Adulteration Of Extra Virgin Olive Oil Research Articles

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA

A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data). The data sets were then processed together using the multi-block fusion method, based on the concept of Cumulative Analytical Signal (CAS). A comparison of the data processing methods in terms of sensitivity, specificity and selectivity showed the following order of excellence: NIR < EEFM < NIR + EEFM. This finding confirms the effectiveness of multi-block data fusion, which cumulatively improves the model performance.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

Detecting adulteration in extra virgin olive oil (EVOO) is particularly challenging with oils of similar chemical composition. This study applies near-infrared hyperspectral imaging (NIR-HSI) and machine learning (ML) to detect EVOO adulteration with hazelnut, refined olive, and olive pomace oils at various concentrations (1%, 5%, 10%, 20%, 40%, and 100% m/m). Savitzky-Golay filtering, first and second derivatives, multiplicative scatter correction (MSC), standard normal variate (SNV), and their combinations were used to preprocess the spectral data, with Principal Component Analysis (PCA) reducing dimensionality. Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). PLS-DA, k-NN, RF, SVM, NB, and ANN models achieved accuracy rates of 97.0–99.0%, 96.2–100%, 96.5–100%, 98.6–99.5%, 93.9–99.7%, and 99.2–100%, respectively, in discriminating between pure EVOO, adulterants, and adulterated oils. PLS-DA, RF, SVM, and ANN significantly outperformed Naïve Bayes (p < 0.05) in binary classification, with Matthews correlation coefficient (MCC) values exceeding 0.90. All the binary classifiers except Naïve Bayes, when coupled with SNV/MSC, Savitzky-Golay smoothing and derivatives, consistently achieved perfect scores (1.0) for accuracy, sensitivity, specificity, F1 score, precision, and MCC in distinguishing pure EVOO from adulterated oils. No significant differences (p > 0.05) in model performance were found between those using full spectra and those based on key variable selection. However, PLS-DA and ANN significantly outperformed k-NN, RF, and SVM (p < 0.05), with MCC values ranging from 0.95 to 1.00, indicating superior classification performance. These findings demonstrate that combining NIR-HSI with machine learning, along with key variable selection, potentially offers an effective, non-destructive solution for detecting adulteration in EVOO and combating fraud in the olive oil industry.

Untargeted 4D-metabolomics using Trapped Ion Mobility combined with LC-HRMS in extra virgin olive oil adulteration study with lower-quality olive oils

Metabolomics is widely established in the field of food authenticity to address demanding issues, such as adulteration cases. Trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) provides an additional analytical dimension, introducing mobility-enhanced metabolomics in four dimensions (4D). In the present work, the potential of LC-TIMS-HRMS as a reliable analytical platform for authenticity studies is being explored, applied in extra virgin olive oil (EVOO) adulteration study. An integrated untargeted 4D-metabolomics approach is being implemented to investigate adulteration, with refined olive oils (ROOs) and olive pomace oils (OPOs) set as adulterants. Robust prediction models are built, successfully discriminating authentic EVOOs from adulterated ones and highlighting markers in each group. Noteworthy outcomes are retrieved regarding TIMS added value in LC-HRMS workflows, resulting in a significant increase of metabolic coverage, while, thanks to platform’s enhanced sensitivity, detection of adulteration is being achieved down to 1%.

A combined classification modeling strategy for detection and identification of extra virgin olive oil adulteration using Raman spectroscopy

Identifying and discriminating of pure edible oils as well as detecting and identifying the type of adulteration, are essential issues in the field of food-related research. Extra virgin olive, hazelnut, canola, soya, and sunflower oils were investigated by Raman spectroscopy to answer two main questions: How can it be recognized that an unknown sample of edible oil belongs to one of these five types of pure oil? Also, if the unknown sample is adulterated extra virgin olive oil, how can the type and composition of that sample be recognized? The problem-based research was planned by proposing a combined classification strategy for achieving the proper solutions. In this approach, class modeling was combined with discriminant analysis to benefit from the advantages of both methods. In the first step, the Raman spectra of five pure edible oil classes were used to model the target objects by DD-SIMCA to exclude adulterated samples. Then PLS-DA was used for the discrimination of five edible oils classes. This strategy was successfully applied for several binary classes, including different types of extra virgin olive oil adulterations with different compositions. Raman spectroscopy, accompanied by the proposed combined classification strategy, showed good power in solving the pattern recognition problems in food adulteration subjects. The proposed combined classification strategy allowed the discrimination between pure edible oil samples with up to 100% of correct classifications. The developed method showed also high performances based on the figures of merit (up to 100% sensitivity and specificity) for the detection and identification the type of adulteration in extra virgin olive oil.

