Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints

Accepted Manuscript Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints

Mbarka Ben Mohamed, Gabriele Rocchetti, Domenico Montesano, Sihem Ben Ali, Ferdaous Guasmi, Naziha GratiKamoun, Luigi Lucini PII: DOI: Reference:

S0963-9969(18)30103-0 https://doi.org/10.1016/j.foodres.2018.02.010 FRIN 7373

To appear in:

Food Research International

Received date: Revised date: Accepted date:

21 December 2017 27 January 2018 1 February 2018

Please cite this article as: Mbarka Ben Mohamed, Gabriele Rocchetti, Domenico Montesano, Sihem Ben Ali, Ferdaous Guasmi, Naziha Grati-Kamoun, Luigi Lucini , Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Frin(2017), https://doi.org/10.1016/ j.foodres.2018.02.010

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ACCEPTED MANUSCRIPT Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints

Mbarka Ben Mohameda,b,c, Gabriele Rocchettid*, Domenico Montesanoe, Sihem Ben Alia,b,c,

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Ferdaous Guasmib, Naziha Grati-Kamouna, Luigi Lucinif

Technology and Quality Research unit, Institute of the Olive, BP. 1087, 3000 Sfax, Tunisia.

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Arid Land and Oasis Cropping Laboratory, Institute of Arid Land, Route Eljorf, 4119

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Medenine, Tunisia.

Faculty of Sciences - Bizerte 7021 Jarzouna, Tunisia.

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Department of Animal Science, Food and Nutrition, Università Cattolica del Sacro Cuore,

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Via Emilia Parmense 84, 29122 Piacenza, Italy.

Department of Pharmaceutical Sciences, Section of Food Sciences and Nutrition, Via S.

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Costanzo, 06126 Perugia, Italy. f

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Department for sustainable food process, Università Cattolica del Sacro Cuore, Via Emilia

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parmense 84, 29122 Piacenza, Italy.

*

Corresponding author: Via Emilia Parmense 84, 29122 Piacenza (Italy). E-mail:

[email protected] – Tel.: +39 0523 599433.

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ACCEPTED MANUSCRIPT Abstract In the last years, olive oil authentication issues have become topics of prominent importance, not only for consumers, but also for suppliers, retailers, and administrative authorities, and particularly for assurance of public health. In this work, the sterolic and phenolic profile of Tunisian and Italian extra-virgin olive oil (EVOO) samples was depicted using an untargeted

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UHPLC-ESI/QTOF mass spectrometry approach. Polyphenols and sterols were quantified

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according to their chemical sub-classes, with high sterols (around 1000 up to 2000 mg/kg) and

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tyrosols (on average 420.2 mg/kg) contents detected. The metabolomics data were elaborated by means of multivariate statistics, i.e. unsupervised hierarchical cluster analysis and

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orthogonal projections to latent structures discriminant analysis (OPLS-DA). This approach allowed to identify the best markers (i.e. hydroxybenzoic acids, cholesterol and stigmasterols

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derivatives) of the geographical origin able to discriminate Tunisian and Italian EVOO samples, showing the potential of sterolic and phenolic fingerprints for olive oil authenticity

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evaluations.

Keywords: Extra-virgin olive oil; Food metabolomics; Food authentication; Polyphenols;

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Sterols; Multivariate statistics.

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ACCEPTED MANUSCRIPT 1. Introduction Olive (Olea europaea L.) is one of the most produced crops due to its use for oil production and table olives, with 65%, 16%, and 15% of worldwide production in Europe, Asia, and Africa, respectively (Oliveira & Kristbergsson, 2016; Putnik et al., 2017), and a very long cultivation history in the Mediterranean Basin (Efe, Soykan, Cürebal, & Sönmez,

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2011). At this regard, different parts of olive (i.e., fruits and leaves) are characterized by

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several health promoting effects, such as antioxidant properties (Vinceković et al., 2013).

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Moreover, in the last years, new technologies are emerging for the effective valorization of wastes and by-products generated during olive oil production (Roselló-Soto et al., 2015;

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Puértolas, Koubaa, & Barba, 2016). Tunisia is the most important olive-growing countries in the Southern Mediterranean, characterized by an important phenotypic and genetic diversity,

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whose oils enjoy organoleptic quality (ONH, 2016) as well as economic relevance (Dabbou et al., 2009).

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However, because of their high commercial value, extra-virgin olive oils (EVOOs) are

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highly susceptible to fraud (Bazakos et al., 2016). Thus, in recent years, considerable efforts have been carried out to characterize olive oils, often looking at markers for autochthonous

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cultivars (Dabbou et al., 2010; Issaoui et al., 2007; Laroussi-Mezghani et al., 2015). The quality and the organoleptic characteristics of EVOO can be affected by several factors such

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as planting, harvesting and technological processing (Gharbi et al., 2015). In the last years, food traceability can be considered as a competitive factor in the agro-food sector. Traceability can be defined according to the European Council Regulation EEC 178/2002 as “the ability to identify and trace a product or a batch of products at all stages of production and marketing”. With regards to the olive oils, the characterization of varieties and geographical origin is becoming very important in order to support both producers and consumers. Therefore, over the last years, there has been an increasing interest for the

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ACCEPTED MANUSCRIPT production of certified monovarietal and geographical origin-labeled olive oils. For this reason, accurate and promising analytical approaches able to identify the authentication of the geographical and botanical origin of EVOOs are constantly increasing (Lerma-García, SimóAlfonso, Méndez, Lliberia, & Herrero-Martínez, 2011; Loubiri et al., 2016). In addition, some authors stated the interesting use of statistical platforms to investigate nutritional and

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functional properties of different functional foods (Granato, Sávio Nunes, & Barba, 2017).

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Within this context, the research in this field is moving from classical methodologies to

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advance analytical strategies in which -omics approaches, especially metabolomics (chromatography coupled to mass spectrometry based methodologies followed by

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chemometric evaluations) plays a pivotal role to check the adulteration of some foodstuffs, such as extra-virgin olive oil, according to specific markers (Monfreda et al., 2012; Gázquez-

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Evangelista et al., 2014).

