Developments in the detection of adulteration of olive oil

Review The authenticity of products labelled as olive oil is of great importance from the standpoints of both commercial value and health aspects. Over the years, a high degree of sophistication has evolved in chromatographic methods for the analysis of both major and minor components of oils and fats. At the same time, spectroscopic methods are emerging as polential tools for rapid screening of samples for the detection of adulteration. However, the complexity and intrinsic variability of biological samples such as olive oil and its potential adulterants demands the application of multivariate calibration or pattern-recognition techniques to aid interpretation of the data obtained using these instrumental methods. Combination of the techniques of analytical chemistry and chemometrics is mandatory for unequivocal identification and quantification of the adulteration of olive oii.

Developments in the detection of adulteration of olive oil Eunice Li-Chan

'Adulteration has been a problem in the oil and fat trade for a long time. It is sometimes deliberate, sometimes accidental. "4.~ In addition to the commercial impact of adulteration of edible oils by other cheaper oils. th~ harsh realities of health implications were evident in the numerous deaths and illnesses in the case now known as the 'Spanish Toxic Syndrome' or 'Toxic Oil Syndrome' (see Glossary) 6. In countries that proOlive oil has gained in popularity in recent years. This duce olive oil, such as Greece. Italy, Spain and other trend can be attributed not only to its superior flavour Mediterranean countries, adulterants in virgin olive oil but also to reports of potential health benefits, including may include refined olive oil (obtained by alkali refining, a specific reduction in plasma non-high-density lipo- bleaching ~,t~ddeodorization), olive residue (obtained by protein (non-HDL) cholesterol levels while HDL chol- solvent extraction and refining of the pomace or pit esterol and triaeylglyeerol levels remain unchanged I. residues remaining after physical expression of virgin Due to this demand, olive oil commands a high price on oil) or a synthetic or esterified product (produced by the market, and a clear identification of the purity of reaction of low-grade olive oil or olive oil by-product with glycerol) 7. In countries that import olive oil, olive oil products is therefore necessary. The International Olive Oil Council 2 gives the tbllow- including Canada and the USA, additional potential ing definitions to classify olive oil products: 'Virgin adulterants include seed oils that are locally available olive oil is the oil obtained from the fruit of the olive and less expensive 8. tree solely by mechanical or other physical means under This paper presents an overview of the current status conditions that do not lead to alterations in the oil. The and recent trends in improving methodologies for oil has not undergone any treatment other than washing, detecting the adulteration of olive oil. Examples of decantation, centrifugation and filtration. Olive oil or specific analytical tnethods are presented with two trends pure olive oil (or IOO% pure olive oil) is the oil consist- in mind: developments in instrumental methods of ing of a blend of virgin olive oil fit for consumption as it analysis, particularly those based on spectroscopy and chromatography; and the impact of applying computeris and refitted olive oil.' Within the class of virgin olive oils there exists a wide aided techniques to improve the sensitivity and accurange of qualities and types, which fall into two main racy of these instrumental methods for detecting adultercategories: the edible oils, which are high-quality oils ation. for human consumption; and the lampante oils, which are unsuitable for consumption without further refining Overview of methods due to excessive acidity or defective organeteptic charac- Current status of adulteration methodology teristics. Edible oils range from the extra-virgin olive Various methods have been suggested to detect the oils (which are permitted a maximum acidity as oleic adulteration of olive oil, either with other olive oil prodacid of 1 g per I00 g) to the ordinary or semifine-quality ucts or with different seed oils. Traditional tests based olive oils (maximum acidity of 3 g per I00 g). Oils may on physical and chemical constants include determibe further classified as "elemental' (from a single var- nation of the iodine value, saponification value, density, iety) or 'commercial' (from several varieties growing in viscosity, ultraviolet (UV) absorbance, fluorescence and the region), leading to potentially large variability in the refractive index, as well as a variety of colorimetric compositional characteristics of even authentic virgin reactions. A comprehensive review of these methods olive oils 3. was presented by Gracian Tous ~, who summarized the physical and chemical characteristics of virgin olive oil EuniceLi-Chanis at The Universityof BritishColumbia,Departmentof Food (Table I). Unfortunately, it is fairly easy to prepare Science, 6650 North West Marine Drive, Vancouver, BritishColumbia, fraudulent mixtures, even with high proportions of adulCanada V6T 1Z4. terants, that have physical or chemical properties that

Trends in Food Science & Technology January1994 [Vol. 5]

~,~;,~.~EI,~,,,,,,s~,e,~,,Ud n,~4 .'~.4~4~0:00

3

GlossaHy Coul~ Olive oils: Mixtures of virgin and refined olive oil. Curie poinl: Temperature of transition at which ferromagnetic substances lose their ferromagnetism and become paramagnelic. The 'Curie point' temperature is usually lower than the melting point. Equival~tnt Cai'l~,n Number (ECN): EqualsCN-2n, where CN is the sum of the carbon numbers and n is the sum of the double bonds on the fatty acyl groupsof triacylglycero! species. Multi,lariate methods: Methods seeking to clefine functional relation:ihips between and among various measured variables x~, x,.... x, and target variables y~, y~.... yj. Multivariate calibration method~i use selectfrd x variablesor their combinations to predict the target y variables.

