Food Chemistry 134 (2012) 2283–2290

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Food Chemistry

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Analytical Methods

Blends of olive oil and sunflower oil: Characterisation and olive oilquantification using fatty acid composition and chemometric tools

M. Monfreda a,b,⇑, L. Gobbi b, A. Grippa c

a Central Directorate for Chemical Analysis and Development of Laboratories, Italian Customs Agency, via M. Carucci 71, 00143 Rome, Italyb Department of Management, Sapienza University of Rome, via del Castro Laurenziano 9, 00161 Rome, Italyc Department of Business and Law, Roma Tre University, via Silvio D’Amico 77, 00154 Rome, Italy

a r t i c l e i n f o

Article history:Received 30 December 2011Received in revised form 29 February 2012Accepted 15 March 2012Available online 4 April 2012

Keywords:Olive oilBlendPrincipal component analysisTarget factor analysisSoft independent models of class analogyPartial least squares

0308-8146/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.foodchem.2012.03.122

⇑ Corresponding author at: Central DirectorateDevelopment of Laboratories, Italian Customs AgencRome, Italy. Tel.: +39 3396093528; fax: +39 0650244

E-mail address: maria.monfreda@gmail.com (M. M

a b s t r a c t

A method capable of recognising the percentage of olive oil in a blend is required to verify whether itslabelling complies with the statements set out by the Commission Regulation (EC) No. 1019/2002. In thisstudy an analytical methodology was developed in order to define blends of olive oil and sunflower oil,which contain 50% of olive oil, compared to blends with 40% and 60% of it, respectively. Methyl esters offatty acids were analysed by GC–FID and processed through chemometric tools (PCA, TFA, SIMCA andPLS). A strong differentiation of blends according to the amount of olive oil contained and a quantificationmodel with a standard error of prediction of 1.51% were obtained. As this issue represents a significantanalytical challenge, variability associated with the fatty acid composition of olive oil was first studied.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Olive oils have been extensively studied; in 1991 an EEC Com-mission Regulation (No. 2568) was issued, which regulates notonly the characteristics of olive oil and olive-residue oil but alsothe relevant methods of analysis for establishing any oil’s confor-mity to the relevant characteristics established for each category.The olive oil control field is in constant evolution; as a result, theabove regulation has been continuously amended, most recentlywith the introduction of fatty acid methyl esters and fatty acidethyl esters method of analysis in Commission Regulation (EU)No. 61/2011.

In Italy, the olive-growing industry is worth an estimated€3.2bn and is made up of over 775,000 businesses that use over1.1 million hectares of land and produce around 550,000 tonnesa year (Dell’Orefice, 2011). With exports and imports booming,strict control that aims to combat illegal attempts to boost profitsis of absolute importance.

European concern about commercial fraud on olive oils hasalways been significant: a typical case is the unlawful addition ofoilseeds in oils which should have been obtained exclusively from

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olive pressing. This fraud can be revealed by means of several chem-ical analyses provided for in EEC Regulation No. 2568/91 and its sub-sequent modifications and supplements. However, no officialmethod currently exists for quantifying olive oil and/or an oilseedin a blend. Several studies have been carried out on this subject,using various analytical methods followed by appropriate chemo-metric tools, with the aim of accurately measuring the relativequantities of olive oil and oilseeds in ad hoc blends. It is noteworthyto observe that these studies focused their attention on the detec-tion and quantification limits of seed oils in samples where oliveoil is the main ingredient (Aparicio & Aparicio-Ruìz, 2000; Cristo-poulou, Lazaraki, Komaitis, & Kaselimis, 2004; Fauhl, Reniero, &Guillou, 2000; Gurdeniz & Ozen, 2009; Papadopoulos, Triantis,Tzikis, Nikokavoura, & Dimotikali, 2002; Parcerisa, Casals, Boatella,Codony, & Rafecas, 2000; Peña, Cárdenas, Gallego, & Valcárcel, 2005;Poulli, Mousdis, & Georgiou, 2007; Priego Capote, Ruiz Jiménez, &Luque de Castro, 2007; Vigli, Philippidis, Spyros, & Dais, 2003;Vlachos et al., 2006). This approach appears reasonable when con-sidering that blends have always been associated with the battleagainst commercial fraud regarding oils that should have beenobtained exclusively by olive pressing. Consequently, even if theconcentration range of binary blends has been completely explored(0–100%), these studies have focused, however, on their evaluationat a low concentration of seed oils, instead of examining blends inthe strict sense of the word. It should also be noted that in severalcases, a relatively small number of olive oil samples has been used

