Adulteration Of Virgin Olive Oil Research Articles

Detection of Extra Virgin Olive Oil Adulteration With Edible Oils Using Front‐Face Fluorescence and Visible Spectroscopies

AbstractSynchronous front‐face fluorescence and visible spectroscopies are utilized for the simple, rapid, and nondestructive quantification of extra virgin olive oil (EVOO) adulteration with corn, soybean, and sunflower oils. For each adulterant, 42 adulterated EVOO samples in the adulterant amount in the range 1.0–50 g/100 g were prepared. The partial‐least‐squares regression was executed for quantification. Both full (leave‐one‐out) cross‐validation and external validation were performed to evaluate the predictive ability. The plots of observed vs. predicted values exhibit high linearity. The coefficient of determination (R2) values are larger than 0.99. The root mean square errors of both cross‐validation and prediction are no more than 2%. The detection limits for the three seed oils using fluorescence and visible spectroscopies are in the range of 1.5–2.2% and 1.8–2.4%, respectively. The merit of this method is that both the front‐face fluorescence and visible spectroscopies are recorded toward neat oils, avoiding any sample pretreatment including dilution.

Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration

In the present work, two approaches for the voltammetric fingerprinting of oils and their combination with chemometrics were investigated in order to detect the adulteration of extra virgin olive oil with olive pomace oil as well as the most common seed oils, namely sunflower, soybean and corn oil. In particular, cyclic voltammograms of diluted extra virgin olive oils, regular (pure) olive oils (blends of refined olive oils with virgin olive oils), olive pomace oils and seed oils in presence of dichloromethane and 0.1 M of LiClO4 in EtOH as electrolyte were recorded at a glassy carbon working electrode. Cyclic voltammetry was also employed in methanolic extracts of olive and seed oils. Datapoints of cyclic voltammograms were exported and submitted to Principal Component Analysis (PCA), Partial Least Square- Discriminant Analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). In diluted oils, PLS-DA provided a clear discrimination between olive oils (extra virgin and regular) and olive pomace/seed oils, while SIMCA showed a clear discrimination of extra virgin olive oil in regard to all other samples. Using methanolic extracts and considering datapoints recorded between 0.6 and 1.3 V, PLS-DA provided more information, resulting in three clusters-extra virgin olive oils, regular olive oils and seed/olive pomace oils-while SIMCA showed inferior performance. For the quantification of extra virgin olive oil adulteration with olive pomace oil or seed oils, a model based on Partial Least Square (PLS) analysis was developed. Detection limit of adulteration in olive oil was found to be 2% (v/v) and the linearity range up to 33% (v/v). Validation and applicability of all models was proved using a suitable test set. In the case of PLS, synthetic oil mixtures with 4 known adulteration levels in the range of 4–26% were also employed as a blind test set.

Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment.

The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process.

Effect of Temperature on Ultrasonic Signal Propagation for Extra Virgin Olive Oil Adulteration

Fraud cases involving adulteration of extra virgin olive oil has become significant nowadays due to increasing in cost of supply and highlight given the benefit of extra virgin olive oil for human consumption. This paper presents the effects of temperature variation on spectral formed utilising pulse-echo technique of ultrasound signal. Several methods had been introduced to characterize the adulteration of extra virgin olive oil with other fluid sample such as mass chromatography, standard method by ASTM (density test, distillation test and evaporation test) and mass spectrometer. Pulse-echo method of ultrasound being a non-destructive method to be used to analyse the sound wave signal captured by oscilloscope. In this paper, a non-destructive technique utilizing ultrasound to characterize extra virgin olive oil adulteration level will be presented. It can be observed that frequency spectrum of sample with different ratio and variation temperature shows significant percentages different from 30% up to 70% according to temperature variation thus possible to be used for sample characterization.

