Food Chemistry 135 (2012) 2561–2568

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Food Chemistry

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Analytical Methods

Influence of harvest year, cultivar and geographical origin on Greek extra virginolive oils composition: A study by NMR spectroscopy and biometric analysis

Alexia Agiomyrgianaki a, Panos V. Petrakis b,⇑, Photis Dais a,⇑a NMR Laboratory, Department of Chemistry, University of Crete, P.O. Box 2208, Voutes Campus, 710 03 Heraklion, Crete, Greeceb National Agricultural Research Foundation, Institute of Mediterranean Forest Research, Laboratory of Entomology, Terma Alkmanos, Ilissia, 115 28 Athens, Greece

a r t i c l e i n f o a b s t r a c t

Article history:Received 15 November 2010Received in revised form 12 August 2011Accepted 3 July 2012Available online 20 July 2012

Keywords:NMROlive oilCultivarChemometricsMonophyly

0308-8146/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.foodchem.2012.07.050

⇑ Corresponding authors. Tel. +302810 545037; faxtel.: +30 2107784763; fax: +30 2107784602 (P.V. Pet

E-mail addresses: pvpetrakis@fria.gr (P.V. PetrakiDais).

Two hundred twenty-one extra virgin olive oils (EVOO) were extracted from four olive mono-cultivars(Koroneiki, Tsounati, Adramitini, and Throubolia) originated from four divisions of Greece (Peloponnesus,Crete, Zakynthos, and Lesvos) and collected in five harvesting periods (2002–2006 and 2007–2008). Allsamples were chemically analysed by means of 1H and 31P NMR spectroscopy and characterised accord-ing to their content in fatty acids, phenolic compounds, diacylglycerols, total free sterols, free acidity, andiodine number. The influence of cultivars on the compositional data of the EVOO samples according toharvest year and geographical origin was examined by means of the forward stepwise canonical discrim-inant analysis (CDA) and classification binary trees (CBT). The CDA, when the a priori grouping was inaccordance with harvest, was high (94%), whereas the classification in terms of groups formed by inclu-sions of geographical origin was reduced to 85%. Inclusion of both the harvesting year and geographicalorigin in the CDA analysis resulted in a high classification (90%) for the EVOO samples grouped into thefour cultivars. The variables that most satisfactorily classified the Greek olive oils were the phenolics p-coumaric acid, pinoresinol, 1-acetoxypinoresinol, syringaresinol, luteolin, apigenin, and the hydrolysisproducts of oleuropein expressed collectively by the concentration of total hydroxytyrosol. Amongstthe fatty acids, linoleic acid was the predictor with the highest discriminatory power. Finally, the phylo-genetic significance of the olive oil compounds as determined by NMR was investigated by estimatingtheir support to monophyly of cultivars.

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1. Introduction

Determining phenolic compounds that may explain the varia-tions in olive oil characteristics is a major concern amongst oliveoil chemists. The identity and amount of these oil constituentsare influenced by several factors including cultivar, olive fruitmaturity stage (degree of ripening), harvesting time, agronomicpractices, geographic location, and extraction processes. Maintain-ing some of these variables, the phenolic profile obtained by high-performance liquid chromatography allowed the discrimination ofdifferent olive varieties (Gomez-Alonso, Salvador, & Fregapane,2002; Kalua, Allen, Bergood, Bishop, & Prenzler, 2005; Lerma-Garcia, Herrero-Martinez, Ramis-Ramos, & Simo-Alfonso, 2008;Japon-Luján, Ruiz-Jiménez, & Luque de Castro, 2006; Ocakoglu,Tokatli, Ozen, & Korel, 2009).

Amongst the various phenolic compounds, the secoiridoids3,4-DHPA-AC, p-HPEA-EDA, ligstroside aglycon and the lignan (+)

ll rights reserved.

: +302810 545001 (P. Dais);rakis).s), dais@chemistry.uoc.gr (P.

1-acetocy pinoresinol extracted from olive oils were found to bebetter varietal markers than other phenolic compounds (Kaluaet al., 2005; Vinha et al., 2005). On the other hand, oleic, linoleicand palmitic acids were found to be important predictors for theEVOO characterisation (Aranda, Gomez-Alonso, Rivera del Alamo,Salvador, & Fregapane, 2004; Lerma-Garcia et al., 2008; Manninaet al., 2003; Montealegre, Alegre, & Garcia-Ruiz, 2010; Ollivier, Ar-taud, Pinatel, Durbec, & Guérére, 2006).