Application of Electronic Nose and Eye Systems for Detection of Adulteration in Olive Oil based on Chemometrics and Optimization Approaches

In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses.&amp;nbsp;

A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.

Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil: A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.

Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0-20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R2pred (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.

Simultaneous Concurrent Assessment of Extra Virgin Olive Oil Adulteration via Fourier Transform Mid-Infrared and UV-Visible Spectroscopy Combined with Partial Least Squares Regression

Adulteration of olive oil is a common practice in the industry, where old and commercial oils are mixed with fresh olive oils. Adulteration can negatively affect the quality and authenticity of olive oil, leading to economic fraud and health concerns. Therefore, identifying and quantifying adulteration in olive oil is crucial for ensuring product quality and consumer protection. The objective of this study was to identify and measure the adulteration of fresh olive oils with old oil and commercial oil from the previous harvest year. The study aimed to achieve this goal using spectroscopic techniques in combination with chemometrics. Different spectroscopic techniques, such as FT-MIR and UV-vis spectroscopy, were utilized in this study. Partial least squares (PLS) regression was applied to predict the levels of adulteration in the samples with varying concentrations (0.84 - 52.13 % w/w). Various pre-treatment methods were employed for both FTMIR and UV-Vis spectral data. All the PLS models generated for FT-MIR and UV-Vis spectral data were successful in predicting the levels of adulteration, with high coefficients of determination for both calibration (0.963 - 0.995) and cross validation (0.935 - 0.993) models. The error values for calibration (0.621 % - 2.728 %) and cross validation (0.730 % - 3.314 %) were also low. Based on the results, it was found that the use of second derivative preprocessing for FT-MIR data and SNV preprocessing for UV-Vis data led to the best performance results in quantifying the level of adulteration of olive oil. Spectroscopic techniques in combination with chemometrics can be used to identify and measure the adulteration of olive oil.

Development of fast analytical method for the detection and quantification of Moroccan picholine extra virgin olive oil adulteration using MIR spectroscopy and chemometrics tools

In this study, the adulteration of Moroccan Picholine extra virgin olive oil with Arbequina virgin olive oil was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics methodologies. To discriminate between olive oil that has been adulterated and unadulterated, principal component analysis (PCA) was utilized for qualitative analysis. We created the best calibration models for quantitative analysis using principal component regression (PCR) and partial least-squares regression (PLS). The first three principal components account for 95% of the overall variability, according to PCA analysis. PCA allows for the classification of the dataset into two groups: adulterated and unadulterated Moroccan Picholine olive oil. The application of the PLS and PCR calibration models for the quantification of adulteration demonstrates high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999 and 0.995 and lower values of root mean square error (RMSE) less than 0.767 and 2.16 using PLS and PCR, respectively. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification and discrimination of adulterated olive oil.

Detection of ternary mixtures of virgin olive oil with canola, hazelnut or safflower oils via non-targeted ATR-FTIR fingerprinting and chemometrics

Detection of extra virgin olive oil (EVOO) adulteration with ternary mixtures of undeclared foreign oils has rarely been reported in literature. The aim of the study was to develop a fit-for-purpose and highly sensitive non-targeted fingerprinting method via ATR-FTIR spectroscopy and qualitative modelling to detect ternary blends of a cheap unsaturated oil (safflower), two high in oleic acid oils (canola and hazelnut oils) at a standard adulteration level (20% v/v). An in-house spectral library for authentic EVOOs was exploited as reference data source. The 2nd derivative ATR-FTIR spectra were carefully explored for outliers throughout the multivariate calibration procedure using the Partial Least Squares Projection to Latent Structures-Discriminant Analysis (PLS-DA) and the Soft Independent Modelling of Class Analogy (SIMCA) methods. Stepwise external validation of the resulting models signified 100% sensitivity in detecting challenging cases of adulteration, i.e. with 5% canola and 15% hazelnut oils. Taken the natural variability in composition of authentic EVOOs from different producing regions and harvest year and the complexity of the studied mixtures, the overall predictive power of every resulting model was found quite high, >92%. A prospective for expanding application at lower adulteration levels i.e. 10% v/v was also evidenced.

