Spectroscopic techniques and chemometrics in analysis of blends of extra virgin with refined and...

Research Article

Spectroscopic techniques and chemometrics in analysisof blends of extra virgin with refined and mild deodorizedolive oils

Krzysztof Wójcicki1, Igor Khmelinskii2, Marek Sikorski3, Francesco Caponio4, Vito M. Paradiso4,Carmine Summo4, Antonella Pasqualone4 and Ewa Sikorska1

1 Faculty of Commodity Science, Pozna�n University of Economics, Pozna�n, Poland2 FCT and CIQA, Universidade do Algarve, Campus de Gambelas, Faro, Portugal3 Faculty of Chemistry, Adam Mickiewicz University, Pozna�n, Poland4 Food Science and Technology Unit, Department of Soil, Plant and Food Sciences, University of Bari, Bari, Italy

The capability of different spectroscopic techniques in estimation of adulteration of extra virgin olive oilwithmild deodorized and refined olive oils was investigated. The visible absorption and ultraviolet–visiblefluorescence spectra were distinctly different for oils of different quality grades. Differences in the near‐and mid‐IR absorption spectra were less pronounced and could only be revealed by principal componentanalysis. The calibration models for estimation of adulterant oil in the blends obtained using partial leastsquares (PLS) and principal component regression (PCR) showed similar performance using all of theexamined spectroscopic techniques. The simultaneous analysis of combined spectra using concatenatedmatrix did not improve the calibration results. The prediction parameters for the set of 20 samples were:R2>0.96, the relative error of prediction (REP) was in the range 5.6–12.0%. The RPD (ratio of the SD ofreference values to RMSEP) were in the range of 5.1–10.9, indicating that all models were at least suitablefor quality control.

Keywords: Adulteration / Olive oil / Partial least squares regression (PLS) / Principal component regression (PCR) /Spectroscopy

Received: October 22, 2013 / Revised: June 29, 2014 / Accepted: July 9, 2014

DOI: 10.1002/ejlt.201300402

:Supporting information available online http://dx.doi.org/10.1002/ejlt.201300402

1 Introduction

Extra virgin olive oil is obtained from the olive fruits (Oleaeuropaea L.) solely by mechanical means. It has superiororganoleptic properties and health benefits as compared toother oils. Its economic value is much higher than that ofother seed oils, thus its illegal adulteration with cheaper oilsbecomes a real concern for regulatory agencies, oil producers,

and importers and, last but not least, consumers. One ofthe common adulterations is addition of olive oils of lowerquality grade. Refined or mild deodorized olive oils arefrequently used for this purpose. Refined olive oil is obtainedusually from “lampante” olive oil (low quality oil, unsuitablefor human consumption, mechanically extracted from olivefruit) by means of the refining process. Mild deodorizedoil is an inexpensive product derived from oils with someorganoleptic defects, which were submitted to thermaldeodorization at moderate temperatures to remove theundesired volatile components. Their quality is not compa-rable with that of authentic extra virgin olive oils, as they areobtained by removing defects from low‐quality virgin oliveoils, whose deficiencies are due to either low‐quality olivesor to bad manufacturing practices.

Detection of low‐quality oils is very difficult. Particularly,the detection of mild thermally deodorized oil, in mixtureswith extra virgin olive oil, constitutes one of the modern

Correspondence: Dr. Ewa Sikorska, Faculty of Commodity Science,Pozna�n University of Economics, al. Niepodległo�sci 10, 61‐875 Pozna�n,PolandE‐mail: [email protected]: þ48 61 8543993

Abbreviations: DO, deodorized olive oil; EVO, extra virgin olive oil; MSC,multiplicative scatter correction; PCR, principal component regression;PLS, partial least squares; RMSEP, root‐mean‐square error of prediction;RO, refined olive oil

Eur. J. Lipid Sci. Technol. 2014, 116, 0000–0000 1

� 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.ejlst.com

challenges in oil analysis, since the relatively low processingtemperatures (<120°C) adopted allow to obtain oils free fromthe typical artifacts formed during refining [1].

Different analytical techniques have been applied todiscriminate among oils of different categories as well asdetect adulteration of extra virgin olive oil with cheaper oliveoils. Some of them are based on the detection and/orquantification of some chemical components characteristicfor the specific oil quality that may serve as markers for oilauthenticity or presence of adulterant [2].

For example, the absence of cis‐phytol can be used asa marker for non‐refined (e.g., cold‐pressed) edible oils [3].The concentration of fatty acid alkyl esters, mainly ethylesters, had been used to distinguish extra virgin olive oils fromthose obtained from altered olive or olive pomace and todetect mildly refined low‐quality olive oils in blends withextra virgin oil [4, 5]. Fatty acid methyl ester – 9(E),11(E)‐octadecadienoate – had been detected at trace levelsexclusively in thermally stressed oils and was proposed asa marker for adulteration of extra virgin olive oil withdeodorized oil [6].

Detecting and quantifying specific chemical componentscharacteristic to the oil grade involves application ofchromatographic techniques including high‐resolution GCand high‐performance liquid chromatography [7].

An alternative approach to authentication and adultera-tion detection is based on obtaining a chemical fingerprintof oil using non‐selective, usually spectroscopic methods.Using multivariate analysis methods, chemical informationincluded in spectra may be used for qualitative (characteriza-tion, classification, and authentication) and quantitative(determination of adulterant concentration) purposes. Themain advantages of spectroscopic techniques coupled withchemometrics are: their simplicity, rapidity, ease, or totalabsence of sample preparation and ability to obtain theinformation about different product properties in a singlemeasurement. Molecular absorption and emission spectros-copy has been extensively used for authentication andadulteration detection of extra virgin olive oils. Successfulapplications of spectroscopic techniques for detection ofadulteration with lower quality olive oils were reported byseveral researchers [2]. FTIR spectroscopy coupled withchemometrics was used to detect adulteration of extra virginolive oil with pomace olive oil, high oleic and high linoleicsunflower oils, canola oil, and hazelnut oil [8]. High‐field31P NMR spectroscopy was used for detection of extra virginolive oil adulteration with refined and lampante olive oils atlevel of 5% w/w [9]. Diffuse‐light absorption spectroscopyin visible and near‐IR region was used for detecting andquantifying the adulteration of extra virgin oil with olivepomace, refined and deodorized olive oils [10]. UV–Visspectroscopy was applied to quantify adulterations of extravirgin olive oil with refined olive oil and refined olive‐pomace [11]. Total luminescence and synchronous fluores-cence spectroscopy enabled differentiation of edible from

lampante virgin olive oils and quantification of adultera-tion [12, 13]. The excitation‐emission fluorescence spectros-copy and three‐way methods of analysis were used to detectolive‐pomace oil adulteration in extra virgin olive oils at lowlevels (5%) [14]. Synchronous fluorescence spectroscopy wasused for detecting adulteration of extra virgin olive oil witholive oil [15].