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for preparing blends with seed oils: in other words, the responsevariability range of olive oil towards analytical methods could bemore fully detailed.

The subject of blends of olive oils and other vegetable oils ishandled by Commission Regulation (EC) No. 182/2009, amendingCommission Regulation (EC) No. 1019/2002 on marketing stan-dards for olive oil. In these regulations, the marketing of blendsof olive oils and other vegetable oils is also included. RegulationNo. 1019/2002 sets the following trade description for blendswhose labelling highlights the presence of olive oil elsewhere thanin the list of ingredients, using words, images or graphics: ‘Blend ofvegetable oils (or the specific names of the vegetable oils con-cerned) and olive oil’, directly followed by the percentage of oliveoil in the blend. It is also stipulated that the presence of olive oilmay be highlighted by images or graphics on the label of a blend,only where it accounts for more than 50% of the blend concerned.Commission Regulation (EC) No. 182/2009 declares, moreover, thatMember States may prohibit the production in their territory ofblends of olive oil and other vegetable oils for internal consump-tion. However, they may not prohibit the marketing on their terri-tory of such blends originating from other countries and they maynot prohibit the production on their territory of such blends formarketing in another Member State or for export. This regulation,in fact, laid the foundations for the boom in the blends market.

In light of what is stated in Regulation No. 182/2009, an analyt-ical method capable of quantifying the percentage of olive oil in amixed blend of olive oil and other vegetable oils will soon be nec-essary, in order to prevent marketing fraud. The percentage of 50%of olive oil is the most important discriminator, with particular ref-erence to the labelling. De la Mata-Espinosa, Bosque-Sendra, Bro,and Cuadros-Rodríguez (2011) suggested an interesting approachto the problem of olive oil quantification in a blend, using high per-formance liquid chromatography (HPLC) analysis of triacylglyce-rols and subsequent multivariate regression.

The fatty acid composition, evaluated by means of methylesters of fatty acids, is a noteworthy parameter for the charac-terisation of an olive oil fingerprint. Limit values for the per-centage of methyl esters of fatty acids in olive oils areestablished in the EEC Regulation No. 2568/91 and its subse-quent modifications and supplements. It can be seen that suchlimits are consistent for each category of olive oil (except forthe trans isomers), because they depend on the type of oil.The method of analysis is also set out in the Commission Reg-ulation (Annex XA and XB).

The methyl esters limits set by the EEC Regulation No. 2568/91and its subsequent modifications and supplements are shown inTable 1, from which it can be seen that the percentages of somefatty acids (palmitic, palmitoleic, stearic, oleic and linoleic) are

Table 1Fatty acids methyl esters limits set by the Com-mission Regulation(EEC) No. 2568/91 for olive oils.

Fatty acids methyl esters Limits (% m/m)

Myristic 60.05Palmitic 7.5–20.0Palmitoleic 0.3–3.5Margaric 60.3Margaroleic 60.3Stearic 0.5–5.0Oleic 55.0–83.0Linoleic 3.5–21.0Arachidic 60.6Linolenic 61.0Eicosenoic 60.4Behenic 60.2Lignoceric 60.2

expected to vary over quite a large range, whilst for the other acids,found at amounts lower than 1% mass/mass (m/m), only the upperlimits are established.