OOO, I~10, Ott, Ptt, ttt: Triacylglycerol species containing the fatty acyl groups indicated by their symbols, O, P, L (oleoyl, palmitoyl, linoleoyl, respectively),with no regard to their specific position,l;on the glycerol moiety. Supervi.~edmethods:Chemometric methodsthat classify variaoles or samples into groups that are known a priori. A 'training' set comgosed of data of known origin or predeterminedclass is used to construct models. Cross-validation procedures may be used in which the class models are repeatedly reconstructed by excluding samples in the training set one at a time, for use as unknowns in model evaluation. The models are then used to classify samplesin an unknown set.

Toxic Oil Syndrome: Mass food poisoning outbreak in Spain in 1981 re.~;ultingin hundreds of deaths and thousands of cases of perman~Int incapacitance.Toxic oil Syndromehas been associated with the, consumption of aniline-denatured rapeseedoil that had been vai!iouslymixed with very poor quality olive oil, refined olive residue oil, other liquid vegetable oils and/or animal fats. However, the identity of the preciseetioiogical agent is still unknown. UnJvariate methods: Methods seeking to define tile functional relationship or prediction ability between a single measured, variable x and a targetvariable y. i i Unsupervised methods: Chemometric methods that are used l for the detection of basic structure or patterns in complex data [ sets, to reveal relationships between samples and among! variables. Clustering of variables or sarnples into distinct groups rnay result from the analysis,but no prior knowledge of classesor groups i$ required or assumedin unsupervisedmethods, il fall within the limits established as characteristic for olive oil. Compared with traditional teMt;, which measure a chcmic:al ~r phy:;ical ch,atacteristic in the whole oil sample, more delinitive information can be obtained by fract:ionation of the components by chromatographic separation, followed by quantitative determination of fatty acid. triacylglycerol, sterol or tocol compositions. The majority of work on olive oil adulleration in recent years has concentrated on the applic~ation or modification of high-performance liquid chromatography (HPLC) and gas chromatography (GCI methods""'. Official methods for the analysis of fats and oils. including specific methods for the detection ef olive oil adulteration, are regularly reviewed by the International Olive Oil Council as well as in General Referee re4

ports to the Committee on Foods, which are published in the journal of A O A C International (formerly the Association of Official Analytical Chemists). The following summaries taken from the 1992 (Ref. 9) and 1993 (Ref. I0) General Referee reports illustrate some trends in olive oil adulteration over the past two years. A liquid chromatography (LC) study of olive oil triacylglycerols was conducted by the Associate Referee on olive oil adulteration (E. Fedeli) and colleagues II to determine oil quality and to observe changes produced by enzymatic activity and industrial treatments or refining; UV detection at 215, 230 and 268 nm was used to monitor conjugated isomers and to determine changes caused by auk.oxidation or by refining. Nine laboratories were involved in a collaborative study of a reversedphase LC method for the analysis of olive oil triacylglycerols and mixtures of olive oil and vegetable oil triacylglycerols. The tnethod was reported to be especially useful for detecting adulteration by small quantities of linoleate-rich vegetable oils such as sunflower oil or soybean oil. since olive oil contains substantially lower contents of trilinolein (<0,5%) L'. The IUPAC (International Union of Pure and Applied Chemistry) Commission on Oils. Fats and Derivatives also completed a study of an LC method for the determination of triacylglycerols in vegetable oils in terms of their partition numbers ~-'. The AOAC General Referee recommended adoption as official first action of the IUPAC method for LC determination of triacylglycerols in vegetable oils 'J. European Community (EC) regulations specify a maximum trilinolein concentration of 0.5% in olive oils, and the LC determination would be useful for monitoring this. However, Proto b~ reported that of nine olive oil samples analysed, two Tunisian samples did not comply with this tolerance, while some samples of sunflov,'cr, safflower and hazelnut oils had trilinolein contents sufficiently low to permit the aduheration of olive oil without exceeding the tolerance for trilino!ein. Proto t4 thus concluded that additional tests are required for detection of adulteration of olive oi! with these oils. The detection of refined olive oil in virgin oil may be accomplished by measuring the sterol content. For example, erythrodiol levels are much higher in solventextracted refined olive oils than in virgin olive oils. A coupled LC-GC method was reported for the determination of fl-ee erythrodiol, as well as free and esterified .s~.crols and wax esters in olive oil and other vegetable oils ~s. GC methodology was reported for the detection of stigmasta-3,5-diene, which is produced by the dehydration of I3-sitosterol during refining but is not present in significant quantities in virgin olive oil "u" . Other methods mentioned in the 1992 and 1993 reports include t~C nuclear magnetic resonance (NMR) spectroscopy for the control of olive oil quality ~7, GC analysis of triacylglycerols to determine the presence of high-oleic seed oils in olive oil L~, and a spectrophotofluorimetric analysis for the detection of virgin olive oil in refined oil ~'. Trends in Food Science& TechnologyJanuary 1994 IVol. 51