Note that most of the published studies involvedapplication of only one spectroscopic technique at a time.Only very few papers compared performance of differentmethods in quantification of various aspects of olive oilquality. Namely, near‐ (NIR) andmid‐IR (MIR) andUV–Visspectroscopy were simultaneously used for authenticationand classification of olive oils [16, 17]. Alternatively, head‐space MS (electronic nose) in conjunction with UV–Vis andNIR spectroscopy was used for the same purposes [18].

The aim of present studies was to use electronic andvibrational spectroscopic techniques to characterize spectralproperties of representative samples of extra virgin, refinedand mild deodorized olive oils and quantify their blendscomposition.

The novelty of the present research lies in quantitativecomparison of the performance of the most commonspectroscopic techniques in adulteration detection of extravirgin olive oils. Our studies included the absorptionspectroscopy in the Vis, NIR, and MIR ranges, and theUV–Vis fluorescence spectroscopy. Spectroscopy was alwaysused in conjunction with multivariate calibration forquantification of the lower‐quality refined and mild deodor-ized olive oils in extra‐virgin olive oil. We also comparedperformance of two multivariate regression methods in oliveoil adulteration detection: partial least squares (PLS) andprincipal component regression (PCR).

2 Materials and methods

2.1 Sampling and mixture preparation

The extra virgin (EVO), mild deodorized (DO), and refined(RO) olive oils used for the preparation of blends weremixtures of several oils produced in 2009 in Italy. Thesamples and their chemical characteristics were available froma previous study [19]. In particular, the EVOwas provided bya local cooperative, while DO and RO were obtained by alocal refinery plant. Two sets of binary mixtures: EVO withDO and EVO with RO were prepared by adding to EVO theadulterant, DO or RO, respectively, at amounts in the rangeof 2.5–75 g/100 g, with 2.5 g step.

2.2 Spectral measurements

The MIR experiments were performed on a Spectrum 100FTIR spectrometer (Perkin Elmer) using the attenuated totalreflection (ATR) technique. Single beam spectra of the

2 K. Wójcicki et al. Eur. J. Lipid Sci. Technol. 2014, 116, 0000–0000


samples were collected and rationed against a backgroundof air. For each sample, the MIR spectra were recorded from4000 to 650 cm�1 by co‐adding 16 interferograms at aresolution of 4 cm�1.

The NIR and Vis absorption spectra of olive oils weremeasured using a Cary 5E UV Vis NIR spectrophotometer(Varian). The spectra of neat oil samples were measured inthe NIR region using a 2mm quartz cuvette. The spectra inthe Vis range were measured for the oil samples diluted inn‐hexane in a 10mm quartz cuvette.

The fluorescence spectra were obtained using a Fluorolog3–11 Spex‐Jobin Yvon spectrofluorometer. The excitationand emission slit widths were 2 nm. The acquisition intervaland the integration time were maintained at 1 nm and 0.1 s,respectively. A reference photodiode detector at the excita-tion monochromator stage compensated for the sourceintensity fluctuations. The individual spectra were correctedfor the wavelength‐dependent response of the system. Front‐face geometry was used for undiluted samples in a 10mmfused‐quartz cuvette.

The total fluorescence spectra were collected by measur-ing the emission spectra in the 260–700nm range with theexcitation in the 250–500nm range, with a 10 nm step.

The total synchronous fluorescence spectra were collectedby simultaneously scanning the excitation and the emissionmonochromators in the 250–700nm range, with a constantwavelength difference between them (Dl¼ 10–100nm, with10nm step). The synchronous fluorescence (SyF) spectrawere collected by simultaneously scanning the excitation andthe emission monochromators in the respective spectralrange, with a constant wavelength difference between them(Dl¼ 10nm). The fluorescence intensities were plotted infunction of the excitation wavelength.

Three spectra were recorded for each sample.

2.3 Data analysis

Principal component analysis was performed on the NIR andMIR spectra of oil samples. Three replicate spectra wereanalyzed for each of the oils.

Multivariate regressions were conducted on a set ofsamples including mixtures of EVO with DO and RO. Theblends were divided into calibration and prediction sets usingtwo different approaches: systematic and random. In bothcases, the calibration and prediction sample sets spannedthe entire range of adulteration concentrations and werecharacterized by similar mean values and SDs of adulterant.

In the first, systematic approach samples were sortedaccording to their adulterant concentration, and every thirdsample was included into the prediction set (n¼20), startingfrom 2.5 g/100 g for EVO–DO and from 5g/100 g forEVO–RO mixtures, respectively. The remaining samples(n¼ 42) constituted the calibration set. The two sample setswere characterized by mean values and SDs of adulterant(37.5�23) g/100 g and (37.5� 22) g/100 g, respectively, for

the calibration (calibration set No. 1) and prediction(prediction set No. 1) sets. These sets were used foroptimization of regression models using different pre-treatments methods.

In the second approach, all samples (n¼ 62) wererandomly divided into calibration (n¼42) and prediction(n¼ 20) using software build‐in algorithm. The mean valuesand SDs of adulterant were (37.2�22.1) g/100 g and(38.3�23.4) g/100 g, respectively, for the calibration (cali-bration set No. 2) and prediction (prediction set No. 2) sets.These sets were used to calculate regression models andprediction only for optimized preprocessing procedure.

The samples from the calibration set were used to buildregression models between spectral data and adulterantconcentration.