By contrast, the fatty acid composition of seed oils is not set outin any rule (neither in European law, nor in any single MemberState law, but some references can be found in literature (Amelotti,1990)). In Table 2, the fatty acid compositions of some seed oils(corn, peanut, grape seed, sunflower and rice) are shown.

In light of the high variability found in the fatty acid compositionof olive oil, it can be deduced that mixing olive oil with seed oil willproduce some blends that have even more variable fatty acid com-position. For this reason, the identification and the quantificationof the olive oil percentage in a blend is an analytical challenge. Thisissue may be studied with the help of chemometric procedures, suchas principal component analysis (PCA), target factor analysis (TFA),soft independent models of class analogy (SIMCA) and partial leastsquares (PLS). PCA, SIMCA and PLS have already been employedfor the study of blends. PCA has also been used for the evaluationof virgin olive oil evolution with time (Alonso-Salces, Holland, &Guillou, 2011). PCA and SIMCA have also been widely used for oliveoil discrimination in relation to geographical origin (Alonso-Salceset al., 2010; Longobardi et al., 2012; Mannina, Marini, Gobbino,Sobolev, & Capitani, 2010; Marini, Magrì, Bucci, Balestrieri, & Marini,2006; Papadia et al., 2011; Pizarro, Rodríguez-Tecedor, Pérez-del-Notario, & González-Sáiz, 2011), or the olive fruit variety (Bucci,Magrì, Magrì, Marini, & Marini, 2002; Diaz, Merás, Casas, & Franco,2005; Pouliarekou et al., 2011). TFA has been employed in order toverify the presence or the absence of specific compounds in matriceswhich, as a result of some instrumental analyses, gave a notableoverlapping of signals (Lohnes, Guy, & Wentzell, 1999; Miao, Cai, &Shao, 2011; Shao, Roske, & Griffiths, 2010).

The aim of this study was to verify whether it is possible to rec-ognise, firstly in blends of olive oil and sunflower oil, the percent-age of 50% of olive oil (because of the discrimination power of thisfigure in blend labelling), comparing such blends with ones con-taining 40% and 60% of olive oil, and by applying the official meth-od for the analysis of methyl esters of fatty acids by GC–FID,followed by chemometric tools.

2. Materials and methods

2.1. Samples

First of all, 12 olive oil samples with significant variability intheir fatty acid composition were selected, in order to obtainresults covering the full variability that exists among olive oils.For each olive oil sample, three blends with a sunflower oil samplewere prepared, with a total volume of 10 mL and containing 40%,50% and 60% respectively in olive oil volume. Volumes were mea-sured and transferred with a 10-mL micropipette and the blendswere vigorously shaken.

Finally, 36 samples (spread in three categories related to thepercentage of olive oil) were prepared and analysed.

2.2. Sample preparation and analysis by GC–FID

Sample (0.1 g) was dissolved in 2 mL of heptane and shaken.The sample was then trans-esterified with 0.2 mL of 2 N methano-lic potassium hydroxide solution and vigorously shaken. The upperlayer of the solution was analysed by GC–FID (Commission Regula-tion (EEC) No.2568/91).

GC was performed on a 60 m � 0.2 mm i.d. � 0.25 lm film HP-88 capillary column using an Agilent 6890 gas chromatograph con-nected to a flame ionisation detector (FID). The GC conditions usedwere as follows: injection volume 1 lL, split injection 50:1 at

Table 2Some examples of fatty acid composition of seed oils.

Corn (% m/m) Peanut (% m/m) Grape seed (% m/m) Sunflower (% m/m) Rice (% m/m)