To the best of our knowledge, two publications by the sameauthors (Stephanoudaki, Kotsifaki, & Koutsaftakis, 1997; Stepha-noudaki, Kotsifaki, & Koutsaftakis, 1999) reported the classificationof olive cultivars in Greece based on the triacylglycerol and fattyacid profiles. However, this investigation involved only two differ-ent cultivars (koroneiki, tsounati) originated from a rather narrowzone in the west side region of Chania (Crete). The aim of the pres-ent study is to analyse olive oil samples extracted from four majorolive oil mono-cultivars, namely koroneiki, tsounati, adramitini,and throubolia. Except olives of throubolia originated from Creteand collected during the harvest period 2007–2008, the remainingthree cultivars cover different regions of Greece and harvest peri-ods. PDO olive oil samples were obtained from Peloponnesus andCrete, whereas PGI samples were collected from Zakynthos and

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Lesvos. Chemical analysis was performed by 31P and 1H NMR spec-troscopy. Classification was achieved by applying to olive oils com-positional data the forward stepwise canonical discriminantanalysis (CDA) and the classification binary trees (CBT). Apart fromour main task concerning the varietal classification of olive oils, wewere also interested in assessing the influence of the Greek culti-vars on the compositional parameters with respect to variables,such as harvest period, areas and sites of olive oils production.Therefore, these variables will be included separately in the statis-tical analysis. Finally, the phylogenetic significance of the variousolive oil chemical compounds to the monophyly of cultivars willbe investigated.

2. Materials and methods

2.1. Materials

A total of 221 olive oil samples were obtained from different re-gions of Greece; 119 samples from Crete (23 from Sitia, 13 fromHeraklion, 57 from Chania, and 26 from Rethymnon), 68 sampleswere collected from Peloponnesus (30 from Messinia and 38 fromLakonia), and 13 and 21 samples from the islands of Lesvos andZakynthos, respectively. The oils were extracted from four differentolive mono-cultivars; 163 oils from KORONEIKI, 40 oils from TSOU-NATI, 13 oils from ADRAMITINI, and 5 oils from THROUBOLIA. Theolive oil samples were provided by the local cooperatives and pro-duced by the same method of extraction (centrifugation). The olivefruits were collected during five harvesting periods 2002/2003 to2005/2006, and 2007/2008. Provision was made to handpick theolive fruits at the same time period in each location (mid Novem-ber to mid December) to maintain as much as possible the samematurity level. Only healthy fruits, without any kind of infectionor physical damages, were used. The oils were extracted within48 h after harvesting, stored immediately at 4 �C in dark bottles,and analysed twice within one month from the production date.All the Greek olive oils were extra virgin according to certain offi-cial analytical methods and limits (free acidity 6 0.8% in oleic acid,K232 6 2.50, K270 6 0.20, DK 6 0.01, total sterols P 1000 mg/kg)(European Communities, 1991).

Our concern was to collect a large number of samples to main-tain an adequate data base for subsequent multivariate statisticalanalysis, despite the fact that the samples are characterised by aheterogeneous distribution of cultivars, geographic area and cropseason. The heterogeneous set of samples is justified by the factthat koroneiki cv. is the most widely planted olive tree in Greeceand exists in almost all sampled geographical regions, particularlyin Crete, Peloponnesus and Zakynthos. As it will be discussed be-low, the chemometric methods adopted in this study to analysethe compositional data absorb the sample heterogeneity.

2.2. Chemicals

Pinacol, phosphorus trichloride, protonated solvents (reagent oranalytical grade), and deuterated solvents used in the presentstudy were purchased from Sigma–Aldrich (Athens, Greece). Thederivatizing phosphorous reagent (2-chloro-4,4,5,5-tetramethyldi-oxaphospholane was synthesised from pinacol and phosphorus tri-chloride following the method described in the literature(Zwierzak, 1967). However, to increase the yield of the reaction,we utilised hexane solvent instead of benzene and pyridine insteadof triethylamine suggested in the original method. This modifica-tion resulted in �45% yield of the product against 19% obtainedwith the original method. The modified method was describedelsewhere (Hatzakis, Dagounakis, Agiomyrgianaki, & Dais, 2010).

2.3. Extraction of minor polar components from olive oil

Phenolic compounds were extracted following the methoddeveloped by Montendoro and coworkers (Montedoro, Servili,Baldioli, Selvaggini, & Miniati, 1992) using 35 g of olive oil and amixture of methanol–water (80:20, v/v). The polyphenol extractswere used immediately for sample preparation and 31P NMRmeasurements.

2.4. Sample preparation for 31P and 1H NMR spectroscopic analysis

The sample preparation for 31P and 1H NMR experiments hasbeen described in details elsewhere (Fronimaki, Spyros, Christo-phoridou, & Dais, 2002; Petrakis, Agiomyrgianaki, Christophoridou,Spyros, & Dais, 2008).