Olive oil is characterized by several “minor” compounds with interesting chemical and

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nutritional properties, such as carotenoids and sterols (Blasi et al., 2018; Montesano, Gennari,

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Seccia, & Albrizio, 2012; Fattore et al., 2016; Cercaci, Passalacqua, Poerio, RodriguezEstrada, & Lercker, 2007). Phytosterols are triterpenes that can be included among these

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latter, with the desmethylsterols being the most abundant in vegetable oils (Martínez-Vidal, Garrido-Frenich, Escobar-García, & Romero-González, 2007). Their presence in EVOO is

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strictly related to the botanical variety of olive together with the olive ripening degree, harvesting techniques and storage conditions; therefore, sterolic profile can be considered one of the most important tool for discriminating oil authenticity (Gutiérrez, Varona, & Albi, 2000). Some authors have evaluated the influence of geographical origin on sterolic composition of virgin olive oil (Ben Temine et al., 2008; Bagur-González, Pérez-Castano, Sánchez-Vinas, & Gázquez-Evangelista, 2015) showing the great potential of GC and LC platforms followed by chemometric tools for this purpose.

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ACCEPTED MANUSCRIPT Similarly, olive oils contain different classes of phenolic compounds such as phenolic acids, tyrosols/hydroxytyrosols, flavonoids (mainly luteolin) and lignans (Bayram et al., 2012; Blasi et al., 2018). Like sterols, phenolics content is affected by both genetic and environmental factors. High-resolution mass spectrometry is playing an emerging role in research on polyphenols, to achive a comprehensive phenolic profile after a technological

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process (Blasi et al., 2018; Rocchetti et al., 2017) or for authentication and traceability

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purposes (Lucini et al., 2017; Rocchetti et al., 2018). Therefore, the combination of innovative

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UHPLC/MS analytical platforms together with multivariate statistics, could represent a very efficient tool for food quality and authenticity assessments, according to geographical origin.

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On this basis, the aim of the present work was the application of untargeted UHPLCESI/QTOF mass spectrometry combined with multivariate statistics to profile sterols and

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polyphenols in EVOOs, extrapolating those marker compounds better discriminating the

2.1. EVOO samples

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2. Materials and Methods

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geographical origin (Tunisian vs Italian) of EVOO samples.

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A total of 26 samples were analyzed in this work: twenty-one were monovarietal Tunisian samples, while the remaining five were Italian. Indications regarding geographical origin,

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collection site, cultivars name, and quality level of the samples are provided in Table 1. Tunisian olives were collected from cultivars grown in different areas from the South-East of Tunisia. This region is characterized by an arid to semi-arid climate. The Beni khedacheMédenine and Toujane Matmata-Gabès regions are mountainy areas while the other ones are mainly characterized by plains. Italian samples were kindly provided by Coop Italia SpA (Casalecchio di Reno, Bologna, Italy), who owned the supply chain and could ensure geographical origins.

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ACCEPTED MANUSCRIPT Oil extraction was carried out using a laboratory extraction system called oleodoseur (composed of crusher, vertical malaxator and centrifuge) (Laroussi-Mezghani et al., 2015) from handpicked fresh olives at maturity time (2.5 kg) without storage time before extraction. The produced oils were filled in dark glass bottles and stored at 4 °C until analysis.

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2.2. Liquid-liquid extraction of phenolics and sterols

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Phenolic compounds and sterols were extracted in triplicate from each sample as follows:

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an aliquot (3 g) of oil was accurately weighted into conical centrifuge tube, and added with 3 mL of 80 % methanol solution (v/v) (LCMS grade, VWR, Milan Italy). The mixtures were

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vortexed vigorously and then centrifuged at 6,000 x g for 10 min at 4 °C. The methanol fractions were collected, whilst the residues were rejected. The resulting supernatants were

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filtered through 0.22 μm cellulose syringe filters and stored in amber vials at -20 °C until the following analysis through liquid chromatography mass spectrometry.

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2.3. UHPLC-ESI/QTOF screening of polyphenols and sterols The phenolic and sterolic fingerprints of EVOO methanol extracts were gained as

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previously described by Lucini and co-authors (2015), with small modifications. Analysis was carried out through ultra-high-performance liquid chromatography coupled to quadrupole-

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time-of-flight mass spectrometry by an electrospray ionization source (UHPLC-ESI/QTOF). A 1290 liquid chromatography system, equipped with a binary pump and a Dual Electrospray JetStream ionization source, coupled to a G6550 mass spectrometer detector (all from Agilent Technologies, Santa Clara, CA, USA) were used. The mass spectrometer was operating in positive ionization mode to acquire accurate masses in the 50-1000 m/z range. The chromatographic separation was performed on an Agilent Zorbax eclipse plus C18 analytical column (50 x 2.1 mm, 1.8 μm) and water-methanol gradient elution (from 10% to 90% organic in 34 minutes). The injection volume was 3 µL per sample. Source conditions were 6

ACCEPTED MANUSCRIPT the following: nitrogen was used both as sheath gas (10 L/min and 350 ° C) and as drying gas (8 L / min and 330 ° C), nebulizer pressure was 60 psig, nozzle voltage was 300 V and capillary voltage was 3.5 KV. The raw data processing was carried out using the software Profinder B.07 (Agilent

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Technologies), based on "find-by-formula" algorithm. Compounds identification was recursively carried out considering both accurate mass and isotopic pattern (isotope spacing

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and isotope ratio). A custom database obtained combining Phenol-Explorer 3.0 (Phenol-

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Explorer: an online comprehensive database on polyphenol contents in foods, http://phenolexplorer.eu/) and LIPID MAPS (Lipidomics Gateway, http://www.lipidmaps.org/) was used

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as a reference for identification, adopting 5 ppm tolerance for mass accuracy. Data pre-

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processing (mass and retention time alignment, compounds filtering) was realized in software Profinder B.07: only those compounds identified within 100% of replications in at least one treatment were retained. This processed dataset was finally used for statistics and

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chemometrics.