Myristic Max 0.1 Max 0.1 Max 0.2 Max 0.1 0.1–0.7Palmitic 10.0–15.0 8.0–13.5 6.0–8.0 5.0–8.0 17.0–22.0Palmitoleic Max 0.5 Max 0.5 Max 0.1 Max 0.5 0.1–0.4Stearic 1.5–3.0 2.0–5.0 3.0–6.0 2.5–7.0 1.0–2.5Oleic 23.0–41.0 35.0–70.0 12.0–25.0 15.0–40.0 30.0–45.0Linoleic 41.0–63.0 15.0–48.0 60.0–76.0 40.0–74.0 35.0–50.0Arachidic 0.2–0.7 1.0–2.5 Max 0.5 Max 0.5 0.1–0.5Linolenic 0.6–1.1 Max 0.2 Max 0.5 Max 0.3 1.0–2.0Eicosenoic 0.2–0.5 0.9–1.5 Max 0.2 Max 0.5 0.2–0.3Behenic Max 0.2 2.0–4.0 Max 0.2 0.5–1.0 tr–0.3Lignoceric 1.0–2.5

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240 �C; oven temperature set at 170 �C for 30 min, then ramped to230 �C at 5 �C min�1 (held for 3 min), giving a total run time of45 min. Helium gas carrier was held at a constant flow rate of1 mL min�1, whilst the detector was set at a temperature of 300 �C.

A typical fatty acid methyl esters chromatogram of a blend con-taining 50% of olive oil is shown in Fig. 1.

2.3. Preliminary statistical tests

The fatty acid variance associated with the 12 blends containing50% of olive oil was compared with the variance associated with 10blends containing 50% of the same olive oil by a one-tailed F-test at0.05 significance level.

Furthermore, a one-way Anova was performed on the wholedata set in order to compare, for each variable, the variance withinany category with the variance between categories.

2.4. Multivariate statistical analysis

First of all, an unsupervised tool, namely principal componentanalysis (PCA), was applied in order to have a general overviewof the data. PCA was applied in order to highlight a natural group-ing of samples depending on their olive oil percentage and to gainsome useful information about how variable loadings are linked toa potential separation between groups.

Target factor analysis (TFA) is an extremely useful technique forstudying mixtures of pure samples. It is strongly linked to PCA,from which the ‘‘p’’ number of significant factors is achieved, cor-responding to the number of pure samples used to produce the

Fig. 1. Typical fatty acids methyl esters chromatogram of a blend containing 50% of oliv(6), oleic (7) trans-linoleic (80), linoleic (8), arachidic (9), linolenic (10), eicosenoic (11),

mixtures. If the composition of some pure samples is known, TFAtests if they are possible constituents of the mixture by evaluatingthe distance of each pure sample in the data base, as the residualvariance from the hyperplane defined by the ‘‘p’’ significantcomponents.

Among supervised pattern recognition procedures, class model-ling tools show some advantages compared to classification meth-ods as they build a model for each category instead of a simpledelimiter between classes. The modelling rule discriminates be-tween the studied category and the rest of the universe. As a con-sequence, each sample can be assigned to a single category, tomore than one category (if more than one class is modelled) or,alternatively, considered as an outlier if it falls outside the model.SIMCA (Soft independent models of class analogy) builds a mathe-matical model of the category with its principal components and asample is accepted by the specific category if its distance to themodel is not significantly different from the class residual standarddeviation. SIMCA was applied, considering a 95% confidence level,to define the class space and an unweighted augmented distance(Wold & Sjostrom, 1977).

Multivariate regression was finally applied, using PLS algorithmwhich looks for the direction of maximum variance among thevariables, but weighing the variables upon their higher or lowercorrelation with the response variable. The model builds latentvariables, which are a combination of original variables. The num-ber of latent variables used for calculating the closed form was ob-tained from the minimum value of the standard deviation of theerror of prediction (SDEP), as a function of the number of latentvariables.

e oil: myristic (1), palmitic (2), palmitoleic (3), margaric (4), margaroleic (5), stearicbehenic (12) and lignoceric (13).

Table 4Fatty acid composition of the sunflower oil sample usedin preparing the blends.

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All the computations were performed using V-PARVUS (Forina,Lanteri, Armanino, Cerrato-Oliveiros, & Casolino, 2010) and SPSS(IBM Statistics Computer Program, 2010).