Table I. Composition of virgin olive oil" What are the trends for improving detection of adulteration? Although chromatographic techniques offer the possibility for high-resolution separation of components of olh, e ail to aid in the identification and detection of adulteration, these methods are often time-consuming. For example, several steps may precede GC analysis, including saponification or enzymatic hydrolysis, preliminary cleanup using thin-layer or other chromatograpt'y, and derivatization to yield volatile constituents such as fatty acid methyl esters. Spectroscopic methods have the advantage of simple or no sample preparation or pretreatment. However. since the resulting spectra are based on contributions from many possible constituents in the oil sample, clearcut interpretation lbr the purposes of detecting low levels of adulteration or for quantifying adulteration may be diflicult. Two different approaches may be used to resolve this problem. Firstly, preliminary fractionation or cleanup of the oil may be carried out to remove components that cause interference in the spectra. Secondly. computer-aided pattern-recognition techniques rnay be used to distinguish between the contributions of differera cornponents. Inaprovenaent in spectral o r chronlatographic resolution and signal-to-noise ratio may also be achieved using computer-aided techniques such as Fourier deconwflution and m a x i m u m likelihood smootLing and deconvolution:". The past ten years have seen ever-increasing use of statistical and mathernatical methods in chemistry, a discipline known as chemometrics. This growth in chemometrics has been facilitated at least in part by the availability of relatively inexpensive yet powerful personal computers. One of the widely studied areas of chemometrics is pattern recognition, including both supervised and unsupervised methods. The supervised methods include SIMCA, UNEQ, ALLOC, rule-building expert systems and, recently, neural networks, while unsupervised techniques include cluster analysis and principal component analysis. Mult!variate calibration techniques make it possible to build "intelligent" analytical instruments that give quantitative, reliable determination of valuable intbrmation from high-speed+ but highly nonselective input data-'L Haaland-'-" stated that multivariate calibration methods are beginning to have a major impact on the quantitative analysis of infrared (IR) spectral data. improving the precision, accuracy, reliability and applicability of IR spectral analyses relative to the nlore conventional univariate methods of data analysis. Similar improvements in other spectral as well as chromatographic analyses are also evident from the literature. Rather than attempting to lind and use only or, e isolated l+eature in the analysis of spectral data. muhivariate methods derive their power from the simultaneous use of muhiple intensities (i.e. multiple variables) in each spectrum. Thus, the problem of spectral interferences can be reduced with the use of any one of a number of multivariate calibration methods. These ntethods include classical least squares (CLS; or the K-matrix nlethod), inverse least lren(b, in Food Scient o & Technology January 1094 IVol. 5]

Component Fatty acids": Oleic l inoM( Palmitoleit Palmifit Steark Mvri:..fic Linolerfi(: Arachidic gehenic Ul~Sal)onifiable malter exlratle(I I)~, light i)elroleunl LJnsalx)nifiable matter extracted by dieth,,I ether Total hvdro¢ arllons Squalerle Slerols TriterF,enic alcohnls Chlorophyll Total carotonoids (expressedas [3-carotene~ (~-Tocophen;l

Range

fl 1.0-8.L0",, i.'~-20.0% 0.'~- LO% 7.3 18.0%

0.:+- LO",, Not deterred O.1-0.6'!,, O,l-0.8'h. trat +,+-0.8";, t).

{ - 1.4",,

0.6- 2.0'. O.125-0.750". O.12 ~- 0.700",, O.125-0.250'!,,

-gilt} i)pm 0.6-2.2 ppnl 0.6-9.5 ppm

175-200 l)pm

' Ad,lptedIt(ira Rel. " rh~.ran~.,,sill indi~,idual latin,at i(Is a:e t'xpre,,ud a, a pertenrageof the tot.l) hillyd( J({~ [

squares (ILS: also known as multiple linear regression, MLR; or the P-matrix method), tile Q-matrix method, cross correlation, Kalman filtering, partial h-+ast squares (PLS) and principal component regression (PCR). The latter two methods have probably been the most popular and have shown the greatest range of applicability within the last few years-'L Application of neural networks (also known as artificial or computational neural networks;) is also gaining in popularity:'. h has been proposed that well-characterized data sets be archived and provided to developers of chemometric methods, to serve as test data where comparisons with prior work on the same data are possible. In this context, Hopke and Massart -'~ described several reference data sets for chemometrical nlethods testing; these are available for use with the permission of the original generators of the data. One of the sets currently available incorporates the data of Forina and co-w'orkers :s on concentrations of g fatty acids (palrnitic, pahnitoleic, stearic, oleic, linoleic, eicosanoic, linolenic and eicosenoic acids) in 572 samples of olive oil front 9 different growing regions. Derde el al.'-" applied SIMCA (soft independerJt modeling of class analogy) tc, the successful classilication of halian olive oil samples acco.'ding to their origin, on the basis of GC profiles of 7 fatty acids of 100 virgin oils from tv,o regions of Italy, which were a SUtlSet of the data of Forina er a/. -'~ Although Derde et al. did not specilically study adulteration, this example shows the impact of computer-aided ntultivariate analysis or pattern-recognition techniques, in the interpretation of large data sets. `g