Detection of adulteration in extra virgin olive oils by using UV-IMS and chemometric analysis

The techniques of ion mobility are attractive for solving problems in the olive oil sector because they are techniques that provide information quickly, do not require sample preparation and represent a shift towards the use of environmentally-sustainable analytical techniques (green chemistry). This technique could be suitable to determine the origin of the olive oil, quality and adulteration. In this work, the authors propose the use of spectral fingerprint supplied by a home-made headspace system coupled to UV-IMS sensor in combination with multivariate calibration tools as an easy alternative for the detection of adulteration of extra virgin olive oil (EVOO) with other vegetable oils such as sunflower, corn and seed oils. For it, 8 EVOO samples were adulterated with different proportion (among 10 and 50%) of these vegetables oils. Therefore, 96 adulterated samples were submitted to different chemometric treatments. The results obtained from PC-LDA-kNN model show the potential of this method to discriminate EVOO adulterated with oils of lower prices. PLS calibration also allows the quantification of the amount of vegetable oils added to EVOO containing at least 10% of other vegetables oils tested with good regression (R2 > 0.72) and RMSEC and RMSEC values lower than 9.22 and 12.62, respectively.

Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification

Within olive oils, extra virgin olive oil is the highest quality and, in consequence, the most expensive one. Because of that, it is common that some merchants attempt to take economic advantage by mixing it up with other less expensive oils, like olive oil or olive pomace oil. In consequence, the characterization and authentication of extra virgin olive oils is a subject of great interest, both for industry and consumers. This paper reports the potential of front-face total fluorescence spectroscopy combined with second-order chemometric methods for the detection of extra virgin olive oils adulteration with other olive oils. Excitation-emission matrices (EEMs) of extra virgin olive oils and extra virgin olive oils adulterated with olive oils or with olive pomace oils were recorded using front-face fluorescence spectroscopy. The full information content in these fluorescence images was analyzed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA-PARAFAC), and discriminant unfolded partial least-squares (DA-UPLS). The discriminant ability of LDA-PARAFAC was studied through the tridimensional plots of the canonical vectors, defining a surface separating the established categories. For DA-UPLS, the discriminant ability was established through the bidimensional plots of predicted values of calibration and validation samples, in order to assign each sample to a given class. The models demonstrated the possibility of detecting adulterations of extra virgin olive oils with percentages of around 15% and 3% of olive and olive pomace oils, respectively. Also, UPLS regression was used to quantify the adulteration level of extra virgin olive oils with olive oils or with olive pomace oils.

Use of zero order diffraction of a grating monochromator towards convenient and sensitive detection of fluorescent analytes in multi fluorophoric systems

White light excitation fluorescence (WLEF) is known to possess analytical advantage in terms of enhanced sensitivity and facile capture of the entire fluorescence spectral signature of multi component fluorescence systems. Using the zero order diffraction of the grating monochromator on the excitation side of a commercial spectrofluorimeter, it has been shown that WLEF spectral measurements can be conveniently carried out. Taking analyte multi-fluorophoric systems like (i) drugs and vitamins spiked in urine sample, (ii) adulteration of extra virgin olive oil with olive pomace oil and (iii) mixture of fabric dyes, it was observed that there is a significant enhancement of measurement sensitivity. The total fluorescence spectral response could be conveniently analysed using PLS2 regression. This work brings out the ease of the use of a conventional fluorimeter for WLEF measurements.

Selection of robust variables for transfer of classification models employing the successive projections algorithm

Multivariate models have been widely used in analytical problems involving quantitative and qualitative analyzes. However, there are cases in which a model is not applicable to spectra of samples obtained under new experimental conditions or in an instrument not involved in the modeling step. A solution to this problem is the transfer of multivariate models, usually performed using standardization of the spectral responses or enhancement of the robustness of the model. This present paper proposes two new criteria for selection of robust variables for classification transfer employing the successive projections algorithm (SPA). These variables are then used to build models based on linear discriminant analysis (LDA) with low sensitivity with respect to the differences between the responses of the instruments involved. For this purpose, transfer samples are included in the calculation of the cost for each subset of variables under consideration. The proposed methods are evaluated for two case studies involving identification of adulteration of extra virgin olive oil (EVOO) and hydrated ethyl alcohol fuel (HEAF) using UV–Vis and NIR spectroscopy, respectively. In both cases, similar or better classification transfer results (obtained for a test set measured on the secondary instrument) employing the two criteria were obtained in comparison with direct standardization (DS) and piecewise direct standardization (PDS). For the UV–Vis data, both proposed criteria achieved the correct classification rate (CCR) of 85%, while the best CCR obtained for the standardization methods was 81% for DS. For the NIR data, 92.5% of CCR was obtained by both criteria as well as DS. The results demonstrated the possibility of using either of the criteria proposed for building robust models as an alternative to the standardization of spectral responses for transfer of classification.

Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach

This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV–Vis spectroscopies. The adulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200, 250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS) using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm (PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms (SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictive ability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy, satisfactory prediction results were obtained for all the regression models with Root Mean Square Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV–Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.

Identification of 3-MCPD esters to verify the adulteration of extra virgin olive oil

ABSTRACTThe adulteration of olive oil is an important issue around the world. This paper reports an indirect method by which to identify 3-monochloropropane-1,2-diol (3-MCPD) esters in olive oils. Following sample preparation, the samples were spiked with 1,2-bis-palmitoyl-3-chloropropanediol standard for analysis using gas chromatograph-tandem mass spectrometry. The total recovery ranged from 102.8% to 105.5%, the coefficient of variation ranged from 1.1% to 10.1%, and the limit of quantification was 0.125 mg/kg. The content of 3-MCPD esters in samples of refined olive oil (0.97–20.53 mg/kg) exceeded those of extra virgin olive oil (non-detected to 0.24 mg/kg). These results indicate that the oil refining process increased the content of 3-MCPD esters, which means that they could be used as a target compound for the differentiation of extra virgin olive oil from refined olive oil in order to prevent adulteration.

FT-NIR the method of choice to verify the authenticity of extra virgin olive oils

Fourier transform near infrared spectroscopy was recently demonstrated to be an excellent method to evaluate the authenticity and adulteration of extra virgin olive oil. Since this method is matrix dependent, it takes a chemical fingerprint of all the components which sets it apart from the targeted methods. Careful examinations of the Fourier transform near infrared spectra lead to the identification of a minor carbonyl overtone absorption at 5269 cm−1 associated with the volatile fraction in extra virgin olive oil that appears to be a reliable indicator of authenticity. The same spectra were used to identify the fatty acids present in the oil using models based on comparison to accurate GC data. Gravimetric mixtures of extra virgin olive oil with refined edible oils were then prepared to develop PLS1 calibration models to identify possible adulterants and by how much. The great varietal difference in olive oils made it necessary to develop four unique sets of PLS1 calibration models for each extra virgin olive oil variety. As a result, an extra virgin olive oil acceptance specification was established.

First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA.

Economically motivated adulteration (EMA) of extra virgin olive oils (EVOO) has been a worldwide problem and a concern for government regulators for a long time. The US Food and Drug Administration (FDA) is mandated to protect the US public against intentional adulteration of foods and has jurisdiction over deceptive label declarations. To detect EMA of olive oil and address food safety vulnerabilities, we used a previously developed rapid screening methodology to authenticate EVOO. For the first time, a recently developed FT-NIR spectroscopic methodology in conjunction with partial least squares analysis was applied to commercial products labeled EVOO purchased in College Park, MD, USA to rapidly predict whether they are authentic, potentially mixed with refined olive oil (RO) or other vegetable oil(s), or are of lower quality. Of the 88 commercial products labeled EVOO that were assessed according to published specified ranges, 33 (37.5%) satisfied the three published FT-NIR requirements identified for authentic EVOO products which included the purity test. This test was based on limits established for the contents of three potential adulterants, oils high in linoleic acid (OH-LNA), oils high in oleic acid (OH-OLA), palm olein (PO), and/or RO. The remaining 55 samples (62.5%) did not meet one or more of the criteria established for authentic EVOO. The breakdown of the 55 products was EVOO potentially mixed with OH-LNA (25.5%), OH-OLA (10.9%), PO (5.4%), RO (25.5%), or a combination of any of these four (32.7%). If assessments had been based strictly on whether the fatty acid composition was within the established ranges set by the International Olive Council (IOC), less than 10% would have been identified as non-EVOO. These findings are significant not only because they were consistent with previously published data based on the results of two sensory panels that were accredited by IOC but more importantly each measurement/analysis was accomplished in less than 5min.