2.5. NMR experiments

31P and 1H NMR experiments were conducted on a BrukerAMX500 spectrometer operating at 500.1 and 202.2 MHz for pro-ton and phosphorus-31 nuclei, respectively, at 26 ± 1 �C. Detailsfor the execution of the NMR experiments have been given in ear-lier reports (Fronimaki et al., 2002; Petrakis et al., 2008).

2.6. Biostatistical and phylogenetic methods

The phylogenetic significance of compounds was investigatedby estimating their support to monophyly of cultivars (monophy-letic groups are those descended from a single ancestral taxonand derived with simple evolutionary lineages). In this type ofanalysis three independent compatibility support measures areusually employed because in this way the support is estimatedwithout regard to the evolutionary history of the cultivar. The Mea-cham’s monophyly support (Meacham, 1994) is the first measureand Wilkinson’s (Wilkinson, 1998) is the second and stricter one.The algorithms in the program MEAWILK (Estabrook, & Frolich,2000) were used for the calculations. For each hypothesis of mono-phyly the exact probability that a random replacement of a certaincompound would support this hypothesis is calculated. The result-ing measure is the ratio (expressed as a percentage) of all cumula-tive weights of the compounds that support monophyly to theweights of all compounds.

The separation of cultivars demands for the discriminatorypower of the olive oil compositional parameters. On these lines,forward stepwise Canonical Discriminant was used to achievethe most discriminative variables for the arrangement of samplesin a space of reduced dimensionality in a way that maximisesthe distances between the a priori formed groups and the indepen-dence of the axes of the configuration (Petrakis et al., 2008). TheEVOO chemical compounds contributing most to the discrimina-tion of groups are shown by means of the F-value used as a crite-rion for inclusion or removal of the compound in a forwardstepwise CDA mode. Wilks’ k and the associated F approximationwere used to check the significance and estimate the importanceof each compound in CDA analysis. Also, CDA produces classifica-tion tables to present the classificatory efficiency of the method.On the basis of the resulting chemical profile, the Mahalanobis dis-tances were calculated in estimating the misclassification proba-bilities of EVOO samples in the predefined groups. Several pairsof CDA functions were used for the varietal groupings accordingto harvest years, and the two hierarchical levels, namely geograph-ical units and individual sites within each unit area. In both levels,the performance of CDA was measured by the percentage of totalvariance explained by all significant discriminant axes.

In order to produce a set of simple rules in the form of inequal-ities, which will identify the origin of any new sample we used

A. Agiomyrgianaki et al. / Food Chemistry 135 (2012) 2561–2568 2563

Classification Binary Trees (CBT) (Petrakis et al., 2008). The con-struction of a CBT does not require any particular structure of thedata and is considered to handle adequately the lack of linearityand commensurability. Because of this property CBT can be com-plementary to CDA. The construction of a CBT comprises the splitof the original set of samples into two parts on the basis of a crite-rion involving a few variables, usually an algebraic expressioninvolving at most two of them. The data are classified into ‘leaves’of the tree (also, called nodes). The rationale of constructing ascheme from the group affiliation of existing EVOO samples is thata set of simple rules predicts the origin of the already existing andfuture EVOO samples of unknown origin. The group identifier wasused as dependent variable while independent variables are thederived ones by NMR (see below). The reduction of error in theclassification is monitored by means of a loss function. Amongstthe various loss functions, the Gini index has chosen as the fittingmethod on the basis of the better reduction in error it achieves.

3. Results and discussion

3.1. Data analysis

The discrimination of EVOO samples into various groupings wasstudied by initially identifying their compositional parameters, andthen using stepwise CDA and CBT with the identifying compoundsas predictors; the saturated fatty acids (SFA), the unsaturated oleic(OL), linoleic (LO), and linolenic (LN) acids, and the iodine value(IV) were determined by 1H NMR spectroscopy, while the two diac-ylglycerol (DGs) isomers, 1, 2-DGs and 1, 3-DGs, total free sterolsand free acidity were determined by employing 31P NMR spectros-copy. The latter spectroscopic methodology was used as well forthe quantification of eleven phenolic compounds, namely p-cou-maric acid, homovanillyl alcohol, the flavonols apigenin and luteo-lin, the lignans syringaresinol, (+) pinoresinol, and (+) 1-acetoxypinoresinol, free and total tyrosol, free and total hydroxyty-rosol (total hydroxytyrosol and total tyrosol represent the sum offree and esterified forms of both phenyl alcohols.). Spectroscopicassignments and methods of quantification of the 1H and 31PNMR data have been reported in detail elsewhere (Christophori-dou, & Dais, 2006; Fronimaki et al., 2002; Petrakis et al., 2008).