A calibration curve of cholesterol (Sigma grade, Sigma-Aldrich, S. Louis, MO, USA) was used to estimate the total sterols content. Furthermore, considering the availability of nine

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phenolic standards (methanolic standard solutions of individual phenolics, starting from pure

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compounds provided from Extrasynthese, Lyon, France), each phenolic compound identified was classified according to his phenolic class and sub-class, and then quantitative measurements were performed. Matairesinol and sesamin (for different types of lignans), ferulic acid (for hydroxycinnamic and other phenolic acids), cyanidin (for anthocyanins), catechin (flavanols), luteolin (flavones and other remaining flavonoids), resveratrol (stilbenes), pentadecylresorcinol or cardol (alkylphenols) and tyrosols (for tyrosols and other remaining phenolics) were used as representatives of their respective phenolic class. A linear fitting (not forced to origin and not weighed) was built for quantitative purposes, then

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ACCEPTED MANUSCRIPT expressing the abundance for each class as an equivalent of the reference compound within the same class.

2.4. Statistics and chemometrics analysis All experimental assays were performed in triplicate and the values were expressed as

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means ± standard deviation. The significance of differences at a 5% level between means was

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determined by one-way ANOVA using Duncan’s multiple range tests. The software PASW

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Statistics 23.0 (SPSS Inc.) was used to perform these analyses.

Interpretation of metabolomics data was formerly performed using Mass Profiler Professional

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B.12.06 (Agilent Technologies). Unsupervised hierarchical cluster analysis (‘Wards’ as linkage rule with Euclidean distance as the similarity measurement) was performed using a

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fold-change heat map. Latterly, the metabolomics-based dataset on sterols and polyphenols was exported into SIMCA 13 (Umetrics, Malmo, Sweden), UV scaled, and elaborated for

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orthogonal partial least squares discriminant analysis (OPLS-DA) supervised modeling. The

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variation between groups was evaluated considering both predictive and orthogonal components. The presence of outliers was also investigated according to Hotelling’s T2, using

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95% and 99% confidence limits for suspect and strong outliers, respectively. Cross-validation of the multivariate model was carried out to exclude overfitting, using CV-ANOVA (p <

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0.01) and permutation testing after inspecting model parameters (goodness-of-fit R2Y and goodness-of-prediction Q2Y, using a threshold of > 0.5 for the latter). Finally, variable importance in projection (VIP analysis) was used to evaluate the importance of sterolic and phenolic metabolites to discriminate EVOO samples according to the geographical origin, and to select those having the highest discrimination potential (VIP score > 1.2).

3. Results and discussion 8

ACCEPTED MANUSCRIPT 3.1 Phenolic and sterolic profile of different EVOO samples In this study, twenty-six EVOOs (twenty-one Tunisian and five Italian) were analyzed considering three replicates for each sample. The phenolic and sterolic profiles of EVOO extracts were investigated using an untargeted metabolomics approach based on UHPLCESI/QTOF-MS. Overall, this analytical approach allowed to annotate 957 compounds

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considering all EVOO samples; among them, 817 were classified as sterols whilst the

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remaining 140 compounds were polyphenols. The entire list of sterols and polyphenols

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identified across the different EVOO samples is provided as Supplementary material, together with annotations and composite MS spectra.

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The sterolic content of olive oil ranges from 1000 up to 3000 mg/kg (Matos et al., 2007). These levels vary according to the variety, the maturity of fruit and the geographical origin of

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the olives (Ben Temime et al., 2008). In this work, the total sterols content, expressed as cholesterol equivalents, of all oil samples were remarkably around 1000 mg/kg (Table 2), the

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minimum value established by the EU Regulation No 1348/2013 for the "extra virgin" olive

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oil category, and this is undoubtedly a good characteristic because of the great health benefits of these compounds. Overall, the mean content detected considering all EVOO samples was

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1666 mg/kg cholesterol equivalents, with the lowest value (p < 0.05) recorded in the Italian EVOO from Sicily (i.e. 1001.9 mg/kg), whilst the highest (p < 0.05) in the Tunisian EVOO

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from Beni Khedache – Médenine (cultivar Jemri), being 2478.6 mg/kg. The quantitative values of total sterols detected fitted with those reported in other works (Lerma-García et al., 2011; Lerma-García et al., 2009); however, the fact that sterols content is influenced by several factors, such as different climate, cultivar location and irrigation system, should be take into account when looking at these results (Lerma-García et al., 2011). Considering the sterols profile provided by UHPLC-ESI/QTOF mass spectrometry (Supplementary material), the most abundant compounds were isomeric forms of cholesterol derivatives, with 24-

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ACCEPTED MANUSCRIPT hydroxy-cholesterol very representative, resulting in a difference of over one order of magnitude when compared to the other sterols detected. Generally, the major sterol reported in literature for EVOO is -sitosterol (75% up to 90%) followed by other minor compounds such as -5-avenasterol, campesterol, stigmasterol and clerosterol (Cercaci et al., 2007). Other sterols are also present in trace amounts, namely: cholesterol, Δ-7-stigmasterol, Δ-7-

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avenasterol and campestanol (Boskou et al., 2006). Moreover, sterols in olive oil, particularly

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β-sitosterol and Δ-5-avenasterol, are endowed with antioxidant properties (Salvador, Aranda,

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Gómez-Alonso & Fregapane, 2003).

Regarding polyphenols, in our experimental conditions we annotated 140 compounds.

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Overall, flavonoids were the most frequent class of phenolics, with 44 compounds (20 flavones, 16 anthocyanins and 8 flavanols derivatives, as sum of flavonoids). Tyrosols

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equivalents were the second most frequent class with 32 compounds annotated, followed by phenolic acids (30 compounds, in particular 22 hydroxycinnamic acids), lignans (16

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compounds), alkylphenols (11 compounds) and stilbenes (7 compounds). The summed

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intensity of phenolic compounds detected within a given sub-class was used to gain a snapshot of phenolic distribution in the different EVOO samples, and then achieving semi-

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quantification according to methanolic standard solutions for phenolic sub-classes. Table 2 displays the semi-quantitative values for phenolic classes detected in the different EVOO

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samples. Overall, the phenolic distribution in all samples was very similar (p < 0.05), with a tyrosol equivalents content on average of 453 mg/kg and 412 mg/kg in Italian EVOO samples and in Tunisian EVOO samples, respectively. Tyrosol equivalents, in particular the simple phenol hydroxytyrosol, have been deeply studied in order to evaluate its prevention role against tumoral and cardiovascular diseases (Loubiri et al., 2016). Moreover, this phenolic compound is associated to a “health claim”, considering that the European Food Safety Authority has claimed that “the consumption of olive oil rich in polyphenols (hydroxytyrosol,

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ACCEPTED MANUSCRIPT 5 mg/day) contributes to the protection of oxidative damage to lipids in blood” (EFSA, 2011). Anyhow, the other phenolic classes analyzed showed much lower quantitative values, according to the phenolic composition of virgin olive oil samples (Visioli & Galli, 2002). Interestingly, the Italian EVOO sample from Sicily was characterized by low levels (p < 0.05) of several phenolic sub-classes, with catechin, luteolin, sesamin, and matairesinol equivalents

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being not detectable. These results on phenolic profile of EVOO samples were in accordance

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to previous works (Blasi et al., 2018; Loubiri et al., 2016) evaluating the change of phenolic

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profile in Italian EVOO samples after the addition of a carotenoid extract and the phenolic

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distribution in EVOO samples from autochthonous cultivars in Tunisia, respectively.