Sunflower oil (% m/m)

Myristic 0.07Palmitic 6.55Palmitoleic 0.11Margaric 0.04Margaroleic 0.03Stearic 3.67Oleic 32.00Linoleic 56.03Arachidic 0.23Linolenic 0.04Eicosenoic 0.13Behenic 0.72Lignoceric 0.38

3. Results and discussion

The fatty acid composition of 241 olive oil samples, analysedover the last 2 years (2010–2011) was evaluated in order to select12 samples with an acidic profile variable over as wide a range aspossible. These data are shown in Table 3 where, for each fattyacid, the mean value, the standard deviation, the minimum value,the maximum value and the range of variability (the difference be-tween the maximum and minimum value) are indicated. From thistable, for instance, a range of variability of 24.58% for oleic acid(with a minimum value of 55.30% and a maximum of 79.88%)and a range of 15.59% for linoleic acid (with a minimum value of4.38% and a maximum of 19.97%) can be noted.

The fatty acid composition of the sunflower oil sample used inpreparing the aforementioned blends is shown in Table 4, whilstthe fatty acid profiles of the 12 olive oil samples selected for thisstudy are presented in Table 5.

3.1. Preliminary statistical tests

A preliminary statistical test was carried out in order to checkwhether a single analysis for each blend might be adequate for thisstudy; in other words, whether the variability associated with themeasurements repeatability in one sample would be significantlylower than the variability among samples belonging to the samecategory (variability within category), considering that each cate-gory is made up of blends of a sunflower oil with widely varyingolive oils. This comparison (made by a one-tailed F-test) showedthat variance within category is significantly higher than the vari-ance due to the measurements repeatability (the olive oil samplelabelled number 4 in Table 5 was chosen for this evaluation).Therefore, it seemed adequate for the purpose of this study to per-form only one analysis for each sample, with vigorous homogeni-sation of each blend before analysis.

One-way ANOVA was performed on the whole data set, in orderto select, for further statistical analysis, the more significant fattyacids for discrimination between categories. Myristic, margaroleic,oleic, linoleic, arachidic, linolenic, behenic and lignoceric acidshave a between-category variability significantly higher than thewithin-category variability (for lignoceric acid the discriminationpower was verified only between categories containing 50% and60% of olive oil, because the preliminary assumption of homogene-ity of variance, was verified only between these two categories).

Table 3Summary of fatty acid composition of 241 olive oils: mean value (% m/m), standarddeviation, minimum value, maximum value and range of variability (differencebetween the maximum and minimum value) for each fatty acid are shown.

Mean St. Dev. Min Max Range

Myristic 0.010 0.004 0.000 0.038 0.04Palmitic 12.09 2.12 9.49 18.98 9.50Palmitoleic 1.15 0.44 0.58 2.54 1.96Margaric 0.05 0.02 0.00 0.12 0.12Margaroleic 0.10 0.03 0.06 0.21 0.15Stearic 3.01 0.53 1.61 4.15 2.54Oleic 72.77 5.94 55.30 79.88 24.58Linoleic 9.47 3.90 4.38 19.97 15.59Arachidic 0.36 0.03 0.23 0.45 0.22Linolenic 0.60 0.05 0.24 0.71 0.47Eicosenoic 0.23 0.04 0.09 0.42 0.33Behenic 0.11 0.02 0.03 0.22 0.19Lignoceric 0.05 0.02 0.00 0.16 0.16

Palmitic, palmitoleic, margaric, stearic and eicosenoic acids donot have discrimination power because the between-category var-iability is not significantly different from the within-categoryvariability.

3.2. Multivariate statistical analysis

Multivariate statistical analysis was performed on a data set of36 samples and eight variables selected by ANOVA (correspondingto % m/m of myristic, margaroleic, oleic, linoleic, arachidic, linole-nic, behenic and lignoceric acids) in order to:

� highlight the natural variability existing within groups contain-ing 40%, 50% and 60% of olive oil by PCA;� identify the fingerprint of pure oils used for preparing the

blends (olive and sunflower) by TFA;� find a decision rule for evaluating whether an unknown blend

could be associated to one of the analysed groups by means ofSIMCA;� build a quantitative model which would allow one to find the

percentage of olive oil in a blend of olive oil and sunfloweroil, using PLS.

