Discriminating olive and non-olive oils using HPLC-CAD and chemometrics

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.     

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.     

Chiral separation of amino acids by capillary electrophoresis with octyl-beta-thioglucopyranoside as chiral selector

In the present work, we propose the use of direct coupling of a headspace sampler to a mass spectrometer for the detection of adulterants in olive oil. Samples of olive oils were mixed with different proportions of sunflower oil and olive-pomace oil, respectively, and patterns of the volatile compounds in the original and mixed samples were generated. Application of the linear discriminant analysis technique to the data from the signals was sufficient to differentiate the adulterated from the non-adulterated oils and to discriminate the type of adulteration. The results obtained revealed 100% success in classification and close to 100% in prediction. The main advantages of the proposed methodology are the speed of analysis (since no prior sample preparation steps are required), low cost, and the simplicity of the measuring process.     

Use of flow injection atmospheric pressure photoionization quadrupole time-of-flight mass spectrometry for fast olive oil fingerprinting

The recently introduced technique of an atmospheric pressure photoionization (APPI) source coupled to quadrupole time-of-flight mass spectrometry (QqTOFMS) has been applied to fast olive oil fingerprinting on the basis of the accurate mass measurements obtained with this instrumentation. The key compounds can be characterized as [M+H]+ (produced by proton transfer) or as [M]+* (by charge transfer) ions in the mass spectra. [M+H]+ ions, however, show higher abundance, especially for triacylglycerols. Other ions present in APPI-MS are the acylium ion [RiCO]+ and [RiCO-H2O]+. This latter ion is absent in the electrospray ionization (ESI)-MS spectra, and this represents valuable complementary information. Several critical parameters in the APPI source were optimized such as LC eluent composition, ion spray voltage and, especially, declustering potential. APPI-QqTOFMS allows easy discrimination among different edible oils: olive, extra virgin olive, olive-pomace, hazelnut, sunflower, corn and several mixed oils, with high throughput (approximately 1 min per sample). Cluster analysis was applied to obtain the best experimental conditions for oil discrimination on the basis of declustering potential. Principal components analyses of these APPI-MS spectra show that the approach can be used for studies of olive oil adulteration with other oils, even in the case of hazelnut oil that exhibits a high chemical similarity with olive oil.     

Simple and rapid instrumental characterization of sensory attributes of virgin olive oil based on the direct coupling headspace-mass spectrometry

A methodology for obtaining reliable qualitative and quantitative information about negative (fusty, muddy sediment, musty, rancid and vinegary) and positive (fruity) sensory attributes of virgin olive oils (lampante and extra) has been developed. The procedure implies the joint use of a headspace autosampler, a mass spectrometer and an adequate chemometric data treatment. For this purpose, soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) regression approaches were used for attribute identification and quantification, respectively. InStep application was employed to generate a decision tree by the combination of both models in order to provide the joint prediction of the sensory attributes of a given virgin olive oil and their respective intensities by means of a single output result. The good prediction results obtained when the decision tree generated were applied to a new set of virgin olive oil samples (viz, a specificity of 100%, an average sensitivity of 86% and a RMSEP<0.8% in the quantification task) reveals its potential applicability in routine analysis.     

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.     

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]     

Application of solid-phase microextraction to the analysis of volatile compounds in virgin olive oils

Solid-phase microextraction was used as a technique for headspace sampling of extra virgin olive oil and virgin olive oil samples with different off-flavours. A 100 microm coated polydimethylsiloxane fiber was used to extract volatile aldehydes, the sampling temperature was 45 degrees C and the fiber has been exposed to the headspace for 15 min. Nonanal and 2-decenal were present in all the olive oils with extraction off-flavours but were not in extra virgin olive oil sample.     

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.     

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the “extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”, “arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.     

