of correct predictions.Practical applications: The research can be applied in the protection of consumers and honest producers and retailers, and provides potential tools for antifraud authorities and regulatory bodies, which face the challenge of detecting fraudulent practices that do not comply with EU regulations in the trade of VOOs, such as the mislabeling of VOOs produced in a certain geographical origin [Commission Implementing Regulation (EC)

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In order to guarantee food integrity, testing methods should also include the tools needed to verify the geographical origin described on the label. In the particular case of virgin olive oil (VOO), regulatory bodies have yet to establish a standard for geographical identification. This manuscript describes a procedure based on Raman spectroscopy to identify the provenance of these oils based on a classification criterion (European vs non-European VOOs). The sample collection is considered a relevant step in which multiple factors are taken into account (cultivar distribution, complexity of production in each location, new agricultural practices, and new/old cultivars). A total of 78 virgin olive oils collected around the world allow checking the classification accuracy of the model for identifying sample origin. The PLS-model built around the Raman spectroscopic data is validated with a blind set of samples, and the results are evaluated in terms of false negative and false positive quality parameters. This study provides an analytical application to detect mislabeling and fraudulent practices related to the geographical provenance of virgin olive oils declared on the labels. Practical Applications: The results of this study provide a robust mathematical model to assess the origin of virgin olive oils (EU vs non-EU) based on spectroscopic data. Nowadays, the extensive knowledge of olive oil chemical composition has proven that this composition varies according to pedoclimatic conditions, while non-targeted methods can be proposed in geographical traceability for their ability to analyze the chemical profile of samples. However, it was deemed necessary to conduct a study through a collaborative work initiative. Thus, this study provides a strict evaluation of Raman spectroscopy through a defined strategy for evaluating non-targeted methods for geographical identification.