Two different methods of multivariate calibration, namelythe PCR and PLS regression, were used to establish thecalibration models between the spectra (X matrix) andthe concentration of adulterant – RO or DO oil – (Y matrix),in genuine EVO olive oil samples.

PCR is a two‐step procedure that first decomposes anX‐matrix by the principal component analysis, and then fitsa multivariate linear regression (MLR) model, using thePC scores instead of the original X‐variables as predictors.PLS models both the X‐ and Y‐matrices simultaneously tofind such latent variables in X that will best predict the latentvariables in Y [20]. The main differences between twomethods are that in PCR, the data are decomposed using onlyspectral information, while PLS analysis employs simulta-neously spectral and concentration data. Although a numberof studies indicated that performance of both methods issimilar, PLS is much more frequently used in spectralanalysis [21].

PCR and PLS analyses were performed on absorption Vis,NIR, and MIR spectra and SyF spectra measured atDl¼ 10nm. The calibration sample set (n¼42) was usedto obtain the respective regression models. The spectracalculated as averages of threemeasurements per sample wereused in the analysis. Regression was performed using singlespectral regions and the concatenatedXmatrix. In combiningdifferent spectra, the data were normalized to a unit vector inorder to give the same importance to the different spectralregions [22].

Different spectral regions and pre‐processing strategies ofthe spectral data (multiplicative scatter correction (MSC),smoothing, first and second derivative performed usingSavitzky–Golay derivation) were evaluated. Prior to analysis,theX data were columnmean‐centered. Full cross‐validationwas applied for all regression models. Cross‐validation is astrategy for validating calibration models based on systemati-cally leaving out groups of samples in the modeling, andtesting the left‐out samples in a model based on the remainingsamples.

The optimum number of PCR and PLS factors wasdetermined by plotting the root‐mean‐square error of cross‐

Eur. J. Lipid Sci. Technol. 2014, 116, 0000–0000 Spectroscopic techniques and chemometrics in analysis of blends of olive oils 3


validation (RMSECV) versus the number of factors anddetermining the minimum of the plot. The regression modelswere evaluated using the adjusted R2, the RMSECV, as themeasure of the prediction error of the model.

The obtained regression models were subject to externalvalidation by predicting the mixture composition of the oils inthe prediction set (n¼ 20) based upon their spectralinformation. The predicted values were compared to thereference values. The coefficient of determination (R2) andthe root‐mean‐square error of prediction (RMSEP) wereused as indicators of the models’ predictive ability. Therelative error of prediction (REP) was calculated as percent-age ratio of the RMSEP to the mean of the observed Y values.The quality of models was evaluated also by the ratio ofthe SD of reference data for the validation samples to theRMSEP, designated RPD. The RPD provides a means forstandardizing the RMSEP and evaluating the robustnessof the model [23].

The data analysis was carried out using Unscrambler 9.7software (CAMO, Oslo, Norway).

3 Results and discussion

3.1 Analytical characteristics of the olive oils

The characteristics of the EVO, RO, and DO oils used as theblend components have been studied in details previously [19]and are reported in Table S1 in Supplementary Material.

The parameters indicating the oil quality, free fatty acids,and peroxide value were in accordance with the official limitsfor EVO (0.67 g/100 g; 3.8meq O2/kg) and for RO (0.29 g/100 g, 1.9meq O2/kg) [19]. The DO oil was characterized byrelatively high contents of free fatty acids (0.54 g/100 g) andperoxide value (18.0meq O2/kg). In DO, a lower amount ofstigmastadienes (0.10mg/kg) was determined as compared tothe RO oil (5.17mg/kg) due to the fact that this oil was notsubjected to bleaching but only to a mild deodorization.

The contents of polar compounds were significantlyhigher for RO (4.75 g/100 g) and DO (4.38 g/100 g) oils thanfor EVO (2.40 g/100 g) as the consequence of higherhydrolytic and oxidative degradation of RO and DO ascompared to extra virgin oil. The TAG oligopolymers werenot detected in EVO oil. DO and RO oils contained,respectively, on average 0.05 g/100 g and 0.31 g/100 g of TAGoligopolymers and higher concentrations of oxidized TAGsand DAGs than the EVO oil [19].

3.2 Spectral characteristics of olive oils

3.2.1 MIR spectra

The MIR spectra of the studied oils are shown on Fig. 1.The spectra of oils are dominated by bands originating fromTAGs, the main components of oils, although other

components also contributed to the spectra. The spectralprofiles of all three oils, EVO, DO, and RO, are similar, andtheir visual inspection reveals no obvious variations infunction of the quality grade.

To detect any differences between spectral characteristicsin the MIR region, principal component analysis wasperformed on a matrix consisting of three replications ofspectra of the three oils. The projection of samples onto PC1versus PC2 coordinates shows that PC1 values differentiatethe oils according to the quality grade. The spectra of EVOand DO oils are rather similar, with more pronounceddifferences observed between these two oils and RO.Inspection of loadings for PC1 reveals the following bandsimportant for discrimination of oils: 1735 cm�1, 669 cm�1,and the fingerprint region at 900–1500 cm�1 with the mostintense band at 1106 cm�1.

The very intense band in MIR spectra at 1743 cm�1 arisesfrom the stretching vibrations of carbonyl group (C––O)present in glycerol‐fatty acids ester bond (COOR) oftriacyloglycerols. The absorption of the carbonyl group(C––O) of the free fatty acids present in olive oil appears asa shoulder in the low‐frequency region of this band [24, 25].

A

-0.03

0.00

0.03500 1000 1500 2000 2500 3000 3500 4000

Load

ings

PC1

500 1000 1500 2000 2500 3000 3500 40000.0

0.1

0.2

0.3 EVO DO RO

Abs

orba

nce

Wavenumber [cm-1]

B

-0.04 -0.02 0.00 0.02 0.04

-0.04

-0.02

0.00

0.02

0.04

RO

RO

RODO

DODO

EVO

EVO

PC

2 (2

2%)

PC1 (66%)

EVO

Figure 1. (A) Mid IR absorption spectra of extra virgin, deodorized,and refined olive oils. Loadings for the first component resulted fromPCA analysis of MIR spectra (top panel). (B) Score plot for the firstcomponent resulted from PCA analysis of MIR spectra.