SpectrDscopicmethodsfor det.ecting adulteration UV spectrophotometry The adulteration of virgin olive oil with refined olive or olive residue oils may be detected based on the presence oif conjugated polyene systems in the adulterated oils, which give rise to altered UV absorbance spectra. The sensitivity of UV methods is low since the virgin olive oils themselves show a w~de range of absorptivity, prechuiling unequivocal detection of adulteration at levels below 10-20%. Many of the UV methods that were originally proposed were based on measurements of the :~pecific extinction coefficient at 268-270 nm, corresponding to the maximum absorption of conjugated trienes produced during refining~'-'7. f-lowever, storage of virgin ,l~live oil itself leads to the formation of prodacts that ab:l~orb m the same region, resulting in a decrease in the sensitivity of the method, even after treatment with alumina 27. A m Dre promising region of the UV spectrum to use for detecting adulteration is the absorption maxima of the co~ugated tetraene systems near 310-320 rim. These tetraene systems are secondary oxidation products of some chain reactions that occur during the autoxidation of oils. Second-derivative UV spectrophotometry In detect these tetraenes was reported by Kapoulas and Andrikopoulos -'Kto be capable of unequivocal detection of 5% adulteration with good-quality o,live kernel oil or relined olive oils. lu many cases, adulteration with these oils at: I-2% levels was detectable, The distance between the nmximum and minimum of the secondderivative reflection at 315nm, measured in units of absorptivity (AK315). showed the most characteristic differences between over 100 samples of virgin olive oil and refined oils. including olive, olive kernel and olive 'coup6' oils. as well as other seed oils. The values of AK3~ of 1% (w/v) solutions of oil samples in cyclohexane were in the range of 0.0{)8-0.015 for virgin olive oils from tlae last crops of the season, and 0.010-0.030 for virgin olive oils from eider crops, but exceeded 0.450 and 0.990 for refined olive and olive kernel oils, respectively. Tbus, it was concluded that a AK3~5 value of 0.040 or higher would give an almost definitive indication of adulteration of a virgin olive oil by refined oils. Passaloglou-Emmanouilidou -'v measured AKu5 values of 4% (w/v) solutions of oil samples in cyclohexane as absolute values of extinction measurements directly from the UV spectra rather than from second-derivative spectra. Of the 67 virgin olive oil samples studied. 60 had AK3~ values of 0.008-0.018, while 18 of 20 olive residue oils had values of 0.430-0.730, and 18 of 20 refined olive oils had values of 0.410-0.690. Thus, for these ~;amples, adulteration at 5% levels could be detected. However, some samples of virgin, refined and residue oils had A K ~ values distinctly outside the range of their classes, and limits of detection of adulteration in these cases were consequently higher. Mass spectrometry Pyrolysis mass spectrometry (PyMS) systems, in particular Curie-point PyMS, have proved to be uniquely

6

advantageous for applications that require maximum reproducibility in the "fingerprinting" and routine analysis of hundreds or tltousands of samples, including complex samples such as those encountered in materials of biological origin2L This technique belongs to the broad group of filament pyrolysis techniques, but is unique in that the filament is inductively heated 13y a highfrequency coil and the equilibrium temperature of the filament is determined by the Curie point of the ferromagnetic filament. Following sample pyrolysis, mass spectrometric analysis of the multieomponent mixture is performed, and in most cases computer-assisted techniques are used for both quantitative and qualitative interpretation of the spectra. Goodacre et a l ? ° reported that a combination of Curie-point PyMS with multivariate data analysis using artificial neural networks permitted rapid assessment of the adulteration of extra-virgin olive oils witfi various s~.ed oils. Two sets of samples were prepared, each consisting of 12 samples of extra-virgin olive oils and 12 samples variously mixed with 5-50% of soybean, sunflower, peanut, corn or rectified olive oils. The pyrolysis mass spectra of virgin olive oils and the oil mixtures were difficult to distinguish by eye. One sample set was then used for training the artificial neural netwoik, consisting of an input layer of the 150 normalized ion intensities with mass:charge ratios in the range cf 51-200 and one hidden layer of eight nodes, using the standard back-propagation algorithm, and coding virgin oils as 1, non-virgin oils as 0. When the second set of coded samples was analysed as unknowns. the network was able to correctly assess each oil; virgin oils were assessed with a code of 0.99976_+0.000146, and non-virgin oils were coded as 0.001079 _+0.002838. Curie-point PyMS has several advantages over the more common applications of ntass spectrometry (MS) as a detection tool after GC separation (GC-MS). Little or no sample work-up is required for pyrolysis as compared with GC, and speed of analysis is a distinct advantage. Pyrolysis techniques are also better able to distinguish between homologous series of some aliphatic compounds than are other MS techniques-'L Brnmley et al. ~ reported the use of G C - electron ionization mass spectrometry (GC-EIMS) for the identification of sterols in vegetable oils as butyryl esters, and for relative quantification using GC - flame ionization detection (GC-FID). Two types of adulteration of olive oil were readily detected. Rapeseed oil could be detected easily by the presence of brassicasterol, which is not normally found in virgin olive oils; and animal-derived oils could be detected by the substantial increase in cholesterol content. Substitution with other vegetable oils was not as readily identified, and these authors suggested the use of ratios of different sterols, nonsterol marker compounds and pattern-recognition analysis to tackle this complex problem.

