Rapid Authentication of Olive Oil by Raman Spectroscopy Using Principal Component Analysis

A method of principal component analysis was employed to authenticate genuine olive oil based on Raman spectroscopy, which can reliably distinguish olive oil from other types of oils and can also accurately identify the level of adulteration in a set of olive oil samples contaminated with 5% or more of other types of oils, such as soybean oil, rapeseed oil, sunflower seed oil, and corn oil. The method is very easy, effective, time-saving, and requires minimal sample preparation. Therefore, the method is a promising technique for the rapid authentication application of olive oil.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resource(s): Additional text and table]     

Optimization and application of methods of triacylglycerol evaluation for characterization of olive oil adulteration by soybean oil with HPLC-APCI-MS-MS

Triacylglycerols (TAGs) are the main constituents of vegetable oils where they occur in complex mixtures with characteristic distributions. Mass spectrometry using an atmospheric pressure chemical ionization interface (APCI-MS) run in positive mode and an Ion Trap mass analyser were applied in the study of olive and soybean oils and their mixtures. Direct injections of soybean and olive oil solutions allowed the identification of ions derived from the main TAGs of both oils. This procedure showed to be a simple and powerful tool to evaluate mixtures or addition of soybean to olive oil. TAG separation was optimized by high performance liquid chromatography (HPLC) using an octadecylsilica LiChrospher column (250 mm × 3 mm; 5 μm) and a gradient composed of acetonitrile and 2-propanol allowed the separation of the main TAGs of the studied oils. APCI vaporization temperature was optimized and best signals were obtained at 370 °C. Multiple reaction monitoring (MRM) employing the transition of the protonated TAG molecules ([M+H]⁺) to the protonated diacylglycerol fragments ([M+H−R]⁺) improved the selectivity of TAG detection and was used in quantitative studies. Different strategies were developed to evaluate oil composition following TAG analysis by MRM. The external standard calibration and standard additions methods were compared for triolein quantification but the former showed to be biased. Further quantitative studies were based on the estimates of soybean and olive oil proportions in mixtures by comparison of TAG areas found in mixtures of known and unknown composition of both oils. Good agreement with expected or labeled values was found for a commercial blend containing 15% (w/w) of olive oil in soybean oil and to a 1:1 mixture of both oils, showing the potential of this method in characterizing oil mixtures and estimating oil proportions. Olive oils of different origins were also evaluated by mass spectra data obtained after direct injections of oil solutions and principal component analysis (PCA). Argentinean olive oils were clustered in a different area of the principal components plot (PC2 × PC1) in comparison with European olive oils. The commercial blend containing 15% (w/w) of olive oil in soybean oil appeared in a completely different area of the graphic, showing the potential of this method to screen out for olive oil adulterations.     

Differentiation of vegetable oils by mass spectrometry combined with statistical analysis

The main triacylglycerol (TAG) composition of different plant oils (almond, avocado, corn germ, grape seed, linseed, mustard seed, olive, peanut, pumpkin seed, sesame seed, soybean, sunflower, walnut and wheat germ) were analyzed using two different mass spectrometric techniques: HPLC/APCI-MS (high-performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry) and MALDI-TOFMS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry).Linear discriminant analysis (LDA) as a multivariate mathematical statistical method was successfully used to distinguish different plant oils based on their relative TAG composition. With LDA analysis of either APCI-MS or MALDI-MS data, the classification among the almond, avocado, grape seed, linseed, mustard seed, olive, sesame seed and soybean oil samples was 100% correct. In both cases only 6 different oil samples from a total of 73 were not classified correctly.     