Table 1Tests for the monophyly of the varieties/cultivars of the olive oil tree from all divisions an

Compounds Adramitini

1 Syringaresinol2 1-Acetoxypinoresinol ⁄3 t-Hydroxytyrosol Le-d4 t-Tyrosol Le-e5 p-Coumaric acid ⁄6 f-Tyrosol ⁄7 f-Hydroxytyrosol8 Homovanillyl alcohol9 Luteolin10 Apigenin ⁄11 Pinoresinol12 Linolenic acid ⁄13 Linoleic acid ⁄14 Oleic acid ⁄15 SFA16 Iodine value ⁄17 1, 2-Diacylglycerol ⁄18 1, 3-Diacylglycerol ⁄19 t-Diacylglycerol ⁄20 D ratio ⁄21 Free acidity ⁄22 Total free sterols ⁄23 t-Phenols ⁄Number of supports 17

The mean values, the standard deviations and the range of varia-tion of the compositional parameters for the 221 EVOO samplesare summarised in Table S1 (Figures and Tables precede by ‘S’ de-note Supplementary material), whereas the compositional param-eters for each individual olive oil sample are depicted in Table S2,along with averages and standard deviations arranged according toharvest year, region, and cultivar. As mentioned before, variation inthe composition of fatty acids and phenolic compounds is a wellestablished fact, and is attributed to several endogenous and exog-enous factors, which may all influence the oil properties througheffects on olive fruit. This is reflected on the wide data variation ob-served in Table S1 for the concentrations of almost all phenoliccompounds as expressed by the relatively large standard devia-tions and the wide ranges between the minimum and maximumvalues of the measured quantities. The fatty acids were affectedto a lesser extent by these factors. The significant concentrationsspread observed for hydroxytyrosol and tyrosol (free and total)can be explained on the notion that these two phenols can be pro-duced by the partial hydrolysis of oleuropein glucoside and ligstro-side, respectively, leading thus to a diminution of their quantitiesin olive oil (Brenes, Garcia, Garcia, Rios, & Garrido, 1999; Gomez-Alonso et al., 2002; Kalua et al., 2005). Moreover, hydroxytyrosolis endowed by high antioxidant activity, favoring thus its furtherdegradation. These compounds appear to be the best predictorsfor subsequent chemometric analysis discussed below. The wideconcentration range of phenolic compounds depicted in Table S1was found to be a general feature of the data variability, while con-sidering separately the influence of harvest year, area of produc-tion, and cultivar on the olive oils composition. It is worth notingthat the olive oil samples are characterised by much higher con-centration of 1, 2-DGs compared to that of 1, 3-DGs (the ratio Dis close or higher than 0.90), corroborating the freshness of the col-lected olive oils.

3.2. Phylogenetic hypothesis of monophyly

The chemical compounds that support the monophyly hypoth-esis of a variety are phenological characters with a geneticbackground useful in defining the integrity of this evolutionaryunit. Table 1 shows that the compounds employed here convey

d sites where the olive oils originated. Symbols are explained in text.

Koroneiki Tsunati Throubolia

HC-a, CC-⁄, LP-e

HC-aSC-aSC-aSC-cSC-a ⁄SC-aSC-a

RC-dSC-cSC-a RC-e

⁄

RC-eSC-eSC-eSC-e

b

SC-c

12 4 4

2002-2003 2003-2004 2004-2005 2005-2006 2007-2008

Fig. 1. Three-dimensional CDA score-plot for the 221 EVOO samples separatedaccording to five harvesting periods (2002–2006 and 2007–2008). The axes CDA-1 = 49.2%, CDA-2 = 29.3%, and CDA-3 = 16.9% account for by the 96.4% of the totalvariability.

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important phylogenetic and biogeographical information. Thesymbols in Table 1 for the compatibility or Wilkinson support foreach compound are indicated as follows: blank = no support,⁄ = (0.01–0.20), e = (0.21–0.40), d = (0.41–0.60), c = (0.61–0.80),b = (0.81–0.90), a = (0.91–1.00). In hypotheses concerning olivetree cultivars of particular sites, the various symbols are: Le = Les-vos island, MP = Messinia, LP = Lakonia, HC = Heraklion, SC = Sitia,CC = Chania, RC = Rethymnon. Symbols can be combined such asHC-a or LP-e, which states that the monophyly is supported witha value a in Heraklion located in Crete, or with a value e in Lakonialocated in Peloponnesus.