Several absorption bands appear in the fingerprintregion, between 1500 and 900 cm�1. The band at 1460 arisesfromCH2 andCH3 scissoring vibrations, at 1378 cm

�1– from

CH3 bending vibration. The bands at 1238, 1160, 1119,and 1097 cm�1 originate from C–O stretching vibrations.The band characteristic of isolated trans double bonds ispresent at 965 cm�1, assigned to the C–H out‐of‐planedeformation [25].

The band at 723 cm�1 is assigned to the overlapping of theCH2 rocking vibration and the out‐of‐plane vibration ofcis disubstituted olefins [24].

The differences in the MIR spectra of three studied oilsmay reflect the differences in their chemical composition, inparticular, the presence of various amounts of free fatty acids.The EVO and DO oils contained considerably higheramounts of free fatty acids as compared to the RO [19],and these two oils were effectively differentiated from ROby PC1, which is correlated with bands characteristic tovibrations of the carbonyl group.

The very intense bands with maxima at 2924 and2852 cm�1 have only very minor contribution to the oildiscrimination. These bands are ascribed, respectively, toasymmetric and symmetric stretching vibrations of theCH2 groups of the fatty acid chains in triacyloglycerols [25].The stretching vibration of CH3 groups appears as theshoulder in this region (at 2962 and 2872 cm�1) [24]. Thebands of the stretching vibration of the ––C–H double bondsare observed at 3025 cm�1 for trans, and 3006 cm�1 for cisgroups [25]. The band of the first overtone of C––O in estergroups (3468 cm�1) is present at the high‐frequency end ofthe spectrum [25, 26]. Moreover, it was reported thatstretching vibrations of OH group of alcohols obtained asbreakdown products of hydroperoxides may be observedin the region of 3700–3400 cm�1 [26].

3.2.2 NIR spectra

The NIR spectra of oils exhibit broad overlapping bandsoriginating from overtones and combination tones of thefundamental vibrational modes, see Fig. 2.

Similarly to the MIR, the NIR spectra were also verysimilar for the three oils studied. Therefore, here the principalcomponent analysis was also performed on the matrixconsisting of the three replicate spectra of the three oils.The spectral range between 4000 and 4500 cm�1 thatcorresponds to the combinations of the C–H stretchingvibration with other vibrational modes [27] was excludedfrom the PCA analysis.

The differences in the spectral characteristics of variousoils are evident from the scores plot, see Fig. 2. PC1 explainsthe majority (82%) of the spectral variance and is positivelycorrelated to the spectral zone centered at 5869 cm�1. Thiszone corresponds to the high‐frequency slope of the band at5790 cm�1 in the NIR spectra. The first overtones of the C–Hstretching vibrations in methyl, methylene, and ethylene

groups correspond to a band with the two maxima at 5787and 5671 cm�1 [17]. It was reported that oleic acid absorbsat 5797 cm�1 and saturated and trans‐unsaturated TAGsabsorb at 5797 and 5681 cm�1 [17].

PC2 is correlated positively to the same zone as PC1 andalso (with higher loading values) to the region centered at5267 cm�1. The 5260 and 5178 cm�1 bands occurring in theoil spectra correspond to the second overtone of the C––Ostretching vibrations [28].

Both PC1 and PC2 are correlated to the region at4660 cm�1. The low‐intensity bands at 4659 and 4572 cm�1

correspond to the combination of C–H and C––O stretchingvibrations [17] and those at 4662 and 4596 cm�1 areassociated with the presence of the –CH––CH– doublebounds [28].

Small contributions to PC1 and PC2 are also obtainedfrom the low‐intensity bands originating from second

A

0.0

0.1

0.24000 5000 6000 7000 8000 9000 10000 11000 12000

PC2

0.00

0.04Load

ings

PC1

4000 5000 6000 7000 8000 9000 10000 11000 12000

0

1

2 EVO DO RO

Abso

rban

ce

Wavenumber [cm-1]

B

-0.6 -0.3 0.0 0.3 0.6

-0.6

-0.3

0.0

0.3

0.6

RORORO

DODODO

EVO

EVO

PC

2 (1

7%)

PC1 (82%)

EVO

Figure 2. (A) Near IR absorption spectra of extra virgin, deodorized,and refined olive oils. Loadings for the first component resulted fromPCA analysis of NIR spectra (top panel). (B) Score plot for the firstcomponent resulted from PCA analysis of NIR spectra.



overtones of the C–H stretching vibrations at around8261 cm�1 and C–H combinations with other modes (7180and 7075 cm�1) [17].

Similarly to theMIR, PCA analysis of theNIR spectra alsoallowed differentiation of EVO and DO oils from RO by thevalues of the PC1 component. Additionally, EVO isdifferentiated from RO and DO by the PC2 component.As already mentioned, EVO and DO differ from RO in freefatty acids content. Also, RO and DO oils contain higherconcentrations of polar compounds than EVO. Thesechemical differences should be reflected in NIR spectra.

The vibrational spectra of olive oils both in MIR and NIRregions resemble those obtained elsewhere. Although theentire range of spectra looks very similar for the studied oils,PCA analysis revealed differences that result from theirdifferent chemical composition.

3.2.3 VIS spectra

Figure 3 shows the absorption spectra of olive oils in n‐hexane.

The EVO oil absorption in the 450–520nm range isascribed to carotenoid pigments. The carotenoid bandoverlaps with the chlorophyll absorption at 380–450nm,while the characteristic band at 650–700nm originatesexclusively from the absorption of chlorophylls. The maincarotenoid pigments in olive oils are either b‐carotene orlutein, while the main pigment of the chlorophyll group ispheophytin a [29]. The EVO and DO oils exhibit similarintensities of the chlorophylls absorption bands, while theintensity of the carotenoid bands is lower in DO. Thisindicates thatmild deodorization does not lead to a significantdegradation of pigments. The absorption spectrum of the ROoil reveals only traces of pigment absorption, as these were

removed or degraded during refining. The absence ofpigments is characteristic for RO, therefore, these oils maybe differentiated fromEVO andDObased on their color. Thepigment content in olive oil is determined by several factorsincluding olive variety, ripeness, and geographical areas [29].Therefore, although differences observed in the absorbancefor studied samples of EVO and DO enabled discriminationof these particular oils, for the real‐life application thedifferent possible sources of pigment contents variabilitymay restrict usability of VIS spectra for differentiation ofEVO and DO oils.