Effects of conventional heating on the stability of major olive oil phenolic compounds by tandem mass spectrometry and isotope dilution assay

The quality of olive oils is sensorially tested by accurate and well established methods. It enables the classification of the pressed oils into the classes of extra virgin oil, virgin oil and lampant oil. Nonetheless, it would be convenient to have analytical methods for screening oils or supporting sensorial analysis using a reliable independent approach based on exploitation of mass spectrometric methodologies. A number of methods have been proposed to evaluate deficiencies of extra virgin olive oils resulting from inappropriate technological treatments, such as high or low temperature deodoration, and home cooking processes. The quality and nutraceutical value of extra virgin olive oil (EVOO) can be related to the antioxidant property of its phenolic compounds. Olive oil is a source of at least 30 phenolic compounds, such as oleuropein, oleocanthal, hydroxytyrosol, and tyrosol, all acting as strong antioxidants, radical scavengers and NSAI-like drugs. We now report the efficacy of MRM tandem mass spectrometry, assisted by the isotope dilution assay, in the evaluation of the thermal stability of selected active principles of extra virgin olive oil.     

Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils

There is a continuing need for improved methods for assessing the adulteration of foodstuffs. We report some highly encouraging data, where we have developed direct infusion electrospray ionisation mass spectrometry (ESI-MS) together with chemometrics as a novel, rapid (1 min per sample) and powerful technique to elucidate key metabolite differences in vegetable and nut oils. Principal components analysis of these ESI-MS spectra show that the reproducibility of this approach is high and that olive oil can be discriminated from oils which are commonly used as adulterants. These adulterants include refined hazelnut oil, which is particularly challenging given its chemical similarity to olive oils.     

Comparative study of electrospray and photospray ionization sources coupled to quadrupole time-of-flight mass spectrometer for olive oil authentication

The use of fast and reliable analytical procedures for olive oil authentication is a priority demand due to its wide consumption and healthy benefits. Olive oil adulteration with other cheaper vegetable oils is a common practice that has to be detected and controlled. Rapid screening methods based on high resolution tandem mass spectrometry constitute today the option of choice due to sample handling simplicity and the elimination of the chromatographic step. The selection of the ionization source is critical and the comparison of their reliability necessary. The possibilities of the direct infusion electrospray ionization (ESI) and the recently introduced atmospheric pressure photospray ionization source (APPI), coupled to quadrupole time-of-flight (QqTOF), have been critically studied and compared to control olive oil adulteration. These techniques are very rapid (approximately 1 min per sample) and have high discrimination power to elucidate key components in the edible oils studied (olive, hazelnut, sunflower and corn). Nevertheless, both sources are complementary, being APPI more sensitive for monoacyl- and diacylglycerol fragment ions and ESI for triacylglycerols. In addition, methods reproducibility's are very high, especially for APPI source. Mixtures of olive oil with the others vegetable oils can be easily discriminated which has been tested by using principal components analysis (PCA) with both ESI-MS and APPI-MS spectra. Analogously, linear discriminant analysis (LDA) confirms methods reproducibility and detection of other oils used as adulterants, in particular hazelnut oil, which is especially difficult given its chemical similarity with olive oil.     

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.     

Geographical classification of wine and olive oil by means of classification and influence matrix analysis (CAIMAN)

Classification and influence matrix analysis (CAIMAN) is a new classification method, recently proposed and based on the influence matrix (also called leverage matrix). Depending on the purposes of the classification analysis, CAIMAN can be used in three outlines: (1) D-CAIMAN is a discriminant classification method, (2) M-CAIMAN is a class modelling method allowing a sample to be classified, not classified at all, or assigned to more than one class (confused) and (3) A-CAIMAN deals with the asymmetric case, where only a reference class needs to be modelled.

In this work, the geographic classification of samples of wine and olive oil has been carried out by means of CAIMAN and its results compared with discriminant analysis, by focusing great attention on the model predictive capabilities. The geographic characterization has been carried out on three different datasets: extra virgin olive oils produced in a small area, with a “protected denomination of origin” label, wines with different denominations of origin, but produced in enclosed geographical areas, and olive oils belonging to different production areas.