Vibrational spectroscopy IR absorption and Raman scattering processes involve the vibrational energy levels of sample molecules, which

Trendsin FoodScience& TechnologyJanuary1994[Vol. 5]

are related primarily to stretcl~ing or bending delbr- commercial oils. Nevertheless, it may be of interest as a mations of the molecular bonds. IR absorption analysis screening test. considering the ,apidity of the method 3. requires a change in the molecule's intrinsic dipole rap- Furthermore, the sensitivity t ~ the method could be ment with molecular vibration, while Raman scattering improved by coupling it with ~:tfltivariate calibration measurements depend on changes in the polarizabilit,~.J techniques, as was reported lk~r t,,~ determination of low or "shape" of the electron distribution in the m o t e ~ levels of rams unsaturation in ~ :onitoring the autoxiit vibrates. Thus, the same molecule rnay g i v e ~ . . l dalton of unsaturated fatty acid tnethyl esters using Raman b'mds with differing intensity and b a t : ! i i ~ p e I partial least squares regression ,,rialysis of near-IR For e×ample, polar functional groups such as C.--<:J and Fouricr-transfonu IR (N!.~FI'IR) da a 's. C - O - H have strong infrared stretcIfing vibrations, white Raman ~ o p p , , k ; , ~ . i s i n g laser e citation in either nonpolar groups such as C = C show intense Raman t h e ~ { ~ ~ , ~ , and Nakai. S.. 0npubIished)or bands ~-'.These two branches of speeu'oscopy yield comp- ~ ~ : ~ i ~ " w a s reported for the ,,..alysis of varilementary information about molecular vibrations, each , ; ~ J ~ g i T ~ and fats. The relative intensities of contributing to a vibrational spectral "fingerprint" of the ~ n~.~. 3010cm * and 1660cn, ~ assigned to molecules. S~st~etching vibrations, resp~ -fivelv, were Over the past few years, the most signilicant advances u~ ~. , ~ , o r ~ ; (~t",tifferent levels of unsa~ration, and in vlb~ational spectroscopy may be related to Fourier it was ~ , e s t e d that Raman specuoscopv c ~dd be usetransform Raman and 1R ins|,untentation. "fhese instru- ful for the idemificatiou of adulteru, i,;~, H ~vever, the ments offer several advantages over conventional ones: characteristic levels o f oleic and linol~ic tel,z:; in canola improved signal-to-noise ratio due to the averaging of oil make it difficult to dete~:~,the addilic,7, of :if ola oil to hundreds of scans per sample, improved light throughput olive oil. Raman spectra 'Jf olive ¢~i~, froi':~ ~,rre differand speed of analysis due to use of the Michelson ent geographic origins ( G ~ ' S p a i n a-~-', l~aly), of interferometer principle (as opposed to conventional canola oi!s, and of m i x t u ~ , ~ o l i v e ~ ~and"!~i0°la oils dispersive instrumentation); improvements in the accu- were therefore at~.alysed ~ a , G.. ~ E. and ntcy of wavelength calibration in IR analysis through Nakai, S.. u n p u b t i s h e d ~ . a l t c ~ n o l a ~ . . ' r ~ ! a t i v e use of a reference He-Ne laser; virtual elimination of intensity of tile Raman' 0and c e n E ~ ? ~ - , was fluorescence interference ill Raman analysis through use greater than that of the band c e n t ~ ; ~ 4 ~ ] ~ L i- t'~ese of near-IR excitatic,n; and developmcnts in the further bands may be assigned to C = ( ~ . . - , CP]~,~:i(,ions, analysis of the spectral data through computer-aided respectively. The ratio of peak"ieights (~, , ' ~ 1 ~ ~,aried techniques. Such systems are rugged and versatile, and in the range of t,569-1.654 for the " a r ~ ' , ~ , l s , and in principle offer an opportunity for applications in the 0.773-1.092 for the olive oil's. A similar ~rend was noted on-line analysis/control of industrial processes 3~. for the ratios of peak areas. These observ~,,tions are conDifferential IR spectwscopy was reported many years sistent with the fact that canola oil has a t:igber degree ago by Bartlet and Mahon ~4 for the identification of oils of unsaturation than olive oil. and the detection of oil adulteration, For each difference The addition of eanota oil to olive oil a, fected the IR spectrum. 10% (w/v) solutions of oil in carbon tetra- height, area and shape of the two Raman bands malysed chloride were analysed, A total of 242 olive oil samples (Fig. 1). Nevertheless, it was difficult to detect ~ e adulfrom 12 sources were analysed, using one of the teration of olive oil with canola oil based on ti'¢* ratio Spanish olive oils in the reference beam. Minor devi- of either the heights or the areas of these two i ~nds. ations were noted between different sources in the differ- A much better calibration model could be obt~.:ned ence spectra at I lO0-1050cm ~ and t 125-1100cm ~. by using partial least squares (PLS} multivariate ~ :liMuch greater differences were noted when other veg- bration. The results of cross-validation showed that, e etable oils, hydrogenated fats or animal fats were placed PLS model was more stable to sample variation than ~ifc in the reference beam. Differences persisted in mixtures univariate models (Table 2). In this study,, visible ta,'ic consisting of the reference Spanish olive oil with 10cA dispersive Raman instrumentation was used, and ~};.~ adulteration by other oils. such as rapeseed oil or a addition of 7.5 ,i are usually undetectable using other methods, sUCh as canola oil in olive oil) based on triacylglycerol protil,~i iodine number, saponification number and refractive obtained by reversed-phase HPLC (RP-HPLC) s. Ram~. index. The only difliculty encountered was in the detec- spectroscopy has the advantages of speed of analysJ~ tion of tea seed oil, which showed a similar IR spectrum and lack of any sample preparation or dilution. Greate to pure olive oil. Barrier and Mahon u reported thai the sensitivity may be possible with the improved signal-to.'i difference spectrum of an oil s~mple could be obtained noise ratios atlainable with FT-Raman inslruments. in about 10 minutes: thus this technique was claimed to In a recent revie~v, Gerrard and Birnie u noted that be one of the most rapid techniques for checking the Raman spectroscopy undoubtedly has an itnportant role i purity of an oil sample, able to detect adulteration !evels to play as an analytical tool in an industrial environof 10-20% of most fats and oils in a single test, ment, and that many applicatiol~s have demonstrated its This method has not been widely accepted due to the technical feasibility to replace ctnxent methodologies, ~, possibility' that false verdicts of adulteration may arise However. for this potential to be realized, it will have to as a result o f tile variations tonnd between different be dernonMraled to be better in terms of speed, accuracy { Trends in Food Science & Technologv tanuarv l qq4 tVol, 5t