3.2.4 UV–Vis fluorescence spectra

Total fluorescence spectra and total synchronous fluores-cence spectra of intact EVO, DO, and RO oils are shown inFig. 4.

The spectra were measured using front‐face geometry.The fluorescence properties depend on the quality grade ofolive oils. This observation is in accordance with thepreviously published data [30, 31].

The total fluorescence spectra of all of the oils studiedshow two characteristic bands. The short‐wavelength band inthe excitation range of 270–310nm and the emission range of300–360nm with the maximum at the excitation/emissionwavelengths (lexc/lem) of about 300/330 nm is ascribed to thetocopherol emission [32]. The emission of phenolic com-pounds may also be observed at the short‐wavelength side ofthis band. The long‐wavelength band is observed in theexcitation range of 300–480nm and emission range between660 and 700nm with its maximum at lexc/lem of about 400/680 nm. This band is ascribed to the pheophytin andchlorophyll emission [33]. This band has a significantlylower intensity in the RO as compared to the EVO and DOoils, in agreement with the low intensity of chlorophyll bandsobserved in the absorption spectrum of RO. The mostpronounced differences between the oil spectra are observedin the intermediate spectral zone. Indeed, only one bandappears in the spectrum of EVO in this region, with theexcitation at 320–390nm and emission at 440–580nm andthe maximum at lexc/lem of about 360/520 nm. An additionalband is observed in the spectrum of DO oil at about310–330nm in excitation and 370–490nm in emission, withthe maximum at lex/lem of 320/420 nm. On the other hand,the total fluorescence spectrum of RO is characterized bytwo intense bands in the intermediate region.

The total synchronous fluorescence spectra reflect thedifferences between oil fluorescence observed in the respec-tive total fluorescence spectra. The emission bands in thespectrum of EVO appear in the excitation region below310nm (tocopherol emission), 310–350nm, 350–380nm,and above 550 nm (chlorophyll and pheophytin emis-sion) [32]. An additional band is observed in the spectrumof DO between 320 and 370nm. On the contrary, the RO ischaracterized by a relatively weak band between 290–320nm,

400 500 600 700 8000.0

0.2

0.4

0.6

0.8

1.0 EVO

DO

RO

Abs

orba

nce

λ [nm]

Figure 3. Absorption spectra in the visible region of extra virgin,deodorized, and refined olive oil in n‐hexane, 1 cm optical pathlength.



an intensive, broad emission consisting of two bandsspreading to about 500 nm.

The chemical compounds associated with the emission inthe intermediate region have not been unambiguouslyidentified for olive oils so far. However, in several studies itwas suggested that the emission in this region is a result of oiloxidation [34, 35]. Interestingly, the characteristic emissionchanges in the zone characteristic for the oxidation productsare in agreement with the chemically determined polarcompounds content: the lowest for EVO, moderate for DO,and the highest for RO [19].

Figure 5 shows the synchronous fluorescence spectra ofthe EVO,RO, andDO, recorded with the emission‐excitationoffset (Dl) of 10 nm.

Because of the strong differences in the band intensity,these spectra are displayed on a logarithmic scale. The bandscorresponding to tocopherols and pheophytins appear withtheir maxima, respectively, at 301 and 666nm. An additionalband at 284 nm in the EVO spectrum may be ascribed to theemission of phenolic compounds [32]. The characteristicpatterns observed in the spectra in the 320–500nm zonereflect differences in the emission of different oils observed inthe total fluorescence spectra ascribed to the oxidationproducts.

The electronic absorption and fluorescence spectra ofolive oils are more selective than the vibrational spectra,providing information about minor olive oil components.

Both absorption and fluorescence spectra contain informa-tion about chlorophylls content. Additionally, absorptionspectra allow determination of carotenoids content. Themostselective method is the fluorescence spectroscopy, revealingpresence of phenols, tocopherols, and oxidation products.The oils of different quality grades differ in the intensity ofbands ascribed to the particular minor constituents. It seems

A B C

D E F

300 350 400 450 500 550 600 650 700250

300

350

400

450

500

λem [nm]

λ exc [n

m]

300 350 400 450 500 550 600 650 700250

300

350

400

450

500

λem [nm]λ ex

c [nm

]300 350 400 450 500 550 600 650 700

250

300

350

400

450

500

λem [nm]

λ exc [n

m]

0

1.140E5

2.280E5

3.420E5

4.560E5

5.700E5

6.840E5

7.980E5

9.120E5

1.026E6

1.140E6

If

300 350 400 450 500 550 600 650 700

20

40

60

80

100

Δλ exc [nm]

Δλ [

nm]

300 350 400 450 500 550 600 650 700

20

40

60

80

100

Δλ exc [nm]

Δλ [n

m]

300 350 400 450 500 550 600 650 700

20

40

60

80

100

Δλ exc [nm]Δλ

[nm

]

0

1.140E5

2.280E5

3.420E5

4.560E5

5.700E5

6.840E5

7.980E5

9.120E5

1.026E6

1.140E6

If

Figure 4. Total fluorescence (A–C) and total synchronous fluorescence (D–F) spectra (Dl¼ 10–100nm) of extra virgin (A, D), deodorized(B, E) and refined (C, F) olive oils, measured for neat oil samples using front‐face geometry.

300 400 500 600 700

100000

1000000

1E7

1E8

EVO

DO

RO

Fluo

resc

ence

inte

nsity

[a.u

.]

λ [nm]

Figure 5. Synchronous fluorescence spectra (Dl¼ 10 nm) of extravirgin, deodorized, and refined olive oils, measured for neat oilsamples using front‐face geometry, note the logarithmic scale usedfor the fluorescence intensity.



that the most characteristic qualitative changes are observedin the fluorescence spectra, where additional bands appearedfor the lower‐quality oils, DO and RO, as compared to EVO.