Rapid characterization of edible oils by direct matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis using triacylglycerols

Direct matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOFMS) analysis of solutions of edible fats/oils yielded spectra useful for their rapid differentiation and classification. Results also reflected the individual fatty acid components and their degree of unsaturation. After dissolution in hexane, MALDI-MS analysis revealed spectra showing characteristic triacylglycerols (TAGs), the main fat/oil components, as sodium adduct ions. The Euclidean distances calculated using the mass and intensity values for 20 TAGs were used to evaluate and compare spectra. With cluster analysis, animal fats grouped together differently than vegetable oils and the individual oils grouped together by type. The ion abundances for the individual TAGs and their presumed compositions were used to approximate the overall fatty acid composition of canola, soybean, corn, olive and peanut oil, as well as lard. Using this approach the calculated fatty acid compositions and degree of unsaturation generally fell within about 4% of literature values. When the degree of saturation was compared with values calculated from the package labeling the differences were about 7%.     

Comprehensive two-dimensional liquid chromatography x gas chromatography: evaluation of the applicability for the analysis of edible oils and fats

Edible fats and oils are complex mixtures containing a wide range of (classes of) compounds. The most important group of compounds are the triglycerides (triacylglycerides, TAGs). Because of the large number of possible fatty acid combinations, an enormous number of TAGs is possible. In the present feasibility study, the applicability of different modes of comprehensive two-dimensional LCXGC for detailed oil and fat analysis is evaluated. Comprehensive LCXGC was found to be an extremely powerful analytical method for the analysis of complex TAG samples. Using the new comprehensive set-ups, TAGs can be separated according to two independent parameters: carbon number vs. number of double bonds, or fatty acid composition vs. number of double bonds. The information content of comprehensive separations by far exceeds that of the current generation of analytical methods. The quantitative results of the separations show a good agreement with data obtained from standard analytical methods. The comprehensive methods studied can also be used for fingerprinting of oil samples, as well as for the analysis of target compounds or compound groups. Highly detailed separations of olive oil samples were obtained. Zooming in on one region of the chromatogram allowed reliable analysis of wax esters without interferences of sterol esters.     

Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection.Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.     

Comprehensive two-dimensional liquid chromatography×gas chromatography: evaluation of the applicability for the analysis of edible oils and fats

Edible fats and oils are complex mixtures containing a wide range of (classes of) compounds. The most important group of compounds are the triglycerides (triacylglycerides, TAGs). Because of the large number of possible fatty acid combinations, an enormous number of TAGs is possible. In the present feasibility study, the applicability of different modes of comprehensive two-dimensional LC×GC for detailed oil and fat analysis is evaluated. Comprehensive LC×GC was found to be an extremely powerful analytical method for the analysis of complex TAG samples. Using the new comprehensive set-ups, TAGs can be separated according to two independent parameters: carbon number vs. number of double bonds, or fatty acid composition vs. number of double bonds. The information content of comprehensive separations by far exceeds that of the current generation of analytical methods. The quantitative results of the separations show a good agreement with data obtained from standard analytical methods. The comprehensive methods studied can also be used for fingerprinting of oil samples, as well as for the analysis of target compounds or compound groups. Highly detailed separations of olive oil samples were obtained. Zooming in on one region of the chromatogram allowed reliable analysis of wax esters without interferences of sterol esters.     

Usefulness of the direct coupling headspace-mass spectrometry for sensory quality characterization of virgin olive oil samples

The applicability of the headspace coupled to mass spectrometry for evaluation of the sensory quality of virgin olive oil samples is presented. The volatiles of the oil are directly transferred from the sample vial to the detector without chromatographic separation. The mass spectrum obtained can be considered as a fingerprint of the oil sample and can be used for classification purposes. After a training step with samples previously qualified following the official method, a classification model was created using the supervised technique soft independent modeling of class analogy (SIMCA). Eight negative (rancid, winey-vinegary, muddy sediment, hay-wood, vegetable water, earthy, fusty and musty-humidity) and three principal positive attributes (fruity, bitter and pungent) have been included in this study. With them, a classification model consisting of two main classes (extra- and lampante-virgin olive oil) was constructed. In addition, the unsupervised technique cluster analysis permited the discrimination among oils with different negative attributes. The proposed procedure has been applied to the classification of commercial samples (as extra- or lampante-virgin olive oils) and the results were compared with those provided by the expert's panel with acceptable correlation.     