3.2 UHPLC-ESI/QTOF-MS discrimination of Tunisian and Italian EVOO samples

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Both the non-averaged unsupervised cluster analysis and the supervised orthogonal projection to latent structures discriminant analysis (OPLS-DA) multivariate statistical

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approaches allowed differentiating between Tunisian and Italian EVOO samples, suggesting

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that secondary metabolites, such as sterols and polyphenols, might be influenced by the geographic area (Figures 1 and 2). In particular, when looking the heat-map based on the fold-

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change analysis (Figure 1), three main clusters could be identified: the first group was represented by the Italian EVOO from Sicily, the second included the majority of Tunisian

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EVOO samples, whilst the third group consisted in the most of Italian EVOO samples. These findings indicated that the main differences between the various EVOO samples studied were actually represented in our dataset, suggesting the need for a detailed assessment regarding the compounds these differences could be attributed to. Multivariate analysis of metabolomics-based fingerprints is usually performed by applying supervised tools, such as partial least square discriminant analysis (PLS-DA) and OPLS-DA together with unsupervised methods (Worley and Powers, 2013). Unsupervised cluster

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ACCEPTED MANUSCRIPT analysis could be applied in order to reveal differences between classes without supervision, whilst the utilization of class membership in the PLS-DA allows a better separation between classes in scores space. However, the variation not directly correlated with Y is still present in the scores, complicating the interpretation of PLS-DA when the number of classes increase. On the contrary, OPLS-DA is a multivariate statistical approach able to effectively separate

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Y-predictive variation from Y-uncorrelated variation in X. In our experimental conditions, the

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model cross validation parameters of the OPLS-DA model were excellent, being R2Y = 0.98

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and Q2Y = 0.94, with adequate CV-ANOVA and permutation test cross-validation (Supplementary material), thus confirming the robustness of the model based on sterolic and

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phenolic fingerprints to discriminate EVOO samples according to geographic origin. Looking at the OPLS-DA score plot (Figure 2) a complete separation between Italian and Tunisian

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EVOO samples was achieved, with the EVOO sample from Sicily very distant from the origin of the model (representing a possible outlier in the OPLS-DA model). Indeed, the output of

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Hotelling’s T2 (Supplementary material) allowed to confirm that the EVOO sample from

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Sicily exceeded the 95% and 99% confidence limits for suspect and strong outliers respectively. Therefore, these preliminary results confirm that metabolomics analysis

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followed by a robust chemometric approach could be an efficient tool for studying olive oil traceability and authentication. Afterwards, the variables importance in projection of the

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OPLS-DA model (VIP analysis) were identified, considering the VIP scores for each phenolic and sterolic compound identified. In multivariate statistics, the VIP score is able to summarize the contribution a variable makes to the model, and it is calculated as a weighted sum of the squared correlations between the OPLS-DA components and the original variables. Table 3 displays the phenolics and sterols possessing the highest VIP score (> 1.2). Overall, 50 compounds (13 phenols and 37 sterols) identified could be considered those variables mostly contributing to class discrimination in the OPLS-DA supervised model. Intriguingly, the most

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ACCEPTED MANUSCRIPT abundant phenolic markers were hydroxybenzoic acids (galloyl glucose, gentisic acid, protocatechuic acid, and isomeric forms of dihydroxybenzoic acid), whilst among sterols an abundance of cholesterol and stigmasterols derivatives was observed (13 and 8 compounds, respectively). The results obtained in the present work sustained the use of multivariate chemometrics

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following untargeted detection of sterols and polyphenols as powerful tool to discriminate

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EVOO samples from different origin. At this regard, plant sterols have been deeply studied as

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markers for detecting oil adulteration or classifying olive oils according to fruit variety (Ben Temime et al., 2008). Therefore, these minor compounds could represent a parameter to be

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monitored in EVOOs for their characterization or to detect fraud related to adulteration. In particular, for certain food products such as EVOO, the provision of information on origin

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could have a positive and significant impact on the acceptability of the final product by the consumers (Caporale & Monteleone, 2001). Therefore, the synergistic use of instrumental

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analytical techniques and chemometrics represents at date the best way to obtain reliable

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results in the development of authenticity and traceability models (Bertacchini et al., 2012). Some studies have pointed out the ability to distinguish the EVOO samples according to

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geographical origin (different regions of the same country or different countries), using different analytical techniques and chemometric tools (Giacalone et al., 2015; Laroussi-

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Mezghani et al., 2015). Concerning the evaluation of Tunisian olive cultivars, several works can be found in literature, based on analytical techniques coupled with different multivariate statistical approaches (Abdallah et al., 2016; Loubiri et al., 2016; Hassine et al., 2015). Metabolomics-based analyses have been successfully applied in order to evaluate, among other things, the authenticity of a variety of plant-based foods such as fruit juices (Vardin et al., 2008), tomato (Lucini et al., 2017), vanilla (Busconi et al., 2017), and goji berries (Rocchetti et al., 2018).