3.2.1. Principal component analysis (PCA)Variables were preprocessed by column autoscaling:

yiv = (xiv � xvm)/sv, where xiv is the value of the variable v relativeto the sample i, xvm is the mean value of the variable v, and sv isthe standard deviation of the variable v. Two PCs were extracted,with eigenvalues >1; they explain 79.7% of the total variance.

The bi-plot of PC2 versus PC1 is shown in Fig. 2. A well-definedseparation of samples in accordance with the percentage of oliveoil was achieved on PC1: the blends containing 40% olive oil clusterin the negative values of PC1, samples with 50% olive oil aregrouped around the origin, whilst blends with 60% of olive oil havethe highest positive scores on PC1.

From the bi-plot, it can be seen that variables with positiveloadings on PC1 are linolenic, oleic, arachidic and margaroleicacids, whilst variables with negative loadings on PC1 are behenic,linoleic, myristic and lignoceric acids.

Such loading distribution is strongly related to the fatty acidcomposition of pure oils; in fact, olive oil has a significantly highercontent of oleic, linolenic, arachidic and margaroleic acids com-pared to sunflower oil, whilst the latter has a significantly higherconcentration of behenic, linoleic, myristic and lignoceric acidscompared to olive oil (see Tables 2 and 3). This explains whyblends that are more similar to olive oil than sunflower oil (with60% of olive oil) cluster toward positive values of PC1, whilstblends that are more similar to sunflower oil than olive oil

Table 5Fatty acid composition of the 12 olive oil samples selected for this study (% m/m).

Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Sample 10 Sample 11 Sample 12

Myristic 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00Palmitic 11.85 13.22 13.32 13.41 17.33 10.35 18.06 10.36 10.4 13.79 9.51 16.79Palmitoleic 1.04 0.74 1.38 1.37 2.08 0.86 2.42 0.87 0.85 1.40 0.75 2.16Margaric 0.05 0.06 0.04 0.04 0.03 0.06 0.03 0.04 0.04 0.07 0.09 0.03Margaroleic 0.11 0.09 0.09 0.09 0.07 0.10 0.08 0.08 0.08 0.15 0.15 0.08Stearic 2.97 2.86 2.62 2.63 2.42 3.57 2.26 3.81 3.74 2.19 3.54 2.20Oleic 74.49 66.73 67.69 67.29 58.44 76.66 57.16 77.72 77.62 70.18 77.28 59.54Linoleic 8.13 14.67 13.55 13.83 18.21 7.04 18.59 5.78 5.92 10.8 7.16 17.76Arachidic 0.34 0.42 0.36 0.36 0.37 0.36 0.37 0.36 0.36 0.36 0.37 0.36Linolenic 0.58 0.71 0.59 0.58 0.67 0.61 0.67 0.60 0.61 0.60 0.66 0.64Eicosenoic 0.24 0.33 0.25 0.22 0.19 0.22 0.17 0.21 0.21 0.25 0.24 0.17Behenic 0.13 0.11 0.03 0.11 0.12 0.11 0.11 0.11 0.11 0.12 0.11 0.11Lignoceric 0.04 0.05 0.07 0.06 0.06 0.05 0.07 0.05 0.05 0.08 0.13 0.16Others 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Fig. 2. Bi-plot of PC1 versus PC2 obtained from the data set relative to 36 samples and eight variables.

Table 6Fatty acid composition of six pure oils used in target factor analysis (% m/m).

Olive Corn Peanut Grape seed Sunflower Rice

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(with 40% of olive oil) are grouped toward negative values of PC1.Finally, blends containing 50% of olive oil cluster in the middle ofthe plot.