A New Method for Olive Oil Screening Using Multivariate Analysis of Proton NMR Spectra

A new NMR-based method for the discrimination of olive oils of any grade from seed oils and mixtures thereof was developed with the aim of allowing the verification of olive oil authenticity. Ten seed oils and seven monovarietal and blended extra virgin olive oils were utilized to develop a principal component analysis (PCA) based analysis of ¹H NMR spectra to rapidly and accurately determine the authenticity of olive oils. Another twenty-eight olive oils were utilized to test the principal component analysis (PCA) based analysis. Detection of seed oil adulteration levels as low as 5% v/v has been shown using simple one-dimensional proton spectra obtained using a 400 MHz NMR spectrometer equipped with a room temperature inverse probe. The combination of simple sample preparation, rapid sample analysis, novel processing parameters, and easily interpreted results, makes this method an easily accessible tool for olive oil fraud detection by substitution or dilution compared to other methods already published.     

Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and time consuming analytical techniques. There is a need for fast and simple analytical techniques for the objective of a quality control methodology. Virgin olive oils present characteristic NIR spectra. Chemometric treatment of NIR spectra was assessed for the quantification of fatty acids and triacylglycerols in virgin olive oil samples (n=125) and for their classification (PLS1-DA) into five very geographically closed registered designations of origin (RDOs) of French virgin olive oils ("Aix-en-Provence", "Haute-Provence", "Nice", "Nyons" and "Vallée des Baux"). The spectroscopic interpretation of regression vectors showed that each RDO was correlated to one or two specific components of virgin olive oils according to their cultivar compositions. The results were quite satisfactory, in spite of the similarity of cultivar compositions between two denominations of origin ("Aix-en-Provence" and "Vallée des Baux"). Chemometric treatments of NIR spectra allow us to obtain similar results than those obtained by time consuming analytical techniques such as GC and HPLC, and constitute a help fast and robust for authentication of those French virgin olive oils.     

On testing quality and traceability of virgin olive oil by calorimetry

Extra Virgin olive oils (7 samples) originating from different areas of Tuscany, defective olive oils (5 samples), commercial edible seed oils (4 samples) and two commercial samples of olive oil (one declared ‘extra virgin olive oil’ and one ‘olive oil’) were studied by different calorimetric techniques: high sensitivity isothermal, differential scanning, and modulated scanning calorimetry. The temperature interval (–60) – (+30)°C was explored for monitoring: i) the main features of the liquid↔solid phase transitions, ii) the nucleation and growth rate of the polymorphous crystalline phases of the triacylglicerols, and iii) the melting process. This investigation was planned for verifying the utility and effectiveness of calorimetry for screening quality and origin of olive oil. To this end, the main calorimetric operation modes have been applied, the experimental results reported and their utility for developing an effective and reliable screening protocol discussed.     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.     

激光解吸电离傅立叶变换离子回旋共振质谱用于食用油的快速分析

利用激光解吸电离傅立叶变换离子回旋共振质谱(Laser desorption ionization,LDI-FTICR-MS)建立了一种快速分析食用油中甘油三酯(TAG)的方法。在激光能量为45%,激光频率100 Hz和辐照次数100shots的条件下,可以获得稳定重复的信号(RSD10%)。通过TAG的一级质谱图和二级碎片信息可以初步区别不同类型的食用油。在置信度为95%条件下,利用主成分分析法和聚类分析法可以有效地将34种食用油归类。此外,利用该方法可直接识别橄榄油中掺杂5%的菜籽油且根据线性公式可初步预测橄榄油中掺杂油品的种类。分析数据表明,LDI-FTICR-MS技术具有快速筛查和识别食用油的潜力。     