3.3 Regression models for olive oil mixtures

In order to quantitatively evaluate the composition of oilmixtures based on their spectral characteristics, multivariateregression methods were used, namely the PCR and thePLS regression.

Different types of mathematical pre‐processing wereapplied to the spectra before building the models. Acharacteristic shift was observed in the sets of absorptionspectra recorded in the NIR, MIR, and VIS regions,indicating a scattering effect. Therefore, the pre‐treatmentmethods in these regions included MSC, MSC and the firstderivative, and MSC and the second derivative calculation.As no scattering was detectable in SyF, the first and thesecond derivatives were used in the analysis without scattercorrection.

Complete spectra were analyzed as the first step in allspectral regions. Next, specific sub‐regions were chosen,relying on the regression coefficients obtained for thecomplete spectra and the chemical information containedin the specific sub‐regions. The detailed characteristics ofPLS and PCR models for individual spectral ranges andconcatenated spectra are presented in Tables S2–S6 inSupplementary Material section. The characteristics of bestcalibration models obtained for each spectroscopic techni-ques are shown in Table 1. Note that all of the PLS and PCRmodels underline the correlation between the spectralcharacteristics and the composition of oil mixtures.

The results of the regression analysis of MIR spectra aresummarized in Supplementary Table S2. In the MIR region,both of the best regression models, PLS and PCR, wereobtained for the analysis of the MSC and first‐derivativepreprocessed spectra in the fingerprint region (1500–900 cm�1). This spectral range also significantly contributedto the oil differentiation. Interestingly, the regression analysisin the 4000–2500 cm�1 range gave similarly good results(with a higher number of latent variables), although this rangewas inessential for oil discrimination.

The results of the regression analysis of NIR spectra aresummarized in Supplementary Table S3. The best PLSmodel inNIR region was obtained using the 6150–4500 cm�1

range withMSC preprocessing; this model required six latentvariables. The best PCR models were obtained in the samespectral range and involved MSC and first‐derivativepreprocessing of spectra, this model also required six latentvariables. This model showed a performance marginallysuperior than that of the best PLS model. Similarly to MIR,the best results of the regression analysis of the NIR spectrawere also obtained for the spectral zone important for thedifferentiation of oils (6150–4500 cm�1).

The results of the regression analysis of Vis spectra aresummarized in Supplementary Table S4. The spectra of oilsin the Vis region are relatively simple, showing the bandscorresponding to the carotenoids and chlorophylls. Both ofthe best regression models, PLS and PCR, were obtained forraw spectra in the same spectral range of 350–550nm, andequally required six latent variables. The PCR model showeda slightly better performance than that of the best PLSmodel.

The synchronous fluorescence spectra of oils recorded at10 nm offset between excitation and emission wavelengths

Table 1. Partial least squares (PLS) and principal component regression (PCR) statistics for calibration models and prediction of adulterantconcentration using different spectra.a)

ModelSpectral region(cm�1) Pretreatment

Cross validation Prediction

LV R2 RMSECV R2 RMSEP REP RPD

MIR PLS 1500–900 MSCþ 1st derivative 3 0.985 2.9 0.990 2.1 5.6 10.9MIR PCR 1500–900 MSCþ 1st derivative 3 0.981 3.2 0.987 2.5 6.7 9.2NIR PLS 6150–4500 cm�1 MSC 6 0.977 3.5 0.967 3.9 10.4 5.9NIR PCR 6150–4500 cm�1 MSCþ 1st derivative 6 0.979 3.4 0.984 2.7 7.2 8.5VIS PLS 350–550nm no 6 0.980 3.3 0.972 3.6 9.6 6.4VIS PCR 350–550nm no 7 0.982 3.1 0.974 3.5 9.3 6.6SyF PLS 280–690nm 1st derivative 5 0.988 2.6 0.981 2.9 7.7 7.9SyF PLS 500–690nm 1st derivative 5 0.989 2.4 0.981 2.9 7.7 7.9SyF PCR 500–690nm No 5 0.988 2.5 0.979 3.0 8.0 7.7IRþNIRþVISþSyF PLS Reduced No 2 0.956 4.9 0.958 4.4 11.7 5.0IRþNIRþVISþSyF PLS Reduced No 4 0.967 4.2 0.964 4.1 10.9 5.4

LV, number of latent variables; R2, determination coefficient; RMSECV, RMSEP, root mean square error of cross validation or prediction,respectively (g/100 g); REP, relative error of prediction calculated as percentage ratio of the RMSEP to the mean of the observed Y values;RPD, ratio of the SD of reference values to RMSEP.Reduced range: IR (1500–900 cm�1), NIR (6150–4500 cm�1), Vis (350–500 nm), SyF (500–690 nm).a)Calibration set No. 1 and prediction set No. 1.



were used for the regression analysis, see Supplemen-tary Table S5. The best PLS regression parameters wereobtained for the analysis of the first derivatives of spectra inspectral zone of 500–690nm, corresponding to pheophytinemission, and required five latent variables. The best PCRmodel was obtained in the same spectral range but for rawspectra, with similarly five latent variables. This modelshowed a slightly worse performance than that of the bestPLS model.

The analysis of concatenated spectra was performed for allof the studied spectral regions (MIR, NIR, VIS, andfluorescence) and different spectral combinations, including:MIR and NIR, NIR and Vis, Vis and fluorescence, MIR andfluorescence, see Supplementary Table S6.

The regression models were analyzed for the matrix of theentire spectra and the matrix of selected regions for whichthe best regression models were obtained in the analysis ofseparate spectra. All of the PLS and PCR regression modelsshowed inferior parameters as compared to models obtainedfor separate analyses of the corresponding spectral zones.

3.4 Quantification of extra virgin olive oil adulteration

To check the applicability of the calibration models forprediction of EVO oil adulteration, the best PLS and PCR

models were utilized for quantification of lower‐quality oil(DO or RO) contents in the prediction set (No. 1) consistingof 20 samples not included in the calibration. The sampleswere subjected to the same mathematical pretreatmentas those in the calibration set for the particular model,see Table 1.

Figure 6 shows the relationship between the concentrationof the adulterant predicted by the respective PLS regressionmodel and its actual value.

