In order to understand the distribution of the main secondary metabolites found in Olea europaea L., eight different samples (olive leaf, stem, seed, fruit skin and pulp, as well as virgin olive oil, olive oil obtained from stoned and dehydrated fruits and olive seed oil) coming from a Picudo cv. olive tree were analyzed. All the experimental conditions were selected so as to assure the maximum coverage of the metabolome of the samples under study within a single run. The use of LC and GC with high resolution MS (through different ionization sources, ESI and APCI) and the annotation strategies within MetaboScape 3.0 software allowed the identification of around 150 compounds in the profiles, showing great complementarity between the evaluated methodologies. The identified metabolites belonged to different chemical classes: triterpenic acids and dialcohols, tocopherols, sterols, free fatty acids, and several sub-types of phenolic compounds. The suitability of each platform and polarity (negative and positive) to determine each family of metabolites was evaluated in-depth, finding, for instance, that LC-ESI-MS (+) was the most efficient choice to ionize phenolic acids, secoiridoids, flavonoids and lignans and LC-APCI-MS was very appropriate for pentacyclic triterpenic acids (MS (−)) and sterols and tocopherols (MS (+)). Afterwards, a semi-quantitative comparison of the selected matrices was carried out, establishing their typical features (e.g., fruit skin was pointed out as the matrix with the highest relative amounts of phenolic acids, triterpenic compounds and hydroxylated fatty acids, and seed oil was distinctive for its high relative levels of acetoxypinoresinol and tocopherols).

Looking for a strategy to authenticate the declared origin of commercial extra virgin olive oils (EVOOs), 126 samples from six different Mediterranean geographical indications (GIs) are analyzed by means of two different platforms [LC‐ESI‐QTOF MS (in positive and negative polarity) and GC‐APCI‐QTOF MS (in positive mode)] combined to chemometrics. The sample treatment and chromatographic/detection conditions (in both platforms) are chosen to enable the comprehensive characterization of the complete minor fraction of the oils within a single run. Noticeable discrimination among the six evaluated GIs [Priego de Córdoba and Baena (Spain), Kalamata (Greece), Toscano (Italy), and Ouazzane and Meknès (Morocco)] is achieved building two‐class PLS‐DA models which consider the data coming from both platforms. The contribution of a few thousand molecular features to the statistical models is evaluated in depth and several compounds are pointed out as possible origin markers, describing characteristic compositional patterns for each GI in the evaluated crop year. The complementarity of the different analytical approaches is discussed and diverse strategies are used to identify the classifiers. Practical Applications: Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) labels are important tools to promote high quality EVOOs, assuring the connection to a particular territory and the characteristic combination of natural and human factors which make possible to obtain unique oils. In this context, it is imperative to furnish the control labs with innovative tools and methods which are able to provide extensive information about the EVOOs’ minor fraction (of unquestionable importance regarding its overall quality) in just one run and to give the chance to find and identify (and validate) origin markers. The utility of validated classifiers to authenticate the belonging (or not) of an EVOO to a particular GI is clear. The consumers’ confidence can be perceptibly undermined if the geographical name is used on products not having the expected qualities or if the production specifications are sometimes not followed by producers. The capability of LC‐MS and GC‐MS multi‐class methods to discriminate virgin olive oils from different geographical indications (crop season 2016‐2017) and to identify potential origin markers is demonstrated.

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