Myristic 0.01 0.03 0.04 0.04 0.1 0.36Palmitic 12.09 9.97 11.82 6.58 8.01 19.5Palmitoleic 1.15 0.11 0.07 0.10 0.12 0.24Margaric 0.05 0.06 0.06 0.05 0.00 0.03Margaroleic 0.10 0.03 0.03 0.03 0.00 0.00Stearic 3.01 2.02 3.38 3.84 3.21 1.97Oleic 72.77 28.02 39.97 19.11 27.52 42.19Linoleic 9.47 57.98 37.75 69.65 60.11 32.55Arachidic 0.36 0.32 1.45 0.15 0.18 0.99Linolenic 0.60 0.90 0.10 0.23 0.05 0.92Eicosenoic 0.23 0.17 0.90 0.15 0.11 0.46Behenic 0.11 0.19 2.85 0.02 0.45 0.29Lignoceric 0.05 0.20 1.46 0.05 0.14 0.47Others 0.00 0.00 0.12 0.00 0.00 0.03

3.2.2. Target factor analysisTarget factor analysis was applied in order to check whether the

fingerprints of olive oil and sunflower oil would have been recogni-sed in the 36 blends, forming the original data matrix. For this pur-pose, a test set of six ‘‘pure’’ objects, corresponding to the fatty acidcomposition of the same number of pure oils, was added to theaforementioned data matrix. The pure oils fatty acid compositionis reported in Table 6 where for the olive oil composition a mean va-lue was chosen among the 241 olive oils already mentioned, whilstfor the sunflower oil a different sample was chosen from the oneused for preparing blends. For the purpose of this study, corn, pea-nut, rice and grape seed oil were also chosen, as shown in Table 6.Factors with eigenvalues >1 were chosen as significant.

TFA was applied to a data set consisting of 36 samples, six pureobjects and eight variables (the same variables, pre-processed bycolumn autoscaling: yiv = (xiv � xvm)/sv, used for PCA). As only twosignificant factors, namely two factors with eigenvalues >1, wereextracted with PCA, TFA has to identify the same number of target

factors, in other words two types of oil used to produce blends.Indeed, the first and the second target factor (ordered accordingto the residual variance) were the chromatographic profiles of oliveoil and sunflower oil, respectively. It must be noted that, despitethe sunflower oil used as a pure sample having a different

Fig. 4. Cooman’s plot for classes 2 and 3.

2288 M. Monfreda et al. / Food Chemistry 134 (2012) 2283–2290

composition from the one used for preparing blends, and althoughthe olive oil used as a pure sample had a mean composition among241 samples (most of them were not included in this data set), thefingerprints of olive oil and sunflower oil were still recognisedamong six different kinds of vegetable oils. Such a result is note-worthy because, in real cases, where the exact composition of pureoils used for preparing blends might not be available, it would bepossible to check whether a blend actually contains the oils listedon the label. This result is also important because it demonstrates(for blends of olive oil with sunflower oil) that just a single analysisof fatty acids, in spite of the variability existing in pure samples,could be enough for a qualitative control of the blend compositionstated on the label.

3.2.3. SIMCASIMCA was applied to the same data matrix used for PCA and

TFA, performing a cross validation with six cancellation groupsand using seven components to build the mathematical model ofeach class. Classification ability (modelling rate) and predictionability (prediction rate) of 100% in both were obtained. The sameparameters are normally provided by all the classification methodstoo but the performance of a class-modelling tool can also be eval-uated by sensitivity and specificity. The sensitivity (the percentageof objects belonging to the category which are correctly identifiedby the mathematical model) and specificity (the percentage of ob-jects from other categories which are classified as foreign) wereboth equal to 100%.

From Cooman’s plots (Coomans et al., 1984), representing thedistance of each sample from a specific category, all samples areclassified in their own class, there are no outliers, nor are thereany samples assigned to both classes. Fig. 3 is relative to classes1 (blends with 40% of olive oil) and 2 (blends with 50% of oliveoil), whilst Fig. 4 is relative to classes 2 and 3 (blends with 60%of olive oil). Cooman’s plot relative to classes 1 and 3 is not dis-played as it is less relevant for the purpose of this study than thediscrimination between class 2 and each of the other classes.