O l i v e oil (%)

Chromatographic methods for olive oil analysis Column and gas chromatography 0 As is evident from the reports to 0 the AOAC Committee on Food"-'". 0 most of the current work on olive oil adulteration is based on chro0 matographic analysis, especially RP-HPLC and/or GC analysis of various constituents of fats and oils. including fatty acids, triacylglycerols and sterols. However. the wide range of naturally occurring fatty acid and sterol compositions in olive oils from different sources or regions is a limiting factor in the interpretation of the data with regard to adulteration. 1 One approach to overcoming t700 16'25 1550 14~5 1400 the difficulty due to this variaW a v e n u m b e r shift (cm i) bility is to take advantage of the predictions of the "even" and "restricted random" distribution Fig. 1 theories, which suggest that the Raman speclra Ismoothedl ot mixtures o( ca~lo[a oil and var,~ ng propor ions of Greek virgin olive oil, qualitative triacylglycerol compoin the spectral regions correspondingto C=C i 1600-1700 cm II and CH: I1400-t 500 cm ~)vibrations of sitions of various types of vegthe fatty acyl chains, iAdapted from Arteaga, G., Li-Chan,E, and Nakai, S,, unpublished.) etable oils are not affected by natural quantitative variations in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . their contents of individual fatty acids. Thus. despite Table 2, Comparison~of partial [east squares (PL5) and two natural variations, OOO and POO are the characteristic univariate medals for predkfing the degree of adulteration of triacylglycerol species in olive oil, while OLL, PLL offve off with carmla oll based on data from gamzn spectral and LLL are characteristic of linoleate-rich oils. Earlier bands c~mredat1660cm_land ,1445cm_~ methods to measure triacylglycerol species often required two steps: oil fractionation by low-temperature crystallization or argentation chromatography, followed Cross-validation All samples by GLC analysis of the fatty acid methyl esters of the isolated triacylglycerol fraction. However, a single-step Method r "~ SEP(%) r" SEP (%) RP-HPLC method has been reported by Kapoulas and Andrikopoulos TM to be capable of resolving triacylPLS 0.% 7.0 0.99 6.3 glycerol mixtures according to their equivalent carbon Ratio of peak hei~.h~s 0.85 20.2 0.95 1~.2 number (ECN). Triacylglyeerol fractionation according Ratio o~peak ~reas 0.75 23.1 0.92 19.3 to ECTN was accomplished in 22-25 minutes by elation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of a column packed with lOIJm C1~ alkyl-bonded" Comp.i~ri',~m,)t restgl, *or reRressiollanatv:,e~,o¢ actual ~er~uspredicled phase particles with acetonitrile : absolute ethanol : iso(ontenl of oli~( oil m mi,~iuw~i)l oh~e a n d canola ¢),1 Adapled irom propanol (72 : 18 : [0l. Using UV absorbance at 210 nm Arleaga,G., Li-Chan,[. and Nakai,S., unpubli,,hed 10 monitor the HPLC profiles, adulterant linoleate-rich r. Ome]ialioncoefficient oils were detected on the basis of the areas of peak:: SEP.Slandarderroroiprediclion corresponding to ECNs 42, 44 and 48 relative to the . . . . . . . . . . . . . . . . . . . . . . . . . . . . . area of the ECN-46 peak. Visual inspection of HPLC patterns could unequivocally reveal the presence of and/or cost than alternatPce techniques. Given the com- 2.5c~ sunflower oil and 3-4c~ of other tinoleate-rich oils merciaJ avadability of FT-Raman and FTIR instrunaents in olive oil. that can analyse samples in minutes, and published Detection of the adulteration of olive oil with seed reporbl of their suitability for the analysis of fats and oils that are not rich in linoleic acid is more difficult. oils wit|lout any sample pretreatment'" ~, it is likely that Using the RP-HPLC method to analyse ECN profiles of in the foreseeable fitture these spectral "fingerprint" the triacylglycerols. Salivaras and McCurdy ~ reported analyses will be used as rapid screening methods for that the common seed oils such a.,, sunflower, soybean and detectiing olive oil adulteration, especially if the spectral corn oils could easily be detected ,tt 2.5c/~ adulteration data are analysed whh the aid of chemonaetrics. levels. Canola oil could be positively detected only at "~0