A Hybrid Magnetoelastic Wireless Sensor for Detection of Food Adulteration

This paper investigates a step by step design procedure of a hybrid passive wireless sensor. The hybrid sensor measures both the electrical (dielectric constant) and the mechanical (viscosity) properties of liquid, providing a two-factor quality control. The hybrid sensor is based on an inductor-capacitor resonant tank coupled with a magnetoelastic strip. The mechanical and the electrical resonances change as a function of viscosity and dielectric constant, respectively. Two different hybrid sensor designs are investigated: 1) a parallel plate capacitor coupled with a separate amorphous ferromagnetic magnetoelastic strip (Metglas) and 2) a capacitor made using two parallel mounted magnetoelastic strips. The sensors are integrated as part of the “smart vial” making it field operable for food quality monitoring and control. Here, detection of adulteration in extra virgin olive oil is achieved by measuring the change in viscosity and dielectric constant for different adulteration levels. The real part of the dielectric constant for different liquid samples is measured in the frequency range of 3–24 MHz. The hybrid sensor is able to detect adulteration levels below 10% in volume. These sensors can be integrated with passive RFIDs for simultaneous measurement of multiple samples in an array format.

Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data

This work evaluates the use of transfer of classification models for identifying adulteration of extra virgin olive oil (EVOO) samples involving, separately, two analytical techniques: fluorescence spectroscopy and digital imaging. The chemometric procedures, including development of classification models and application of classification transfer methods, were performed individually for each analytical technique. Methods of direct standardization (DS) and piecewise direct standardization (PDS) were applied to transfer samples sets in order to estimate an adjustment function and apply it to a samples set measured by the secondary instrument. For purposes of comparison, classification models were built based on linear discriminant analysis (LDA) with previous selection of variables by the successive projections algorithm (SPA), and partial least squares discriminant analysis (PLS-DA). The performance of the classification models was evaluated according to the number of errors and correct classification rate (CCR) for the prediction set measured by the secondary instrument. Before standardization, SPA-LDA and PLS-DA models achieved the same CCR using two analytical techniques: 54% for fluorescence emission spectra and 47% for histograms of digital images. After the standardization, a substantial increase of the CCR was observed. For the SPA-LDA models, a CCR of 88% was obtained for the fluorescence emission spectra and 82% for the histograms of the digital images. The PLS-DA classification models reached 85% and 76% of CCR for the fluorescence and imaging data, respectively, after standardization. These results demonstrate the efficiency of standardization procedures applied to multivariate classification models developed from fluorescence spectroscopy and digital images.

Classification and Detection of Adulteration in Olive Oil Using Improved Gaussian Mixture Model and Regression by Artificial Bee Colony Algorithm

Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) can be used to detect adulteration in extra virgin olive oil. The estimate of the GMM parameters is commonly obtained from the expectation- maximization (EM) algorithm. EM algorithm has some limitations such as local optimum problems and sensitivity to the initial values. In this paper, artificial bee colony (ABC) algorithm is used to determine the optimal parameters in GMM and GMR. To improve the optimized performance and reduce computational effort of ABC algorithm, the information sharing mechanism among the global best food sources is introduced in ABC. The improved GMM and GMR by artificial bee colony algorithm (GMMRABC) were used to discriminate and quantify the adulteration of extra virgin olive oil with rapeseed oil using FT-IR spectroscopy. It has been demonstrated that the proposed method is an accurate, rapid, stable strategy for identifying and quantifying the extra virgin olive oil.

Short Distance Standoff Raman Detection of Extra Virgin Olive Oil Adulterated with Canola and Grapeseed Oils.