3.2.4. PLSTwelve samples were extracted from the data set in order to

construct the external calibration set. Variables were centred, eightcancellation groups were used for model validation, obtaining a

Fig. 3. Cooman’s plot for classes 1 and 2.

prediction ability that was less optimistic but more realistic thanthe one given by the leave-one-out application. Performance ofthe model was evaluated through the mean of SDEP (because thisparameter was calculated for each cancellation group), whilstmodel stability was evaluated by the standard deviation of SDEPin cancellation groups and the standard deviation of the mean ofSDEP in cancellation groups.

External evaluation of the model was quantified by the root-mean-squared error of prediction (RMSEP) (Kowalski & Seasholtz,1991; Massart et al., 1997). The best prediction was obtained withfive latent variables; therefore, the closed form was calculated witha complexity of five. The prediction ability, evaluated by the meanvalue of SDEP in cancellation groups, is extremely satisfying, con-sidering that equals 1.51% of olive oil in blends.

Model stability (that is, performance variability among cancel-lation groups) was also good, because of the low values of the stan-dard deviation of SDEP in cancellation groups (equals 0.68%) andthe standard deviation of the mean of SDEP in cancellation groups(equals 0.24%). The RMSEP obtained for the external evaluation setwas 2.10%. In Fig. 5, the predicted (5a) and computed percentages(5b) of olive oil versus the actual ones are shown.

4. Conclusions

Multivariate statistical analysis, applied to blends of olive oil(12 samples were used) with sunflower oil (1 sample) gave out-standing results.

PCA allowed the highlighting of the differences existing be-tween groups containing 40%, 50% and 60% of olive oil. Such groupsare, in fact, well separated on the first principal component, eventhough the data set was studied to represent the existing variabil-ity in the fatty acid composition of olive oil in the widest way pos-sible. PCA also gave some useful information about variableloadings in the separation between groups.

With TFA, it was possible to recognise the fingerprints ofboth oils used for preparing blends, in spite of the sunflowerand olive oil targets having a different fatty acid compositionfrom that of the samples used for preparing blends. Such a pro-cedure might be applied, in real cases, in order to checkwhether a blend is really obtained from the oils listed on thelabel, where only the type of pure oils and not their fatty acid

Fig. 5. Predicted (a) and computed (b) percentages of olive oil versus actual ones.

M. Monfreda et al. / Food Chemistry 134 (2012) 2283–2290 2289

composition is supposed to be indicated. Moreover, TFA indi-cated that just a single analysis of fatty acids, in spite of theexisting variability in the fatty acid composition of pure sam-ples, might well be enough for an (qualitative) identificationof pure oils forming a blend.

SIMCA allowed the construction of class models without outliersor overlapping areas between classes (sensitivity and specificityboth of 100%). It can be concluded that blends containing 50% of oliveoil were perfectly distinguishable from the others, even thoughthere was a great variability in the olive oils used for preparing them.

Finally, with PLS a good quantitative model was constructed,having a standard error of prediction of 1.51% and a standard errorof prediction on the external evaluation set of 2.10%.

This study provides an initial evaluation of how natural vari-ability in the olive oil might affect the blends with a specific seedoil (sunflower) and suggests also a methodological approach, beit qualitative or quantitative, for verifying the composition of a bin-ary blend of olive oil and sunflower oil, in compliance with the

rules established by Commission Regulation (EC) No. 1019/2002.This method provides some advantages because it involves theapplication of only one analysis (against the several analyses re-quired by the EEC Regulation No. 2568/91 for the classification ofa pure olive oil) using an official method (relatively quick and al-ready set for the characterisation of olive oils) and is subject onlyto the analyses of some blends that have a known composition.

The model proposed by this work will be extended to also in-clude the variability associated with different types of seed oils,in order to build wider decision rules that could be applied toblends of vegetable oils to identify the percentage of olive oil,and/or the threshold value for the use of olive imagery or graphicson blended oil labelling.

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