Table 1 shows that the RMSEP were in range of2.1–3.9 g/100 g, which corresponds to the relative errorof 5.6–10.4%. To evaluate the model quality, the ratioof the SD of the Y data set to RMSEP (RPD) was alsoused [23]. According to [23], models with RPD values of5–10 are adequate for quality control, while those withRPD greater than 10 have an excellent predictive ability.The RPD of all of the models were in the range 5.9–10.9,which indicates their good capability to predict theadulterant concentration, see Table 1.

To test the effect of distribution of samples incalibration and prediction sets on the model performance,we perform the PLS and PCR analysis using sets (No. 2)with randomly divided samples. The optimal pretreatmentsfor particular spectral ranges were applied prior to analysis,Supplementary Table S6. Some effect, but only minor

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70VIS, without pretreatment, 350-710 nm

Pre

dict

ed c

once

ntra

tion

(g/1

00g)

Actual concentration (g/100g)0 10 20 30 40 50 60 70

0

10

20

30

40

50

60

70SyF ∆λ=10 nm, 1st derivative, 280-690 nm

Pre

dict

ed c

once

ntra

tion

(g/1

00g)

Actual concentration (g/100g)

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70NIR, MSC, 4500-6150 cm-1

Pre

dict

ed c

once

ntra

tion

(g/1

00g)

Actual concentration (g/100g)0 10 20 30 40 50 60 70

0

10

20

30

40

50

60

70 IR MSC+1st derivative, 1500-900 cm-1

Pre

dict

ed c

once

ntra

tion

(g/1

00g)

Actual concentration (g/100g)

Figure 6. Predicted versus actual adulterant concentration obtained using different PLS calibration models.



and non‐systematic on models performance was observed.The relative errors and RPD of all of the models were inthe range, respectively: 6.8–12.0% and 5.1–9.0, whichindicates still good predictive ability.

The performance of PLS and PCR methods was similarin these studies; for some models the optimal numberof latent variables was higher for PCR than for PLS.

4 Conclusions

This study showed that studied extra virgin olive oil may bediscriminated from refined and mild deodorized olive oil bycomparing their spectral properties. Vibrational spectroscopyboth in MIR and NIR provides similar, most comprehensiveinformation about chemical composition, but spectra aredominated by the main oil components. Electronic spectros-copy is more selective and therefore restricted only toselected components. The most pronounced differenceswere observed in electronic spectra – VIS absorption andUV–VIS fluorescence. These differences are a consequenceof different contents of minor components. Additionally,in the fluorescence spectra the characteristic emissiontentatively ascribed to the oxidation or degradation productscontributes significantly to discrimination of oils. Differencesin the vibrational spectra (NIR and MIR) were muchless pronounced. However, the PCA analysis revealed somecontribution of bands characteristic for vibrations of carbonylgroup to the discrimination of oils.

The analysis of the respective spectra using multi-variate regression methods enabled quantification ofadulterant concentration with similar performance forall of the studied spectral techniques. Simultaneousanalysis of the combined spectra gave somewhat worseresults than the analysis of separate spectra. The tworegression techniques tested, PCR and PLS, gavesimilar results.

The grants NN312428239, 2010‐2013 from the Polish Ministryof Science and Higher Education is gratefully acknowledged.

The authors have declared no conflict of interest.

References

[1] Garcia‐Gonzalez, D. L., Aparicio, R., Research in olive oil:Challenges for the near future. J. Agric. Food Chem. 2010, 58,12569–12577.

[2] Arvanitoyannis, I. S., Vlachos, A., Implementation ofphysicochemical and sensory analysis in conjunction withmultivariate analysis towards assessing olive oil authenti-cation/adulteration. Crit. Rev. Food Sci. Nutr. 2007, 47,441–498.

[3] Vetter, W., Schroder, M., Lehnert, K., Differentiation ofrefined and virgin edible oils by means of the trans‐ and cis‐

phytol isomer distribution. J. Agric. Food Chem. 2012, 60,6103–6107.

[4] Perez‐Camino, M. C., Moreda, W., Mateos, R., Cert, A.,Determination of esters of fatty acids with low molecularweight alcohols in olive oils. J. Agric. Food Chem. 2002, 50,4721–4725.

[5] Perez‐Camino, M. C., Cert, A., Romero‐Segura, A., Cert‐Trujillo, R., Moreda, W., Alkyl esters of fatty acids a usefultool to detect soft deodorized olive oils. J. Agric. Food Chem.2008, 56, 6740–6744.

[6] Saba, A., Mazzini, F., Raffaelli, A., Mattei, A., Salvadori, P.,Identification of 9(e),11(e)‐18:2 fatty acid methyl ester attrace level in thermal stressed olive oils by GC coupled toacetonitrile CI‐MS and CI‐MS/MS, a possible marker foradulteration by addition of deodorized olive oil. J. Agric. FoodChem. 2005, 53, 4867–4872.

[7] Aparicio, R., Aparicio‐Ruiz, R., Authentication of vegetableoils by chromatographic techniques. J. Chromatogr. A 2000,881, 93–104.

[8] Maggio, R. M., Cerretani, L., Chiavaro, E., Kaufman, T. S.,Bendini, A., A novel chemometric strategy for the estimationof extra virgin olive oil adulteration with edible oils. FoodControl 2010, 21, 890–895.

[9] Fragaki, G., Spyros, A., Siragakis, G., Salivaras, E., Dais, P.,Detection of extra virgin olive oil adulteration with lampanteolive oil and refined olive oil using nuclear magneticresonance spectroscopy and multivariate statistical analysis.J. Agric. Food Chem. 2005, 53, 2810–2816.

[10] Mignani, A. G., Ciaccheri, L., Ottevaere, H., Thienpont, H.et al., Visible and near‐infrared absorption spectroscopyby an integrating sphere and optical fibers for quantifyingand discriminating the adulteration of extra virgin olive oilfrom Tuscany. Anal. Bioanal. Chem. 2011, 399, 1315–1324.

[11] Torrecilla, J. S., Rojo, E., Dominguez, J. C., Rodriguez, F.,A novel method to quantify the adulteration of extra virginolive oil with low‐grade olive oils by UV–VIS. J. Agric. FoodChem. 2010, 58, 1679–1684.