8

Trends in Food Science & Technology January 1994 [Vol. 5]

levels above 7.5% (w/w): nevertheless, this was an improvement over other methods (Salivaras, M. and McCurdy, A.R., unpublished). GC analysis of fatty acid composition could only detect levels of adulteration of 10ck, 15ck, 20ck and 25ck for soybean, canola, sunflower and corn oil, respectively, due to the variations in fatty acid composition recognized to occur naturally in different olive dls, UV absorbance at 270rim was within the range of pt:re olive oil for all the seed oils at t0ck adulteration levels, and within the range for virgin olive oil for 10% canola adulteration. Refractive index measurements could not detect ca,nola in olive oil even at an adulteration level of 60%. In order to detect adulteration of olive oil by oils with a similar fatty acid and/or triacylglycerol composition (i.e. high oleic acid and low linoleic acid contents) or to distinguish between extra-virgin, virgin, pure and lampante oils, it may be necessary to evaluate non-acylglycerol components, such as sterols, fatty alcohols and their esters, or to analyse the stereospecific structure of the triacylglycerols. Grob et. al. ~~° reported on the application of an online coupled L C - G C method to analyse these minor components in olive oils as an indication of the purity of extra-virgin olive oils, in order to, detect pretreatment or admixture with other oils such as solvent-extracted olive oils. The method involved the addition of an internal standard and pivalic acid anhydride to the oil to esterify free alcohols, then the use of LC to separate the free and esterified forms of the alcohols from the triacylglycerol matrix and other interfering material. Two L C - G C runs were required, the first to measure the sterol fraction and the second to measure the erythrodiol fraction. The detection of re-esterified oils by the analysis of fatty acids at the sn-2 position was reported by Gegiou and Georgoulial~ using pancreatic lipase digestion, followed by thin-layer chromatography to isolate the 2-monoacylglycerots, derivatization to form methyl esters, and GC analysis of the fatty acid methyl esters. Generally, the level of palmitic acid at the 2-position is lower in olive oil acylglycerols than in re-esterified oils, but a orecautionary statement was included in the report since certain refining processes may elevate this level in oils that have not been re-esterified. These authors also reported a rapid argentation thin-layer chromatographic method that was able to detect re-esterified oils at a level of ~15ck (Ref. 42). Triacylgtycero] structure has been determined following partial hydrolysis of triacylglycerols of extra-virgin olive oil with ethyl magnesium bromide to form diacylrac-glycerols, using a sequential combination of silverion HPLC and stereospecific analysis by HPLC on silica~. This technique has potential for detecting the adulteration of olive oil with esterified oil. but suffers from the drawbacks of the lengthy procedure required.

oils based on either triacylglycerol profiles obtained from reversed-phase HPLC monitored using a mass detector or fatty acid composition obtained from GC analyses of the fatty acid methyl esters. These authors emphasized that although fatty acid and triacylglycerol profiles provide a characteristic "fingerprint" of an oil. multivariate statistics are mandatory in order to study the effects of adulteration, as they provide the power to consider differences in the whole chromatogram rather than in individual components only. Mixtures of olive oil and maize, cottonseed, sunflower, soybean or rapeseed oils at IOC/c or 20e~ addition levels could not be clearly distinguished from pure olive oil by comparing the GC data to the acceptable ranges of fatty acid profiles given by the Codex Alimentarius. due to the small number of common fatty acids found in these oils and their natural variability from sample to sample of the same type of oil. Adulteration at 20ok levels could be clearly detected based on PCA plots of the HPLC data of triacylglycerol composition (Fig. 2). The distinction was less clear at 10% adulteration levels. Thus, triacylglycerol profiles proved to be m,~re dtscnminating than fatty acid composition for both the classification of olive oils and the detection of adulteration. This conclusion would be expected since the triacylglycerol profiles contain structural inforrnation such as the distribution of fatty acid residues among the different triacylglycerols, while this information is lost during the methanol transesterification step necessary for fatty acid analysis by GC. It should be noted that Tsimidou et al. ~'~5 did not have a complete range of the necc~,,ry triacylglycerol standards, so their results were based on evaluation of the uncalibrated HPLC data. It is anticipated that greatly improved detection limits will be possible in the near future by coupling the improved RP-HPLC methods, which give excellent resolution of triacylglycerols according to ECN values s~'~, with multivariate dala analysis such as PCA, P L S , neural networks or the recently proposed PCS (principal component similarity attalysis) ~'. An important feature of these multivariate techniques is that they fully utilize even subtle variations in the total spectral or chromatographic profiles that may be imporlant for pattern recognition. One example of this trend was recently reported by Kaufinann ~v, who applied PCA and PLS regression analysis of the fatty acid and triacylglycerol profiles analysed by chromatographic methods to fulfil three goals: to construct a model to detect adulteration; to identify the components in the mixture: and to predict the levels of the components present in the mixture, Nine types of authentic oils (linseed, palm kernel, peanut, sesame, sunflower, high-oleic sunflower, olive, safflower and soybean) were analysed by chromatogr~aphy. Additional chromatograms were generated by multiplying the standard deviations of the chromatographic data by a random number and randomly adding or subtractMt!lfivariate data analysis of chromatographic dala ing these quantities from the authentic chromatograms Tsimidou et al. ~'~5 applied principal cofnponent analy- to generate new sample ct~romatograms. Nine different sis (PCA) to confirm the authenticity of virgin olive calibration mixtures (ranging from 50 : 50 to 99 : I I of

Trendsin FoodScience& TechnologyJanuary1994[Vol. 51

9

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~to

o

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,

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Fig. 2 AHocation of unadulterated and adulterated Greek : irgin olive oils on the principal components analysis (PCAI plots of l,.:o prin(:ipat t omponents IPC1 and PC21 o~ HPLC triao;Iglycerol data. ! ), Data #ore 45 unadulterated '~,irgin olive oils, {@), Data from one particular virgin olive oil adulterated with: ia--el, 20% maize, (:ottonsee'J, sunflov,,er, soybean ur rapeseeci oils, respectively: (K), no adulteration. (Adapted ~xith permission from Rei. 45.!