Only two out of twenty-three chemical compounds and param-eters do not support the monophyly of the varieties adramitini,koroneiki, tsounati, and throubolia; saturated fatty acids (SFA)and syringaresinol do not offer support for the monophyly of thefour varieties. Table 1 shows that koroneiki and tsounati are differ-entially supported by many phenols. Koroneiki is supported onlyby the Cretan locality Sitia, whereas tsounati by two localities ofCrete, namely Heraklion and Chania. The variety adramintini orig-inated from Lesvos is supported by many compounds though eachcompound offers little support. On the other hand, throubolia vari-ety, a native of Rethymnon, Crete, is supported by three com-pounds and linoleic acid which additionally is highly Wilkinsonsupported. The Cretan populations of koroneiki in Sitia are stronglysupported by twelve compounds while tsounati populations arevariously supported by four compounds complementary to thosesupporting the monophyly of koroneiki from Sitia. These findingsagree with the geographical isolation of Sitia among Cretan locali-ties and the integrity of the cultivar tsounati. Sitia is an easternCrete locality and all areas in this geographic part bear many low-land isolated populations of southern Greece endemics. The mono-phyly support of linoleic acid to all olive tree varieties raises theinfluence of this compound in their formation. Linoleic acid seemsto be an important marker, and in this way it is not surprising thatis important in all multivariate analyses. In this way all variablesused for the analysis of olive cultivars is substantiated.

3.3. Influence of cultivar to the EVOO composition according toharvesting period

The statistical importance of the cultivar on the EVOO composi-tion was investigated by using the forward stepwise CDA. Thismethod restricts the selection of variables, since, at each step, allvariables are reviewed and evaluated to determine which one willcontribute most to discrimination between groups. That variablewill then be included in the model, and the process starts again.The stepwise CDA application to the compositional data of 221EVOO samples grouped into five harvesting periods proved to behighly significant (Wilks’ k = 0.019, Fapprox = 18.93, df1 = 72,df2 = 784, P < 10�4). The variation of the data is distributed intothree significant CDA axes accounting for by 49.2%, 29.3% and16.9% (total 96.4%) of the data variation, respectively. The three-dimensional CDA score-plot shown in Fig. 1 illustrates the verygood separation of EVOO samples extracted from four cultivarswith respect to the five harvest periods. This finding is also sup-ported by the very good classification rate (94%) reflected on thecalculated classification matrix shown in Table S3. The compoundsthat are statistically significant for EVOO discrimination are shownin Table 2 For the initial 23 compounds (variables), 13 were highlysignificant for this particular CDA; nine phenolic compounds, twofatty acids (linolenic and oleic), and total sterols appear to contrib-ute the most to the EVOO separation according to harvest period.The contribution of total sterols to olive oils separation accordingto crop season has not been reported previously, although applica-tion of chemometrics to olive oil composition revealed that somesterols alone or in combination with other olive oil constituents

can discriminate among olive varieties in olive oil (Montealegreet al., 2010). The remaining groups of variables, such as free acidity,diacylglycerols and the ratio D are characterised by low discrimi-natory power, and as expected they do not contribute to the groupseparation.

This analysis shows that harvest year is a strong discriminatingcomponent of the EVOO samples in agreement with earlier inves-tigations (Morelló, Romero, & Motilva, 2004; Ocakoglu et al., 2009;Romero, Tovar, Ramo, Motilva, 2003). The effect of harvest yearwas monitored by either measuring colorimetrically the total phe-nolic fraction or by determining individual phenols through HPLCexperiments. In one study (Ocakoglu et al., 2009), individual phe-nolic compounds influenced the most by the crop season werehydroxytyrosol, tyrosol, p-coumaric acid, cinnamic acid, vanillicacid, luteoline and apigenin, whereas in another study (Morellóet al., 2004), the levels of vanillin, tyrosol, apigenin, luteolin, seco-iridoid derivatives, and lignans were particularly affected by sea-sonal conditions. As indicated by the present study, almost thesame phenolic compounds found in Greek olive oils are influencedby the harvest period. However, our subsequent attempt to classifyGreek olive oils according to harvesting period was supported bycompletely different predictors (Table 2) compared to thosedescribing the separation of Turkish olive oils (Ocakoglu et al.,2009). In the latter study, it was found that the most influentialvariables were vanillin, syringic acid, caffeic acid, total phenol con-tent, and the parameters peroxide value and colour index.

To study further the influence of cultivar on the olive oils con-tent as a function of the crop season, CBT analysis of the EVOOsamples grouped into five harvest periods was performed. The re-sults are presented as a mobile in Fig. S1. The overall performanceof the mobile structure is very good with a high proportionalreduction of error of 95%. The EVOO samples of the five harvestperiods are distributed amongst 17 terminal nodes, eight of whichhave zero impurity. Half of the terminal nodes (leaves) have sam-ples from two at most harvest years, while the other half containssamples from the same year. This indicates that an unknown EVOOsample can be always classified in a leaf of the mobile with accept-able low uncertainty (5%). It is worthwhile to note that the split

Table 3Significant compounds and linear combinations of two compounds expressed as values responsible for the splitting in a CBT classifications of EVOO samples according to fiveharvest periods and four divisions. The same letters are used at the splitting nodes of Figs. S1 and S2.