Final results seem to indicate that the application of CAIMAN to the geographical origin identification offers several advantages: first, it shows – on an average basis – good performances; second, it is able to deal in a simple way classification problems related to tipicity, authenticity, and uniqueness characterization, which are of increasing interest in food quality issues.     

Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils

The aim of this article is to study tree-based ensemble methods, new emerging modelling techniques, for authentication of samples of olive oil blends to check their suitability for classifying the samples according to the type of oil used for the blend as well as for predicting the amount of olive oil in the blend. The performance of these methods has been investigated in chromatographic fingerprint data of olive oil blends with other vegetable oils without needing either to identify or to quantify the chromatographic peaks. Different data mining methods—classification and regression trees, random forest and M5 rules—were tested for classification and prediction. In addition, these classification and regression tree approaches were also used for feature selection prior to modelling in order to reduce the number of attributes in the chromatogram. The good outcomes have shown that these methods allow one to obtain interpretable models with much more information than the traditional chemometric methods and provide valuable information for detecting which vegetable oil is mixed with olive oil and the percentage of oil used, with a single chromatogram.

Quantification of the intensity of virgin olive oil sensory attributes by direct coupling headspace-mass spectrometry and multivariate calibration techniques

The main sensory defects of virgin olive oils (rancid, vinegary, winey, muddy sediment, musty and vegetable water) and one positive attribute (fruity) characteristic of three monovarietal extra virgin olive oils (Arbequina, Picual and Frantoio) have been quantified using the direct coupling headspace-mass spectrometry. The results obtained were compared with those provided by the panel test for the same samples. Taking into account that no chromatographic separation exists, multivariate calibration techniques (partial least squares, PLS, and principal components regression, PCR) were used to create the appropriate models. The best results, in terms of standard error of prediction and prediction residual error sum of squares were obtained by PLS and therefore it was used for the prediction of a new set of samples with the above-mentioned positive and negative attributes at different concentration levels. The samples were also assessed by the panel test and good correlations were obtained in all cases. In order to extend the applicability of the model with the time, a multiplicative calibration transfer was used. The benefit of this approach was found to be more marked for the negative than the positive attributes.     

Easy Ambient Sonic-Spray Ionization Mass Spectrometric of Olive Oils: Quality Control and Certification of Geographical Origin

Herein, we show that easy ambient sonic spray ionization mass spectrometry in the negative ion mode [EASI(-)-MS] of water:methanol extracts of olive oil samples from 5 different countries (Portugal, Italy, Spain, Lebanon, and Greece) provides very characteristic profiles of chemotaxonomic markers, that is, free fatty acids and phenols. These EASI(-)-MS fingerprints, acquired with great speed and simplicity after minimal sample preparation, permits secure identification of the samples as olive oils via their unique profiles of fatty acids plus phenolic constituents as well the certification of geographical regions via characteristic features of the profiles of phenolic constituents.     

High performance liquid chromatography-mass spectrometry based chemometric characterization of olive oils

In this study the effective discrimination of extra virgin olive oils is described using HPLC-MS, combined with chemometric evaluation. The presented method is simple since the diluted oil sample is directly injected into the system, without any preliminary chemical derivatization or purification step. Separation of diacylglycerols, triacylglycerols and sterols occurs within 20 min and is achieved using an octadecyl-silica column. Detection is performed by positive APCI mass spectrometry which provided sensitivity to detect over 50 compounds in the sample. After extraction of data, stepwise discriminant function analysis is used to select the variables with the highest discriminative power. These variables are used to perform linear discriminant analysis and classify/predict the samples. One-hundred per cent classification and 99% prediction rate was achieved for olive oils obtained from Nocellara, Biancolilla and Cerausola cultivars. Reliability of prediction was tested by cross validation.