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ACCEPTED MANUSCRIPT With this regard, in this work the identification of phenolic and sterolic markers in Tunisian and Italian EVOO samples has been used to discriminate samples according to their geographic origin. The analysis of the unsaponifiable fraction of oil is largely considered a detection tool for food adulteration (Garcia, Martins, & Cabrita, 2013). Furthermore, according to Bajoub and co-workers (2016), the development of metabolomic profiling

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approaches for the phenolic detection in monovarietal virgin olive oils could help to better

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understand the olive oil properties, also promoting their use for traceability purposes. Another

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important aspect that should be taken into account is that olive oil is usually marketed as a mixture from different cultivars and origins (Aued-Pimentel, da Silva, Takemoto, & Cano,

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2013). Therefore, a metabolomics-based approach for markers detection could help to better discriminate the overall EVOO quality. Some results in literature reporting that in addition to

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secondary metabolites, other markers (such as DNA markers) have been successfully used to identify single cultivar olive oils (Vietina et al., 2011; Bazakos et al., 2016) or blended olive

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oils (Pasqualone al., 2007). However, some olive oils are mixtures of high-and-low quality

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oils, then commercialized as high-quality products. This is why a well-documented traceability system has become a requirement for quality control in the olive oil chain, and

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metabolomics followed by multivariate statistics could be exploited for these purposes. The combined use of sterols and phenolics is a promising approach, likely because these two

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classes have different biosynthetic pathways and therefore they might be differentially affected by pedo-climatic factors. Nonetheless, it is important to point out that other dynamics such as post-harvest factors and processing technologies might affect both phenolic and sterol profile. Therefore, further studies are needed to strengthen the use of this analytical approach in the olive oil geographic traceability field.

4. Conclusion

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ACCEPTED MANUSCRIPT The recognition of EVOO authenticity is an important issue in terms of commercial, economic, and quality aspects. Although several approaches have been suggested in literature, the comprehensive untargeted profiling of sterols and polyphenols coupled to multivariate chemometrics appears to be a very promising tool to discriminate different EVOO samples from different geographical origin (Tunisian vs Italian). Furthermore, this untargeted

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approach could be also useful to characterize olive oil extracted by conventional and non-

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conventional processes (Koubaa et al., 2016), although further studies are strongly

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recommended. The application of UHPLC-ESI/QTOF mass spectrometry followed by multivariate statistics, i.e. unsupervised hierarchical cluster analysis and supervised approach

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such as OPLS-DA, allowed to quantify and further identified those polyphenols and sterols makers better contributing in discrimination according to the different geographical origin of

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EVOO samples. These discriminating markers were mainly ascribed to cholesterol derivatives and hydroxybenzoic acids. Although further research is still needed, these results support the

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use of untargeted fingerprints in the food traceability and authentication domain.

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ACCEPTED MANUSCRIPT Giacalone, R., Giuliano, S., Gulotta, E., Monfreda, M., & Presti, G. (2015). Origin assessment of EV olive oils by esterified sterols analysis. Food Chemistry, 188, 279-285. Granato, D., Sávio Nunes, D., & Barba, F. J. (2017). An integrated strategy between food chemistry, biology, nutrition, pharmacology, and statistics in the development of functional

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(2007). Biochemical characterisation of some Tunisia virgin olive oils obtained from different cultivars growing in Sfax National Collection. Journal of Food, Agriculture and

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ACCEPTED MANUSCRIPT Lerma-García, M. J., Concha-Herrera, V., Herrero-Martínez, J. M., & Simó-Alfonso, E. F. (2009). Classification of extra virgin olive oils produced at La Comunitat Valenciana according to their genetic variety using sterol profiles established by high performance liquid chromatography with mass spectrometry detection. Journal of Agricultural and

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Food Chemistry, 57, 10512-10517. Lerma-García, M. J., Simó-Alfonso, E. F., Méndez, A., Lliberia, J. L., & Herrero-Martínez, J.

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from autochthonous and introduced cultivars in Tunisa. European Food Research and

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ACCEPTED MANUSCRIPT Matos, L. C., Cunha, S.C., Amaral, J. S., Pereira, J. A., Andrade, P. B., Seabra, R. M., et al. (2007). Chemometric characterization of three varietal olive oils (Cvs. Cobrancosa, Madural and Verdeal Transmontana) extracted from olives with different maturation indices. Food Chemistry, 102, 406-414.

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Monfreda, M., Gobbi, L., & Grippa, A. (2012). Blends of olive oil and sunflower oil: characterisation and olive oil quantification using fatty acid composition and chemometric

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analytical procedure for the extraction and quantification of lutein from tomato byproducts by HPLC-DAD. Food Analytical Methods, 5, 710-715.

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Pasqualone, A., Montemurro, C., Summo, C., Sabetta, W., Caponio, F., and Blanco, A. (2007). Effectiveness of microsatellite DNA markers in checking the identity of Protected

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Designation of Origin extra virgin olive oil. Journal of Agricultural and Food Chemistry, 55, 3857-3862.

Puértolas, E., Koubaa, M., & Barba, F. J. (2016). An overview of the impact of electrotechnologies for the recovery of oil and high-value compounds from vegetable oil industry: Energy and economic cost implications. Food Research International, 80, 19-26.

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ACCEPTED MANUSCRIPT Putnik, P., Barba, F. J., Spanic, I., Zorić, Z., Dragović-Uzelac, V., & Bursać Kovačević, B. (2017). Green extraction approach for the recovery of polephenols from Croatian olive leaves (Olea europea). Food and Bioproducts Processing, 106, 19-28. Rocchetti, G., Lucini, L., Chiodelli, G., Giuberti, G., Montesano, D., Masoero, F., & Trevisan,

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M. (2017). Impact of boiling on free and bound phenolic profile and antioxidant activity of commercial gluten-free pasta. Food Research International, 100, 69-77.

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Trevisan, M., & Lucini, L. (2018). UHPLC-ESI-QTOF-MS profile of polyphenols in Goji

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berries (Lycium barbarum L.) and its dynamics during in vitro gastrointestinal digestion and fermentation. Journal of Functional Foods, 40, 564-572.

MA

Roselló-Soto, E., Koubaa, M., Moubarik, A., Lopes, R. P., Saraiva, J. A., Boussetta, N., Grimi, N., & Barba, F. J. (2015). Emerging opportunities for the effective valorization of

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wastes and by-products generated during olive oil production process: Non-conventional

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methods for the recovery of high-added value compounds. Trends in Food Science & Technology, 45(2), 296-310.

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Salvador, M. D., Aranda, F., Gomez-Alonso, S., & Fregapane, G. (2003). Influence of

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extraction system, production year and area on Cornicabra virgin olive oil: A study of five crop seasons. Food Chemistry, 80, 359-366. Tunisian Agriculture Ministry – National Office of Oil, 2016. Available online: http://www.onh.com.tn. Vardin, H., Tay, A., Ozen, B., & Mauer, L. (2008). Authentication of pomegranate concentrate using FTIR spectroscopy and chemometrics. Food Chemistry, 108, 742-748.