Split CBT for five harvest periods Split CBT for four divisions

Criterion for the split Value Criterion of the split Value

A Sterols + apigenin 0.23 A Pinoresinol + syringaresinol 1.44B Iodine value + t-Hydroxytyrosola –0.57 B Sterols + Luteolin 1.74C Linolenic acid + luteolin 1.33 C Linolenic acid + t-hydroxytyrosola �1.25D Pinoresinol – t-Hydroxytyrosola 1.96 D Luteolin 0.37E Syringaresinol 0.41 E Iodine value + t-Tyrosola �1.16F SFAb – Homovanillyl alcohol 0.06 F 1-Acetoxypinoresinol �0.36G p-Coumaric acid 0.10 G f-Tyrosolc + p-Coumaric acid �1.24H Linolenic acid + Luteolin –1.42 H Syringaresinol �0.14I 1-Acetoxypinoresinol –1.35 I p-Coumaric acid – t-Tyrosola �0.51J Apigenin –0.35 J Oleic acid – f-Hydroxytyrosolc 0.18K1 Homovanillyl alcohol –0.39 K Linoleic acid 1.58K2 p-Coumaric acid + 1-Acetoxypinoresinol –1.17 L SFA – linoleic acid 1.04L Apigenin 0.23 M Oleic acid – syringaresinol 0.05M Syringaresinol 2.00 N Sterols + 1-Acetoxypinoresinol �0.87N Linolenic acid 0.19 O p-Coumaric acid – 1-acetoxy-pinoresinol 3.64O f-Tyrosolc –0.30 P 1-Acetoxypinoresinol + syringaresinol �0.57

Q Syringaresinol �0.35R Syringaresinol 0.98S Linoleic acid – linolenic acid 0.28T Oleic acid – linoleic acid 0.12

a t stands for total.b f’ stands for free.c SFA = saturated fatty acids.

Table 2Compounds statistically most important for the forward stepwise canonical discriminant analyses.

CDA for five harvest periods CDA for four divisions

Compound Wilks’ ka F-value, df1, df2 Pb Wilks’ ka F-value, df1, df2 Pb

Apigenin 0.353 98.87, 4, 216 <10�4 0.265 20.00, 18, 600 <10�4

t-Hydroxytyrosol 0.218 61.48, 8, 430 <10�4 0.149 16.84, 33, 611 <10�4

p-Coumaric acid 0.146 50.30, 12, 566 <10�4 0.202 18.70, 24, 610 <10�4

Sterols 0.113 42.51, 16, 651 <10�4

Pinoresinol 0.067 36.04, 24, 737 <10�4 0.486 31.26, 6, 432 <10�4

Luteolin 0.053 34.17, 28, 759 <10�4 0.141 15.95, 36, 609 <10�4

Linolenic acid 0.041 33.19, 32. 772 <10�4

Linoleic acid 0.673 35.11, 3, 217 <10�4

Oleic acid 0.034 32.54, 35, 778 <10�4 0.398 26.74, 9, 523 <10�4

Syringaresinol 0.029 30.51, 40, 787 <10�4 0.158 17.81, 30,611 <10�4

f-Hydroxytyrosol 0.024 26.64, 48, 792 <10�4

1-Acetoxypinoresinol 0.023 25.00, 52, 792 <10�4 0.184 17.77, 27.611 <10�4

f-Tyrosol 0.019 23.59, 56, 792 <10�4 0.128 14.40, 42, 606 <10�4

a The compounds are sorted according to their Wilks’ k significance.b Significance at a P-level of < 10�4.

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variables in the mobile summarised in Table 3 are mostly phenoliccompounds and to a lesser extent fatty acids and total sterols. Lin-olenic acid and nine phenolics, namely pinoresinol, 1-acetoxypi-noresinol, syringaresinol, p-coumaric acid, apigenin, luteolin,homonanillyl alcohol, f-tyrosol, and t-hydroxytyrosol contributedas well to the varietal classification using CDA analysis (seeTable 2). The obtained results of both analyses indicate that theharvest period plays an important role in the properties of theGreek olive oil cultivars.

3.4. Influence of cultivar to the EVOO composition according togeographical origin.

To investigate further the effect of cultivars on the EVOO com-positional parameters, the geographical divisions and sites of oliveoil production were considered in the sequel. Our attention was fo-cused first to the four geological divisions, Peloponnesus Crete,Zakynthos and Lesvos which are the main producing areas of oliveoil in Greece. The CDA of the four divisions is depicted in Fig. S2.