Table 3. Relative prediction error of composition of mixtures Of olive oil with other (safflower, peanut, sesameor high-olek sunflower) oils" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Class RSD model Chromalog model Mixture

Oli,,e : ',afflov,er {97 :~1 ()live : safflm,,er~q9 : 11 Oli',e :peanut !60 : 4{li Oli;e : peanut i80 : 2(1~ OGe : ',esame (7n : "~0) OIKe :sesame e,~ :~,!

Oli~,e : high-oleic sunflo~;er180 : 201 Oli,,e : high-oleic sunflo~xer!q~ : 51

Olive Other 4.8 4.2 I l.~ 51

16.2 67.0 50.5 76.(~ 17.(1 35.7 1.2~ 54.8 k3 15.0 2.0 54.0

O!ive Other 0.3 0.3 0.1 0.I 0.4

16.2 52.0 0.5 1.0

0.2

8.0 i .0 8.6

O.3 0.3

0.7

Retalr,e predi(t!im euitr [nr eilher the ~th,,e oil or other oil (imlllnnenl in the l'niklure~ u',ing partial leaq ',qu,are,, d~LS nlodel~ de,,eloped ;,.ilh eilt~r (Ja,,~ residuaI qandard de,,ialum ,,atue~ '(t,>,i RSD model or thromalc)graphR ~.ariahle, ( hromatog nlcx]e['} -\dapted(tom Ret 47 . . . . . . . . . . . . . . . . . . .

1(1

olive oil and four different oils (peat,u(, safflower, sesame and sunflower) were used/'or the calibration set. which was created by summing chromatograms of the authem;c oils multiplied by the appropriate proportionality factors. A test set of eight mixtures was withheld from the model-building process for later use in model validation. Individual PCA models for each of the nine oil types were developed using the SIMCA 3B program (Sepanova AB. Stockholm, Sweden) for classificatiol;, while prediction of mixture composition was done by PLS. Calibrations and prediction using the residual standard deviations of each oil sample based on the SIMCA classification were satisfactory in predicting olive oil content but resulted in some gross errors in identification and quantification of the minor component in the oil mixtures (Table 3). However, using the chromatographic variables as the X-block and the corresponding matrix of mixture proportions as the Y-block yielded nine significant PLS components, which explained 93% and 96c~ of the variance in the X- and Y-blocks. respectively. Adulteration was detected down to at least the 1.59~- level, and components in the mixtures were correctly identified. Prediction of the levels of the components in the mixtures was also correct down to the 1.5c/~ level {Table 3). Conclusions The adulteration of virgin olive oil with olive oil products or other edible oils can be detected using chromatographic analyses of the fatty acid, triacylglycerol and sterol compositions. Spectroscopic analyses such as UV or vibrational spectroscopy to monitor particular functional groups such as conjugated systems or unsaturated versus saturated bonds, or pyrolysis mass spectrometry to yield specific fragmentation patterns, are also emerging as methods for the determination of authenticity. These instrumemal methods of analysis are more reliable than earlier methods based on individual physical/chemical characteristics such as iodine value, saponification value, refractive index, viscosity or cotorimetric reactions. In most cases, the level of adulteration that can be detected by comparing chromatographic or spectral data with reference values for authentic olive oil falls in the range of 5-20%. One of the constraining factors in attempts m lower this detection limit is the intrinsic variability among authentic virgin olive oils. The detection level can be improved considerably by applying chemometrics techniques to aid in more objective interpretation of the data. taking into consideration quamitatively minor but possibly highly significant variations that distinguish olive oils and the adulterants. Multivariate data analysis allows detection at 1-2% adulteration levels, even with adulterants that are usually difficult to detect. It is predicted that continued progress in the application of computer-aided mathematical and statistical ,echniques to enhance the power of chromatographic and spectral analyses will lead to greater ease in the rapid and unequivocal detection of olive oil adulteration. 1rends m Food Science & Technology lanuary 1994 [Vol. 51

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Cross-disdpl[nary trends Trends in Food Science & Technology is one of 12 Trends journals published in Cambridge, UK. The following recently published Trends articles may be of interest to readers of TIFS. Molecular genetics of tomato fruit ripening, by Rupert F. Fray and Donald Grierson, Trends in Genetics 9(12), 438-443 ,-~ssembly and transport of seed storage proteins, by Gad Galiti, Yoram Altschuler and Hanna Levanony, Trends in Cell Biology 3(t 2), 437-442

Trends in Food Science & Technology lanuary 1994 1\/ol. 5]

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