22

ACCEPTED MANUSCRIPT Vietina, M., Agrimonti, C., Bonas, U., Marmiroli, M., & Marmiroli, N. (2011). Application of SSR markers to the traceability of monovarietal olive oils. Journal of the Science of Food and Agriculture, 104, 61-676. Vinceković, M., Viskić, M., Jurić, S., Giacometti, J., Bursać Kovačevic, D., Putnik, P., Donsì,

PT

F., Barba, F. J., & Rezek Kambrak, A. (2017). Trends in Food Science & Technology, 69, 1-12.

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Reviews in Food Science and Nutrition, 42(3), 209-221.

RI

Visioli, F., & Galli, C. (2002). Biological properties of olive oil phytochemicals. Critical

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Worley, B., & Powers, R. (2013). Multivariate analysis in metabolomics. Current

MA

Metabolomics, 1(1), 92-107.

D

Funding

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This research did not receive any specific grant from any funding agency in the public,

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commercial, or not-for-profit sectors.

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Acknowledgements

GR was recipient of a fellowship from the Doctoral School on the Agro-Food System (AgriSystem) of the Università Cattolica del Sacro Cuore (Piacenza, Italy).

23

ACCEPTED MANUSCRIPT Table Captions Table 1. Details regarding the origin of EVOO samples from Tunisia (1-21) and Italy (22-26). a

= Tunisian autochthonous cultivars (monovarietal olive oils), Italian autochthonous cultivars

(olive oil blends).

b

= Quality level according to European Regulations No 1348/2013, and

PT

based on the quality physicochemical contents determined in this study (data not shown): EVOO extra virgin olive oil (simultaneously: Acidity < 0.8%; Peroxide index < 20 mEq

RI

O2/kg; K232 ≤ 2.5; K270 ≤ 0.22 and ΔK < 0.01). PDO = protected designations of origin; PGI =

SC

protected geographical indication.

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Table 2. Total sterol content together with quantification per classes of polyphenols identified from UHPLC-ESI-QTOF-MS in different EVOO samples. Results for total sterols are

MA

expressed as mg kg-1 cholesterol equivalents, whilst those for phenolics are expressed in mg kg-1 as cyanidin, catechin, luteolin, tyrosol, cardol, ferulate, sesamin, matairesinol, and

D

resveratrol equivalents. The data are presented as mean values  standard deviation (n = 3);

PT E

superscript letters within each column indicate homogenous sub-classes as resulted from ANOVA (p < 0.05, Duncan’s post hoc test).

CE

Table 3. Discriminant phenolic and sterolic compounds identified by VIP (Variable Importance in Projection) analysis following OPLS-DA discriminant analysis of different

AC

EVOO samples (Tunisian and Italian). Compounds are provided together with VIP scores (measure of variable’s importance in the OPLS-DA model).

Figure Captions Figure 1. Non-averaged unsupervised cluster analysis on the phenolic and sterolic profile of different EVOO samples from Tunisia and Italy (similarity: Euclidean; linkage rule: Ward).

24

ACCEPTED MANUSCRIPT Compound’s intensity was used to build up heat map, on the basis of which the clusters were generated. Figure 2. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) on EVOO samples phenolic and sterolic profile according to their geographical origin. Individual

RI

PT

replications are given in the class prediction model score plot.

SC

Supplementary material

Supplementary Table 1. Whole dataset of identified phenolic and sterolic compounds,

MA

spectrum (masses and their abundances).

NU

together with abundances, annotations (identification scores and raw formulas) and composite

Supplementary Figure 1. Validation of OPLS-DA discriminant model on phenolic and

D

sterolic profile as a function of geographical origin; Hotelling’s T2 using 95% and 99%

AC

CE

given in the lower one.

PT E

confidence limits is given in upper pane, whereas outcome of permutation test (N = 100) is

25

ACCEPTED MANUSCRIPT Sample Origin

Cultivara

Collection site

Quality levelb

1

Tunisia El Gorthab - Tataouine

Dokhar el Gorthab

EVOO

2

Tunisia El Mai - Djerba

Chemlali Djerba

EVOO

3

Tunisia Dakhla

Boughrara

- Neb Jmal

EVOO

PT

Médenine Tunisia Douiret - Tataouine

Chemlali Douiret

5

Tunisia Douiret - Tataouine

Fakhari

6

Tunisia Chammek - Zarzis

Zarrazi Zarzis

EVOO

7

Tunisia Chammek - Zarzis

Zalmati Zarzis

EVOO

8

Tunisia Mareth - Gabès

Chemlali Abyath

EVOO

9

Tunisia Ben Guerdane

10

Tunisia Zerkine Mareth - Gabès

11

Tunisia Douiret - Tataouine

12

MA

NU

SC

RI

4

EVOO EVOO

EVOO

Zarrazi injassi

EVOO

Chemlali Ontha

EVOO

Tunisia Chammek - Zarzis

Chemlali Zarzis

EVOO

13

Tunisia Toujene Matmata - Gabès

Toffehi

EVOO

14

Tunisia Toujene Matmata - Gabès

Fougi

EVOO

15

Tunisia Toujene Matmata - Gabès

Gousalani

EVOO

16

Tunisia Toujene Matmata - Gabès

Nourgou

EVOO

17

Tunisia Beni khdech - Médenine

Jemri Bouchouka

EVOO

18

Tunisia Zerkine Mareth - Gabès

Toffehi Mareth

EVOO

19

Tunisia Zraoua Matmata - Gabès

Zarrazi injassi Matmata

EVOO

20

Tunisia Beni

AC

CE

PT E

D

Jemri Ben Guerdane

Khedache

- Jemri Beni Khedache

EVOO

26

ACCEPTED MANUSCRIPT Médenine Tunisia Toujene Matmata - Gabès

Jemri Bouchouka Matmata

EVOO

22

Italy

Tuscany (PGI)

Frantoio - Moraiolo - Leccino

EVOO

23

Italy

Apulia (PDO)

Coratina

EVOO

24

Italy

Liguria (PDO)

Taggiasca - Razzola

EVOO

25

Italy

Umbria (PDO)