Discrimination on potential adulteration of extra virgin olive oils consumed in China by differential scanning calorimeter combined with dimensionality reduction classification techniques

Thermal properties of extra virgin olive oil (EVOO) consumed in China were investigated through differential scanning calorimetry (DSC), Principal Component Analysis (PCA) dimension reduction analysis combined with K-nearest neighbors (KNN) and support vector machine (SVM) models were used to determine whether oil samples would be EVOO. Both models exhibited a 100% ability to distinguish EVOO from non-EVOO. Additionally, the discrimination test of canola oil was still limited from EVOO adulteration due to its similarity of cooling profiles with EVOO. Canola oil was artificially added into EVOO in 5%, 10%, 20% and 30% and DSC test results collected were detected in computer models. Both models effectively identified even 5% canola oil adulterated in EVOO; however, KNN (93.75%) model exhibited much higher accuracy than SVM (43.92%). In conclusion, DSC combined with KMN model analysis was more available for the adulteration detection of EVOO.

Determination of some edible oils adulteration with paraffin oil using infrared spectroscopy

The spectroscopy of molecular vibrations using mid-infrared or near-infrared techniques was used more and more to characterize different compounds, including edible oil, in order to monitor any changes and to detect fraudulent modifications. This article presents a new method for quantitative adulteration of extra virgin olive oil (EVOO) or corn germ oil (CGO) with a mineral oil, such as paraffin oil (PO). A Fourier transform infrared (FT-IR) spectrometric method, using ATR spectra, was developed for the rapid, direct measurement of edible oils adulteration. The results indicate the efficiency of the proposed method for the detection of paraffin oil in adulteration of EVOO and CGO with RSD (&amp;lt; 3.0%). Graphical abstract:

Rapid Identification of Adulteration in Extra Virgin Olive Oil via Dynamic Headspace Sampling and High-Pressure Photoionization Time-of-Flight Mass Spectrometry.

High-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic headspace sampling was developed for rapid identification of adulteration in extra virgin olive oil (EVOO). The volatile organic compound (VOC) fingerprints of EVOO, refined rapeseed oil (r-RO), peanut oil (PO), corn oil (CO), fragrant rapeseed oil (f-RO), and sunflower oil (SO) were obtained in just 1.5 min, which enabled satisfactory classification of different edible oils. 1,4-Bis(methylene)cyclohexane and dimethyl disulfide were unique VOCs in r-RO and f-RO, respectively, while 2,5-dimethylpyrazine and 2-methylpyrazine were distinctive VOCs in PO. Percentages as low as 3% r-RO, 1% PO, and 1% f-RO in r-RO-EVOO, PO-EVOO, and f-RO-EVOO mixtures, respectively, were successfully identified based on the characteristic VOCs. Linear regression equations of these VOCs were established and utilized for predicting the adulteration proportions. The good agreements between the actual adulteration proportions and the predicted ones demonstrated that HPPI-TOFMS was reliable for the quantification of EVOO adulteration.

FTIR coupled with machine learning to unveil spectroscopic benchmarks in the Italian EVOO

AbstractNon‐destructive analytical analyses coupled with classification and regression algorithms are promising techniques for monitoring quality, traceability and safety assessment in food industry. To prevent food fraud, Italian Extra Virgin Olive Oil (EVOO) is particularly held in check. Here, attenuated total Reflectance‐Fourier Transform Infrared spectroscopy (ATR‐FTIR) with Machine Learning is carried out to study an Italian EVOO data set coming from 6 regions to verify the geographical traceability, the cultivar and the repeatability of the agronomical practices, till the adulterated EVOO from soy and corn. The present work is carried out without reagents or esterification processes and considering the entire frequency range without any spectral windows selection, drastically reducing time and costs. Toscana, Lazio, Puglia and Calabria result regions well reproducible in terms of geo‐traceability, unlike the Sicilia and Umbria. The model extracts spectral benchmarks in EVOO in the following vibrational modes at 3004, 2952, 2922, 2852, 1742 and 1160 cm‐1.

Extra virgin olive oil: A comprehensive review of efforts to ensure its authenticity, traceability, and safety.

The growing demand for extra virgin olive oil (EVOO), appreciated for its unique organoleptic properties and health benefits, has led to various fraudulent practices to maximize profits, including dilution with lower value edible oils. The adulterated oils would be of poor nutritional quality, more readily oxidized, and may contain unhealthy substances formed during processing. Nevertheless, the range of available techniques to detect fraud in EVOO production has been growing. Reliable markers of EVOO adulteration include fatty acids and minor components such as sterols, tocopherols, triterpene alcohols, phenolic compounds, phospholipids, volatile compounds, and pigments. Additionally, increasing consumer interest in high-quality EVOO has led to the development of robust scientific methods for its traceability. This review focuses on (i) the usefulness of certain compounds as markers of EVOO adulteration; (ii) the potential health risks of consuming adulterated EVOO; and (iii) reliable methods for the geographical traceability of olive oil. In conclusion, fraudulent production practices need to be detected to preserve the beneficial health effects of EVOO and to avoid the potential risks associated with ingesting substandard oil. In this work, as EVOO certification and regulatory framework limitations have already been extensively reviewed, we focus our attention on biomarkers that guarantee both the authenticity and traceability of oil, and consequently its health properties. When it is unavailable to obtain a high-resolution mass spectrometry full fingerprint, stigmastadienes and the sterolic profile are proposed as reliable markers.