Trace elemental characterization of edible oils by ICP-AES and GFAAS

A method for the determination of the inorganic profile in edible oils is proposed. The quantification of selected metals in various oils (olive, pumpkin seed, sunflower, sesame seed, hazelnut, grape, soya, rice oil) was carried out using microwave assisted digestion followed by ICP-AES and GFAAS. The detection power of the ICP-AES technique was sufficient for the determination of Ca, Fe, Mg, Na, and Zn. Since the samples contained very low amounts of Al, Cu, Co, Cr, K, Ni, Mn, and Pb, these elements were measured by GFAAS. Differences of metal concentrations for edible oils obtained in this preliminary study represent a starting basis for the development of an additional analytical procedure applicable for oil characterization.     

Acrylate ester-based monolithic columns for capillary electrochromatography separation of triacylglycerols in vegetable oils

A simple and reliable method for the evaluation of triacylglycerols (TAGs) in vegetable oils by capillary electrochromatography (CEC) with UV-Vis detection, using octadecyl acrylate (ODA) ester-based monolithic columns, has been developed. The percentages of the porogenic solvents in the polymerization mixture, and the mobile phase composition, were optimized. The optimum monolith was obtained at the following ratios: 40:60% (wt/wt) monomers/porogens, 60:40% (wt/wt) ODA/1,3-butanediol diacrylate and 23:77% (wt/wt) 1,4-butanediol/1-propanol (14 wt% 1,4-butanediol in the polymerization mixture). A satisfactory resolution between TAGs was achieved in less than 12 min with a 65:35 (v/v) acetonitrile/2-propanol mixture containing 5 mM ammonium acetate. The method was applied to the analysis of TAGs of vegetable oil samples. Using linear discriminant analysis of the CEC TAG profiles, the vegetable oils belonging to six different botanical origins (corn, extra virgin olive, hazelnut, peanut, soybean and sunflower) were correctly classified with an excellent resolution among all the categories.     

Total luminescence spectroscopy with pattern recognition for classification of edible oils

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.     

Direct olive oil authentication: detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques

Control of adulteration of olive oil, together with authentication and contamination, is one of the main aspects in the quality control of olive oil. Adulteration with hazelnut oil is one of the most difficult to detect due to the similar composition of hazelnut and olive oils; both virgin olive oil and olive oil are subjected to that kind of adulteration. The main objective of this work was to develop an analytical method able to detect adulteration of virgin olive oils and olive oils with hazelnut oil by means of its analysis by a headspace autosampler directly coupled to a mass spectrometer used as detector (ChemSensor). As no chromatographic separation of the individual components of the samples exists, a global signal of the sample is obtained and employed for its characterization by means of chemometric techniques. Four different crude hazelnut oils from Turkey were employed for the development of the method. Multivariate regression techniques (partial least squares and principal components analysis) were applied to generate adequate regression models. Good values were obtained in both techniques for the parameters employed (standard errors of prediction (SEP) and prediction residual error sum of squares (PRESS)) to evaluate its goodness. With the proposed method, minimum adulteration levels of 7 and 15% can be detected in refined and virgin olive oils, respectively. Once validated, the method was applied to the detection of such adulteration in commercial olive oil and virgin olive oil samples.     

Development of a voltammetric electronic tongue for discrimination of edible oils

In this paper, we propose a novel strategy to perform cyclic voltammetric measurements with a platinum microelectrode directly in edible oil samples. The microelectrode was employed as an electronic tongue that, along with the application of chemometrics to the current–potential responses, proved useful for discriminating oils on the basis of their quality and geographical origin. The method proposed here is based on the use of suitable room temperature ionic liquids, added to oils as supporting electrolytes to provide conductivity to the low-polarity samples. The entire voltammograms, recorded directly on the oil/RTIL mixtures, were processed via principal component analysis and a classification technique (K nearest neighbors), to extract information on samples characteristics. Data processing showed that oils having different nature (i.e. maize and olive) or geographical origin (i.e. olive oils coming from different regions) can be distinguished.