A short distance standoff Raman technique is demonstrated for detecting economically motivated adulteration (EMA) in extra virgin olive oil (EVOO). Using a portable Raman spectrometer operating with a 785 nm laser and a 2-in. refracting telescope, adulteration of olive oil with grapeseed oil and canola oil is detected between 1% and 100% at a minimum concentration of 2.5% from a distance of 15 cm and at a minimum concentration of 5% from a distance of 1 m. The technique involves correlating the intensity ratios of prominent Raman bands of edible oils at 1254, 1657, and 1441 cm-1 to the degree of adulteration. As a novel variation in the data analysis technique, integrated intensities over a spectral range of 100 cm-1 around the Raman line were used, making it possible to increase the sensitivity of the technique. The technique is demonstrated by detecting adulteration of EVOO with grapeseed and canola oils at 0-100%. Due to the potential of this technique for making measurements from a convenient distance, the short distance standoff Raman technique has the promise to be used for routine applications in food industry such as identifying food items and monitoring EMA at various checkpoints in the food supply chain and storage facilities.

Quantification of soybean oil adulteration in extra virgin olive oil using portable raman spectroscopy

Extra virgin olive oil is produced through either a cold press procedure or a centrifugation with no thermal and chemical treatments and it is considered as the best quality oil under the category of olive oils. The superior properties of olive oil due to its rich in phenolic and antioxidant content and its contribution to prevent several health problems has increased the demand for olive oil over the years. Consequently, it is nowadays sold at remarkably higher price than regular vegetable oils in the market. Unfortunately, extra virgin olive oil (EVOO) has been adulterated with other cheap oils due to potential high commercial profit. Even though, there are methods available to detect the adulteration in EVOO (such as chromatographic methods and PCR), alternative simpler and faster methods are being studied. In this study, performance of portable Raman spectroscopy to quantify soybean oil (SO) adulteration [up to 25 % (w/w)] in EVOO has been evaluated. Partial Least Square Regression (PLSR) calibration models were developed and both internally (using cross-validation, leave-one-out approach) and externally (using an independent sample set) validated. The model gave standard error of prediction (SEP) of 1.34 % (w/w) SO in EVOO and correlation coefficient of prediction (rPred) of 0.99. Additionally, the residual predictive deviation (RPD) value calculated for the model was found to be 5.71, indicating that the model was considered as “good” and could be used for routine analysis and quality control applications.

Soft-deodorization of virgin olive oil: Study of the changes of quality and chemical composition.

The potential adulteration of extra virgin olive oil (EVOO) with soft-deodorized virgin olive oil (VOO) has raised the interest of researchers in investigating this fraud in recent years. The objective of this study was to determine chemical changes occurring after a soft-deodorization process in terms of volatiles, fatty acid ethyl esters (FAEEs) and pyropheophytins (PPPs) concentrations in VOOs. After testing several conditions (80, 100 and 130°C, for 30 and 60min), the parameters of 100°C and 60min were considered as the optima. These conditions allowed removing volatiles responsible for sensory defects with low losses of total phenols (14%-32%), and values of PPPs (11.83%) and FAEEs (34.53mg/kg) lower than the limits of standard regulations. The experiments show that soft-deodorized VOOs could be added up to 50% to EVOOs and current standard methods would not detect this kind of adulteration.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.

Comparative Study using Raman and Visible Spectroscopy of Cretan Extra Virgin Olive Oil Adulteration with Sunflower Oil

ABSTRACTThe adulteration of extra virgin olive oil with low-quality and inexpensive seed oil is a serious problem in the industry. In recent years, the characterization of extra virgin olive oil adulteration with various techniques has been successfully implemented. In this work, a comparative study of Raman and visible spectroscopy is presented. These methods are rapid, noninvasive, and no sample pretreatment is required. We used both methods to study Cretan extra virgin olive oil adulterated with sunflower oil. Statistical analysis based on partial least square regression was used to determine the detection limits of the methods. Raman spectroscopy was superior in comparison to visible spectroscopy with adulteration detection limits of 3.5 and 5.5%, respectively, for the same samples. These results indicate that both techniques are suitable for olive oil quality control.