The two CDA axes, which are responsible for the 95.4% of the totalvariation in the data (CDA-1 = 65.3%, CDA-2 = 30.2%), leaving a 4.6%for the third CDA axis, allow the identification of the four divisions,though an overlap occurs between EVOO samples from Peloponne-sus and Zakynthos, presumably because of their geographical prox-imity (Petrakis et al. 2008). Also, this overlap may explain theabsence of any monophyly support of olive oils by the island ofZakynthos observed in Table 1. On the other hand, a higher degreeof discrimination is observed for the olive oils originated from theremote islands Lesvos and Crete located at the north-east side andfar south of Greece, respectively. This conclusion is also supportedby the classification matrix in Table S3. The lowest percentage ofclassification is observed for the oils from Zakynthos (61%) andPeloponnesus (77%), whereas the highest classification rates char-acterise the oils from Lesvos (100%) and Crete (93%). The data werefully explained by CDA with Wilks’ k = 0.108, Fapprox = 10.59,df1 = 60, df2 = 591, and P < 10�4. The important compounds thatare statistically significant for this particular CDA analysis are sum-marised in Table 2. As can be seen in this table, for the initial set of

2566 A. Agiomyrgianaki et al. / Food Chemistry 135 (2012) 2561–2568

the 23 compositional variables, 10 were found to be statisticallyhighly significant. It is worth mentioning that eight of these com-pounds, mostly phenolics, are common for the present CDA analy-sis and that for the harvest period (compare the data of both CDAsin Table 2). It appears that the hydrolysis products of oleuropein asexpressed in the present study by the concentration of totalhydroxytyrosol, and the three lignans pinoresinol, 1-acetoxytyro-sol, and syringaresinol are the most influential variables in dis-criminating the Greek olive oil samples according the four majordivisions. The significant contribution of secoiridoid derivativesin classifying olive oils according to geographical origin has beenobserved in previous investigations as well, reflecting the effectof pedoclimatic conditions on these minor compounds (Vinhaet al., 2005). Amongst fatty acids, oleic acid is significant in bothCDAs associated with harvest years and geographical divisions.This analysis is in accord with the phylogenetic approach summa-rised in Table 1, indicating that phenolic compounds and fattyacids bear important phylogenetic information in the sense thatthey support the monophyly of varieties and simultaneously theyseparate the sites of EVOO sample origin unlike the pattern ofextensive gene flow found in O. europaea

Apart from the classification ability of CDA, there is a need foran alternative scheme to identify oil samples according to geo-graphical divisions in a predefined algorithm. This was attemptedby the straightforward classification of CBT presented as a mobilein Fig. 2. In the construction of the mobile, the overall reductionof error was 97% (Table 3). The 221 EVOO samples were distributedin 21 terminal nodes; 17 of them were characterised by zero

P I NO RE S I N O L - S Y R I N G A RE S I N O L < 1 . 4 4

S T E R O L S - D < 1 . 7 4 L O < 1 . 5 8

P _ CO UM A R I C_ A CI D - T _ T Y RO S O L < - 0 .O L - F _ HY DDG 1 _ 3 + T _ H Y DR O X Y T Y R O S O L < - 1 . 2 5 L O U T E O L I N < 0 . 3 7

_ 1 _ A C E T O X Y P I N O R E S I NO L < - 0 . 3 6I V + T _ T Y R O S O L < - 1 . 1 6

F _ T Y R O S O L + P _ C O U M A RI C_ A CI D < - 1 . 2 4

S Y RI NG A R E S I NO L < - 0 . 1 4

Fig. 2. Classification tree (expanded version) of EVOO samples characterised by standarddivisions (Crete, Lesvos, Peloponnesus, Zakynthos) indicated by thick arrows. The overallnumbered in the bottom box. Underlined leaves are pure and those among them writtedivision, that is node 3 [Crete], node 9 [Lesvos], node 16 [Peloponnesus] and node 21 [Zakby a capital letter are explained in Table 3.

impurity, i.e. they contain oil samples corresponding to a singledivision, whereas the impurity for the remaining 4 nodes is lessthan 7% and as a rule contain two samples from two regions. It isinteresting to note in Table 3 that several phenolic compounds(p-coumaric acid, syringaresinol, 1-acetoxypinoresinol) and fattyacids (oleic, linoleic) occur several times in the splitting pattern.An interesting observation in the mobile of Fig. 2 is that the impureterminal leaves 14 and 19 contain mainly oil samples from Zakyn-thos, whereas the pure terminal leaf 11 is positioned in the branchof oils from Lesvos. This is additional evidence that explains the ab-sence of the monophyly support of oils collected in Zakynthos asdiscussed previously.