Metabolic fingerprinting strategy: Investigation of markers for the detection of extra virgin olive oil adulteration with soft-deodorized olive oils

As extra virgin olive oil (EVOO) is a high value commodity, it might be subject of various fraudulent practices. This study is focused on a challenging authentication issue, addition of lower grade, soft-deodorized olive oil to EVOO. In the first step, sample sets of authentic EVOOs, soft-deodorized oils and their admixtures were extracted by aqueous methanol; obtained polar fractions were then analysed by ultra-high performance liquid chromatography coupled to hybrid quadrupole time-of-flight high-resolution tandem mass spectrometry (UHPLC-QTOF-HRMS/MS). Subsequent chemometric evaluation of metabolic fingerprints enabled suggestion of several ions that might be characteristic for deodorized oils; most of tentatively identified compounds were oxidized fatty acid derivatives. In the second phase, the ‘marker' ions were employed for target analysis by ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS) what enabled achieving lower the detection limits. Two compounds were selected as the best markers for detection of soft-deodorized olive oil addition, tentatively identified as methyl ester of hydroxy octadecenoic acid and ester derivative of oleic acid. • Addition of soft-deodorized olive oil into extra virgin olive oil was studied. • Untargeted approach revealed new markers emerging in soft-deodorization process. • Markers were successfully tested by target approach to be used in routine practice. • Best markers were tentatively identified as fatty acid esters.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy.

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

EXPERIMENTO DIDÁTICO DE QUIMIOMETRIA EMPREGANDO IMAGENS DIGITAIS OBTIDAS POR CELULAR PARA DETERMINAR ADULTERAÇÃO DE AZEITE DE OLIVA COM ÓLEO DE SOJA: UM TUTORIAL, PARTE VI

TEACHING EXPERIMENT IN CHEMOMETRICS USING DIGITAL IMAGES OBTAINED BY MOBILE PHONE TO DETERMINE ADULTERATION OF OLIVE OIL WITH SOYBEAN OIL: A TUTORIAL, PART VI. The aim of this manuscript was to show the concepts involving data treatment of RGB based image analysis by univariate and multivariate approaches – the latter using Partial Least Squares (PLS) - using as a practical example application the determination of extravirgin olive oil adulteration. Digital images were collected using a device made of LEDs, batteries and a smartphone and results are shown in a tutorial format using Matlab computing environment. The experiment can serve as an example for teaching the subject for undergraduate and graduate students.

Identification of Adulterated Extra Virgin Olive Oil by Colorimetric Sensor Array

Identification of Adulterated Extra Virgin Olive Oil by Colorimetric Sensor Array

Use of portable Raman spectroscopy in the quality control of extra virgin olive oil and adulterated compound oils

The practice of defrauding food and other consumer goods is growing in recent years, and products with high added value are the main targets of this practice, such as extra virgin olive oil (EVOO). In this paper, we evaluated eight samples of EVOO and four samples of compound oils (blends of soybean oil with EVOO) of different commercial brands. For this, we used the portable Raman spectroscopy in the region of 800 to 1800 cm−1, aiming to identify the adulteration of EVOO using soybean oil. Analytical curves were constructed with different concentrations, ranging from 0 to 100 wt% of vegetable oil in EVOO to build regression models. They were developed by chemometric tools, as partial least squares (PLS) and partial least squares by intervals (iPLS). Besides, a univariate model was elaborated based on the ratio of the bands 1440 cm−1 (CH2) and 1655 cm−1 (CC). These models were applied to predict the concentration of adulterant in twelve commercial samples. The iPLS method presented the best analytical results, with R2p of the 0.9967 and RMSEP of 0.0198 wt%, in addition to the limits of detection and quantification of 0.9731 and 3.2436 wt%, respectively. Among the commercial samples evaluated, seven presented higher soybean oil concentrations than the manufacturer's label, thus characterizing the adulteration of the product. Principal component analysis (PCA) was also used to qualitatively evaluate the degree of similarity between commercial EVOO samples, blends, and vegetable oils. The combination of the portable analytical technique with the chemometric methods allowed the identification and quantification, in loco, of adulterants present in commercial EVOO samples.