[12] Poulli, K. I., Mousdis, G. A., Georgiou, C. A., Classificationof edible and lampante virgin olive oil based on synchronousfluorescence and total luminescence spectroscopy. Anal.Chim. Acta 2005, 542, 151–156.

[13] Poulli, K., Mousdis, G., Georgiou, C., Rapid synchronousfluorescence method for virgin olive oil adulteration assess-ment. Food Chem. 2007, 105, 369–375.

[14] Guimet, F., Ferré, J., Boqué, R., Rapid detection of olive–pomace oil adulteration in extra virgin olive oils from theprotected denomination of origin “Siurana” using excitation–emission fluorescence spectroscopy and three‐way methodsof analysis. Anal. Chim. Acta 2005, 544, 143–152.

[15] Dankowska, A., Malecka, M., Application of synchronousfluorescence spectroscopy for determination of extra virginolive oil adulteration. Eur. J. Lipid Sci. Technol. 2009, 111,1233–1239.

[16] Casale, M., Oliveri, P., Casolino, C., Sinelli, N. et al.,Characterisation of PDO olive oil Chianti Classico by non‐selective (UV–Visible, NIR and MIR spectroscopy) andselective (fatty acid composition) analytical techniques. Anal.Chim. Acta 2012, 712, 56–63.

[17] Sinelli, N., Casale, M., Di Egidio, V., Oliveri, P. et al.,Varietal discrimination of extra virgin olive oils by near andmid infrared spectroscopy. Food Res. Int. 2010, 43, 2126–2131.



[18] Casale, M., Casolino, C., Oliveri, P., Forina, M., Thepotential of coupling information using three analyticaltechniques for identifying the geographical origin of liguriaextra virgin olive oil. Food Chem. 2010, 118, 163–170.

[19] Caponio, F., Summo, C., Bilancia, M. T., Paradiso, V. M.et al., High performance size‐exclusion chromatographyanalysis of polar compounds applied to refined, milddeodorized, extra virgin olive oils and their blends: Anapproach to their differentiation. LWT – Food Sci. Technol.2011, 44, 1726–1730.

[20] Wold, S., Sjöström, M., Eriksson, L., PLS‐regression: Abasic tool of chemometrics. Chemometr. Intell. Lab. 2001, 58,109–130.

[21] Wentzell, P. D., Montoto, L. V., Comparison of principalcomponents regression and partial least squares regressionthrough generic simulations of complex mixtures. Chemo-metr. Intell. Lab. 2003, 65, 257–279.

[22] Dupuy, N., Galtier, O., Ollivier, D., Vanloot, P., Artaud, J.,Comparison between NIR, MIR, concatenated NIR andMIR analysis and hierarchical PLS model. Application tovirgin olive oil analysis. Anal. Chim. Acta 2010, 666, 23–31.

[23] Huang, Y., Rogers, T. M., Wenz, M. A., Cavinato, A. G.et al., Detection of sodium chloride in cured salmon roe bySW‐NIR spectroscopy. J. Agric. Food Chem. 2001, 49, 4161–4167.

[24] Vlachos, N., Skopelitis, Y., Psaroudaki, M., Konstantinidou,V. et al., Applications of fourier transform‐infrared spectros-copy to edible oils. Anal. Chim. Acta 2006, 573–574, 459–465.

[25] Guillén, M. D., Cabo, N., Infrared spectroscopy in the studyof edible oils and fats. J. Agric. Food Chem. 1997, 75, 1–11.

[26] van de Voort, F. R., Sedman, J., Russin, T., Lipid analysis byvibrational spectroscopy.Eur. J. Lipid Sci. Technol. 2001, 103,815–826.

[27] Pereira, A. F. C., Pontes, M. J. C., Neto, F. F. G., Santos,S. R. B. et al., NIR spectrometric determination of qualityparameters in vegetable oils using iPLS and variableselection. Food Res. Int. 2008, 41, 341–348.

[28] Woodcock, T., Downey, G., O’Donnell, C. P., Confirmationof declared provenance of european extra virgin olive oilsamples by NIR spectroscopy. J. Agric. Food Chem. 2008, 56,11520–11525.

[29] Moyano,M. J., Heredia, F. J.,Meléndez‐Martínez, A. J., Thecolor of olive oils: The pigments and their likely healthbenefits and visual and instrumental methods of analysis.Compr. Rev. Food Sci. Food Saf. 2010, 9, 278–291.

[30] Poulli, K. I., Mousdis, G. A., Georgiou, C. A., Synchronousfluorescence spectroscopy for quantitative determination ofvirgin olive oil adulteration with sunflower oil. Anal. Bioanal.Chem. 2006, 386, 1571–1575.

[31] Kyriakidis, N. B., Skarkalis, P., Fluorescence spectrameasurement of olive oil and other vegetable oils. J. AOACInt. 2000, 83, 1435–1439.

[32] Sikorska, E., Khmelinskii, I., Sikorski, M., in: Boskou, D.(Ed.), Olive Oil – Constituents, Quality, Health Properties andBioconversions, InTech, Croatia 2012, pp. 63–88.

[33] Diaz, T. G.,Meras, I. D., Correa, C. A., Roldan, B., Caceres,M. I. R., Simultaneous fluorometric determination ofchlorophylls a and b and pheophytins a and b in olive oilby partial least‐squares calibration. J. Agric. Food Chem.2003, 51, 6934–6940.

[34] Poulli, K. I., Mousdis, G. A., Georgiou, C. A., Monitoringolive oil oxidation under thermal and uv stress throughsynchronous fluorescence spectroscopy and classical assays.Food Chem. 2009, 117, 499–503.

[35] Tena, N., Garcia‐Gonzalez, D. L., Aparicio, R., Evaluationof virgin olive oil thermal deterioration by fluorescencespectroscopy. J. Agric. Food Chem. 2009, 57, 10505–10511.



Spectroscopic techniques and chemometrics in analysis of blends of extra virgin with refined and...

Documents

Transcript of Spectroscopic techniques and chemometrics in analysis of blends of extra virgin with refined and...