Moraiolo - Frantoio - Leccino

26

Italy

Sicily (PDO)

Biancolilla - Nocellara del EVOO

EVOO

RI

PT

21

SC

Belice

AC

CE

PT E

D

MA

NU

Table 1

27

ACCEPTED MANUSCRIPT Cate chin Eq. 2.1  0.0abc

Lute olin Eq. 2.3  0.4abc

d

d

abcde

2

Tunis ia

2037. 8 423.1

Tunis ia

9.9  0.8g

12.7  2.1lm

9.5  0.9n

Tunis ia

2046. 3.5  0.5ab 5 cd 58.9

0.8  0.6ab

cde

2393. 7 121.7

Tunis ia

7.4  2.9ef 5.9  0.5cde

5.1  5.1  3.0cde 1.5fgh fgh

ilm

Tunis ia

cde

1893. 7 203.2

3.6  1.2ab

abc

Tunis ia

1411. 5 198.5 abc

Tunis ia

1595. 7 350.6

AC

9

9.8  1.2g

CE

8

1.5  0.2a

D

1286. 3 146.3

1.0  0.3ab

PT E

Tunis ia

6.3  0.8cde

1.2  0.1ab

1.5  0.2ab

Tunis ia

2282. 9 534.9

Tunis ia

1020. 3 37.6a

bcde

b

81.6  50.9a

10. 5 1.4a

4.5  437.4 11. 0.0cde  1 fghil c 69.6 2.8

1.8  2.2  291.5 10. 0.2abc 1.1abc  1 d b 74.8 2.8a cd

3.2  0.6ab

6.2  3.3efg

4.9  1.2efg

hi

hilm

b

9.3  1.3fg

6.8  2.3fgh i

Tunis ia

2032. 2

6.7  1.4de

bc

172.6 10. 1  a 90.7 5.4a

c

3.9  4.1  3.9  0.6bc 0.0def 0.1abcd d

1.6  0.3bcdef

g

8.2  1.8  1.7  2.6ef 0.0ab 0.0a ghi

10.5  0.8gh

3.0  0.7ghi

cd

3.4  9.6  0.5cd 1.9ghilm

3.8  0.9ilm

efg

i

3.9  4.4  4.6  0.6bc 1.2def 1.4abcde d

4.3  1.2lm

gh

2.0  2.7  5.0  0.7ab 1.0bc 1.4abcde

2.9  1.2ghi

def

0.8  1.2  2.5  0.0ab 0.0ab 0.2ab

0.7  0.2ab

c

2.1  3.7  3.8  0.4ab 0.9cd 0.3abcd

1.1  0.4abcd

efg

3.3  6.1  0.8abc 1.6hil

14.2 4.6  3.0  0.1efg 0.4abc  lm h 5.9

4.0  1.2ilm

n

2.6  2.8  6.5  0.3ab 0.0bc 2.6cdefgh c

2.0  0.8defg

def

bc

3.9  480.4 10. 1.2bcd  5 efghi 134.3 1.9a defg

12

Resver atrol Eq. 0.7  0.1ab

defg

def

11

Mataire sinol Eq. 2.7  0.5abc

bc

abcde

10

Sesa min Eq. 0.3  0.0ab

cd

2.9  3.9  357.2 9.3 0.4abc 0.1bcd   de efghi 125.9 0.9a

bcdef

7

bcd

359.9 13. 9  b 53.4 3.7b

MA

ef

6

b

4.5  3.3  408.1 12. 1.3bcd 0.3bcd  5 efg efg 143.4 0.3a cdefg

2318. 8 357.7

Feru late Eq. 2.3  0.1ab

bc

2.0  339.4 9.0 0.5abc   b 94.0 0.4a

f

5

366.7 11. 3  151.5 3.4a bcdef

ef

4

Car dol Eq. 7.2  3.0a

c

cdef

3

Tyros ol Eq. 268.1  57.2b

PT

Tunis ia

Cyan idin Eq. 2.4  0.2ab

RI

1

Total sterol s 1528. 3 171.6

SC

Origi n

NU

Sam ple

7.3  4.6  4.5  2.3de 0.2efg 0.1abcde fg

0.5  0.0ab

h

bc

298.2 8.8  

2.3  3.4  2.3  0.1ab 0.0cd 0.4ab

0.9  0.2

28

ACCEPTED MANUSCRIPT

2023. 4 428.3

4.6  2.5  4.0  531.7 10. 0.3bcd 2.0abc 1.3cde  9 de fghil 161.9 0.9a

cdef

14

Tunis ia

1245. 5 274.5

efgh

Tunis ia

1931. 4 251.4

13.1  1.1h

9.7  0.9il

4.7  1.3def ghil

bcdef

16

Tunis ia

1036. 4 147.8

3.5  0.2ab

17

Tunis ia

7.6  0.6ef

9.8  5.3il

6.2  2.9hil m

abcde

Tunis ia

2243. 9 593.1

15.5  0.9m

def

10.8  2.0no

efg

11.7  0.4il

4.7  1.6  1.4efg 0.9a

10.0  0.3fg

cdefg

hi

bcd

3.3  0.6hi

h

8.4  4.2  1.6i 1.4abcde

6.6  2.5  5.5  0.8de 0.3ab 1.3abcdef f

1.1  0.7abcd 1.9  0.9cdefg 2.4  0.3efgh

cde

b

747.3 11. 6  166.9 1.9a i

7.3  1.7ef

ghi

422.2 12. 4  124.2 3.8a

11.3 6.0  12.7  0.0ghi 5.1m  hil 5.0

2.5  0.8fgh

6.9  11.4  0.5hi 5.8lm

5.0  0.4m

bc

713.2 16. 34  97.9hi  7.7c

MA

18

8.9  3.7  7.4  0.5ef 0.2cd 0.9defghi

m

hi

1550. 0 332.3

efg

bcde

1.6  3.2  568.3 9.5 1.2abc 0.9bcd   ef 24.4fg 1.9a

a

c

bc

19.3 2.9  3.8  357.1 6.3  3.9i 0.7abc 1.4bcd   de efgh 107.5 1.4a

abc

15

b

SC

Tunis ia

1.7a

cd

NU

13

47.1b

PT

m

cdef

RI

def

500.8

17.4  3.0n

Tunis ia

1424. 1 166.5 abc

Tunis ia

2478. 6 562.9 f

Tunis ia

1030. 2 152.8

AC

21

2.5  2.6  353.8 11. 0.2abc 0.2bcd  5 de e 119.6 1.7a

18.5 5.8   0.8i 2.4def

CE

20

1.7  0.5a

PT E

19

D

d

gh

Italy (Tusc any)