When CDA is applied to the grouping of the four sites located inCrete (Chania, Rethymnon, Sitia, Heraklion) the classification effi-ciency decreased dramatically to 74% (details not shown). Furtherdecrease up to 72% was observed when the sites Lakonia and Mes-sinia from Peloponnesus added into the CDA analysis.

3.5. Varietal classification of olive oils

The final statistical analysis concerns with the discrimination ofthe EVOO samples extracted from the four mono-cultivars (kor-oneiki, tsounati, adramitini, throubolia) without regard to the har-vesting period and geographical origin. Forward stepwise CDAapplied to 23 independent predictor variables determined byNMR is highly significant (Wilks’ k = 0.049, Fapprox = 20.64,df1 = 72, df2 = 599, P > 10�4) and resulted in a very goodclassification of the 221 olive oils grouped according to their

O L + L O < - 0 . 1 2

S F A - L O < 1 . 0 4

5 1

A CI D - T DG < - 0 . 0 5 T DG + _ 1 _ A CE T O X Y P I N O R E S I NO L < - 0 . 8 7

_ 1 _ A C E T O X Y P I NO RE S I NO L + S Y RI NG A RE S I NO L < - 0 . 5 7 L O - L N < 0 . 2 8

S Y RI N G A RE S I N O L < - 0 . 3 5

R O X Y T Y RO S O L < 0 . 1 8 P _ CO U M A RI C_ A CI D - _ 1 _ A C E T O X Y P I NO RE S I NO L < 3 . 6 4

S Y RI N G A RE S I N O L < - 0 . 9 8

ised data of the compound content and grouped according to the four geographicalreduction in error is high (0.97), while twenty-one terminal leaves are produced andn in higher font size and pointed with thick arrows bear the bulk of EVOO oils in aynthos]. Arrows with broken lines indicate terminal leaf numbers. The marked nodes

koroneiki tsunati throubolia adramitini

tt

Fig. 3. The diagram of cultivar samples in a three-dimensional discriminant space.The axes explain a significantly high percentage of the total variation in the originaldata CDA-1 = 71.2%, CDA-2 = 21.0%, and CDA-3 = 7.8% account for by the 100% of thetotal variability.

A. Agiomyrgianaki et al. / Food Chemistry 135 (2012) 2561–2568 2567

varietal identity. The lower rate of classification is observed for thecultivar tsounati (65%), whereas the classification for the remain-ing three cultivars is perfect ranging between 95% and 100%(Table S3). The CDA configuration is depicted in Fig. 3. Three CDAaxes describing the entire variation (100%) of the data are requiredto separate the four Greek cultivars. Nine phenolic compounds(luteolin, apigenin, pinoresinol, 1-acetoxypinoresinol, syringaresi-nol, homovanillic acid, free and total tyrosol, and free hydroxytyro-sol) and two fatty acids (linolenic and linoleic) were the mostimportant predictors in the model as reflected on the Wilks’ k val-ues (not shown). Indeed, the data in Table S2 reveal that thesecompounds show the largest variation amongst the four cultivars.The average amount of total phenolic compounds (shown in paren-thesis) contained in olive oil of each cultivar decrease in the orderthroubolia (222.2 ppm) > tsunati (170.6 ppm) > koroneiki(148.3 ppm) > adramitini (105.9 ppm), whereas linoleic acid fol-lows the succession throubolia (61.44%) > adramitini (11.26%) >koroneiki (5.54%) � tsunati (5.44%).

4. Conclusions

This study can be considered as a first systematic attempt toclassify Greek extra virgin olive oils on the basis of their phenoliccontent and fatty acids. Major phenolic compounds from fourimportant Greek olive cultivars with the highest discriminatorypower were luteolin, apigenin, pinoresinol, 1-acetoxypinoresinol,syringaresinol, homovanillic acid, free and total tyrosol, free andtotal hydroxytyrosol. Amongst the various factors that influencedthe olive oil phenolic composition, the harvest year was found tobe the most effective, and it was the most successful in discrimi-nating the olive oil samples regardless of their botanical origin.Satisfactory discrimination of the oil samples according to geo-graphical origin and cultivar shown by forward stepwise CDAand CBT indicated that oils from different olive species and differ-ent locations have sufficiently different distribution of phenoliccompounds and to lesser extent fatty acids to place them in

separate classes. Furthermore, it appears that compounds charac-terised by high discriminatory power have a firm phylogenetic ba-sis since they were found to support the monophyly of olivevarieties.

Acknowledgements

We thank the General Secretariat of Research and Technology ofGreece and the European Union for financial support through Pro-grams GKOR-017 to P.V.P. and 4.2.6.2B EPAN to P.D. Also, we areindebted to the program Regional Pole of Innovation for Value Cre-ation funded by the Administration of the Region of Crete and theEuropean Union.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foodchem.2012.07.050.

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