1492. 6 546.5

Italy (Apul ia)

1296. 4 115.1

2.9  0.0ab

8.0  2.7ghi

5.8  1.6ghi lm

Italy (Ligu

1206. 3

14.8  0.6m

8.4  4.9  6.1i 1.3abcde

hi

n

4.6  8.6  0.4bcd 0.2hi

6.6  0.8lm

4.8  7.9  0.7bcd 2.8ghi

7.2  1.0m

5.8  2.0  7.0  0.2cd 0.1ab 0.6defgh e

4.4  0.2lm 1.5  0.5bcdef

cde

bc

472.2 11. 4  23.4c 5.9a

7.9  3.3  10.8  0.6ef 0.2cd 3.2ilm gh

ef

600.6 12. 4  50.6g 2.8a

11.8  1.3il

4.7  8.9  0.4efg 0.7fghil

hi

m

defg

abc

24

696.0 18. 4  124.5 7.4d

cdefg

abcd

23

ef

1.2  0.4abcde

bc

4.7  2.5  3.2  406.1 10. 0.5bcd 0.2abc 0.3bcd  7 de ef 134.1 2.1a

a

22

fg

bcde

12.5  2.3o

7.5  2.9  6.0  1.4de 0.4cd 0.4bcdefg

1.0  0.1abcd

bc

bcd

435.2 6.9  

1.0  0.2abcd

h

9.8  4.6  7.8  0.3fg 0.7efg 0.7efghil

1.0  0.4abcd

29

ACCEPTED MANUSCRIPT ria) 25

Italy (Umb ria)

175.9

20.1c

ab

defg

1502. 9 335.9

3.1  0.6ab

8.6  3.3hi

6.4  2.2ilm

abcd

26

Italy (Sicil y)

1001. 9 51.2a

4.3  0.9bc

nda

nda

0.9a

hi

h

416.8 10. 2  169.6 0.5a

10.8  1.7gh

5.2  10.3  0.8fg 2.8hilm

cdefg

il

bc

342.9 8.2   20.3b 1.5a cde

0.9  0.2abcd

h

a 0.3  nd a 0.0

1.9  0.4a

nda

b

AC

CE

PT E

D

MA

NU

SC

RI

PT

Table 2

30

ACCEPTED MANUSCRIPT Class

Subclass

Compound

VIP score

Flavonoids

Flavanols

(-)-Epigallocatechin

1.2098

(+)-Gallocatechin

1.2098

Flavanones

Eriodictyol

1.2099

Hydroxybenzoic acids

Galloyl glucose

1.2516

Gallic acid 4-O-glucoside

1.2516

2,4/3,5/2,3/2,6-Dihydroxybenzoic

1.3989

Phenolic

acid

RI

Gentisic acid

PT

acids

Other

Hydroxybenzaldehydes

SC

Protocatechuic acid Gallic aldehyde

Stibenes

-

Sterols

Stigmasterols and C24

D PT E CE AC

Cholesterol and derivatives

1.3990 1.3990

Piceatannol 3-O-glucoside

1.2947

Poriferast-8(14)-en-3beta-ol

1.4063

MA

ethyl derivatives

NU

polyphenols

1.3989

Fucostanol

1.4063

Sitosterol

1.4063

28-methylxestosterol

1.3382

24-ethyl-23,26-dimethylcholest-

1.3382

5,24E(28)-dien-3beta-ol (25Z)-26-methylstrongylosterol

1.3382

(25E)-26-methylstrongylosterol

1.3189

Sutinasterol

1.3189

Dormatinone

1.3344

DHCEO

1.3344

7-OOH-5,8-dien-3β-ol

1.3344

20S-Hydroxycholestane-3,16-

1.3344

dione EnP(5,8)

1.3344

2,22,25-trideoxyecdysone

1.3344

5α,6α-24S,25-diepoxy-cholesterol

1.3344

31

ACCEPTED MANUSCRIPT 7-oxo-cholestenone

1.3001

Hipposterol

1.2940

5α,6β-dihydroxycholestanol

1.2939

Zymosterol intermediate 1b

1.2285

4alpha-Formyl-4-

1.2284

methylzymosterol 2-deoxy-20-hydroxy-5α-ecdysone

Ergosterols and C24-methyl

PT

3-acetate

Isoxestospongesterol

RI

derivatives

SC

25-methylxestosterol

D

PT E

Cycloartanols and

1.3382

1.3382 1.3382

Xestospongesterol

1.3382

NU

Sipalosterol

Chabrosterol

1.3001

24-keto-25dehydrocholesterol

1.3001

Tigogenin

1.3344

Sarsasapogenenin

1.3344

Neotigogenin

1.3344

(25R)-5β-spirostan-3β-ol

1.3344

24-methylene-cycloartanol

1.3382

(24R)-24-methylcycloart-25-en-

1.3382

MA

Spirostanols and derivatives

1.2186

CE

derivatives

3β-ol 24-Ethyl-24-methyl-22E-

derivatives

dehydrocholesterol

AC

C24-propyl sterols and

25-Methyl-24-isopropenyl-

1.3754

1.3382

cholesterol

Bufanolides and derivatives

Scillirosidin

1.3325

Steryl esters

12:0 Cholesteryl ester

1.3370

Table 3

32

ACCEPTED MANUSCRIPT

Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints

Mbarka Ben Mohamed, Gabriele Rocchetti, Domenico Montesano, Sihem Ben Ali,

SC

RI

PT

Ferdaous Guasmi, Naziha Grati-Kamoun, Luigi Lucini

MA

NU

HIGHLIGHTS

An investigation of sterolic and phenolic profile in EVOO samples was achieved.



All EVOO samples were abundant in total sterols and tyrosol equivalents.



Multivariate statistics discriminated EVOOs according to their geographic origin.



Cholesterol derivatives were the best markers related to geographic origin.

AC

CE

PT E

D



33

Graphics Abstract

Figure 1

Figure 2