Pure Extra Virgin Olive Oil Research Articles

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.

Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network

Phthalates are commonly used plasticizers in the plastics industry, and have received extensive attention due to their reproductive toxicity. Since phthalates are lipophilic solutions, phthalates can easily migrate from packaging to edible oils. This study synthesized stable and sensitive Gold Nanostars as SERS substrates to conduct qualitative and quantitative analysis of two common phthalates, dibutyl phthalate and di(2-ethylhexyl) phthalate. Two ethanol standard solutions and actual oil solutions of phthalates at different concentrations (10, 5, 1, 0.5, 0.1, 0.02 mg/kg) were prepared. After dimension reduction, LSTM achieved the accuracy of 98% for pure EVOO and EVOO adulterated with different types of phthalates. In terms of quantification, LSTM demonstrates great predictive performance with Rp2 greater than 0.97 and the ratio of performance to deviation greater than 5. These results have certain guiding significance for the analysis of plasticizers in edible 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;

In-House Validation of an SPE-GC-FID Method for the Detection of Free and Esterified Hydroxylated Minor Compounds in Virgin Olive Oils.

Minor compounds in vegetable oils are distributed between free and esterified forms, and the ratio of these two fractions could represent an important parameter for assessment of oil authenticity. A simple method based on offline SPE-GC-FID for the analysis of free and esterified hydroxylated minor compounds in olive and sunflower oils has been developed and in-house validated. A satisfactory repeatability relative standard deviation (<7.5%) was obtained in all cases. The method, which requires simple instrumentation, allows for reliable quantification in a single chromatographic run with the advantages of minimizing sample manipulation, use of toxic solvents and reagents, and time consumption. The analytical procedure was applied to pure oil samples, including 15 authentic extra virgin olive oils collected from different European countries (Spain, Italy, Greece, and Portugal). Finally, the proposed SPE-GC-FID methodology could detect changes in the ratio between the free and esterified forms in pure extra virgin olive oil when mixed with refined sunflower oil at different percentages of 2, 5, 10, 15, and 20% (w/w) to simulate adulteration.

Potential of front face fluorescence spectroscopy and fluorescence imaging in discriminating adulterated extra-virgin olive oil with virgin olive oil

Due to the health benefits of extra virgin olive oil (EVOO) and its increasing consumption patterns, adulteration of these oils using lower grade olive oils like virgin olive oil (VOO) and then later sold as EVOO to unsuspecting consumers has become a concern in the industry. There is a need for quick, reliable and inexpensive detection techniques to unmask such vice in order to realize its benefits. In this study, adulteration of EVOO is made using VOO at different proportions; their Excitation Emission Matrices (EEM) were examined using Front-face fluorescence spectroscopy and their corresponding fluorescence images captured using 365 nm ultra-violet (UV) LED for analysis. Specific regions for both the EEM and imaging were explored using Support Vector Machine (SVM) classification analysis and their sensitivities evaluated using Partial Least Squares Regression (PLSR) analysis. It was found that EEM excitation 230–500 and emission 260–620 nm could easily discriminate between different samples as EVOO mixed (at different ratios) and VOO. The significant fluorescence peaks for the discrimination forms a combination of Tocopherols, Tocotrienols, Phenolic compounds, Oxidation products and Vitamin E. The results show that Front-face fluorescence spectroscopy and UV-induced fluorescence imaging can potentially discriminate between pure EVOO and adulterated olive oils.

Rapid screening for hazelnut oil and high‐oleic sunflower oil in extra virgin olive oil using low‐field nuclear magnetic resonance relaxometry and machine learning

As extra virgin olive oil (EVOO) has high commercial value, it is routinely adulterated with other oils. The present study investigated the feasibility of rapidly identifying adulterated EVOO using low-field nuclear magnetic resonance (LF-NMR) relaxometry and machine learning approaches (decision tree, K-nearest neighbor, linear discriminant analysis, support vector machines and convolutional neural network (CNN)). LF-NMR spectroscopy effectively distinguished pure EVOO from that which was adulterated with hazelnut oil (HO) and high-oleic sunflower oil (HOSO). The applied CNN algorithm had an accuracy of 89.29%, a precision of 81.25% and a recall of 81.25%, and enabled the rapid (2 min) discrimination of pure EVOO that was adulterated with HO and HOSO in the volumetric ratio range of 10-100%. LF-NMR coupled with the CNN algorithm is a viable candidate for rapid EVOO authentication. © 2020 Society of Chemical Industry.

A Non-Targeted High-Resolution Mass Spectrometry Study for Extra Virgin Olive Oil Adulteration with Soft Refined Oils: Preliminary Findings from Two Different Laboratories.

This work presents a non-targeted high-resolution mass spectrometry inter-laboratory study for the detection of new chemical markers responsible of soft refined oils addition to extra virgin olive oils. Refined oils (soft deodorized and soft deacidified) were prepared on a laboratory scale starting from low-quality olive oils and analyzed together with a set of pure extra virgin olive oil (EVOO) samples and with mixtures of adulterated and pure EVOO at different percentages. The same analytical workflow was applied in two different laboratories equipped with two types of instrumentation (Q-Orbitrap and Q-TOF); a group of discriminant molecules was selected, and a tentative identification of compounds was also proposed. In summary, 12 molecules were identified as markers of this specific adulteration, and seven of them were selected as discriminative in both the laboratories, with a similar trend throughout the samples (i.e., propylene glycol 1 stearate). The results obtained in the two laboratories are comparable, concretely demonstrating the inter-laboratory repeatability of non-targeted studies. As a confirmation, the same markers were detected also in “in-house” mixtures and in suspect commercial deodorized mixtures, reinforcing the robustness of the results obtained and proving that, thanks to these molecules, mixtures containing at least 40% of adulterated oils can be detected.

Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate calibration and discriminant analysis for authentication of extra virgin olive oil from rambutan seed fat

The adulteration practice in pharmaceutical industries, especially in fats and oils used as a vehicle in some pharmaceutical products must be identified to assure its quality. In this study, Fourier Transform Infrared Spectroscopy (FTIR) in combination with chemometrics techniques of multivariate calibration and discriminant analysis (DA) were used for the authentication of extra virgin olive oil (EVOO) from rambutan seed fat (RSF). EVOO, RSF, and the mixture of EVOO-RSF were scanned using FTIR spectrophotometer at mid-infrared region (4000-650 cm-1 ). The results showed that normal FTIR spectra at wavenumbers region of 1446.8-1409.7 cm-1 and 2368.6-1769.9 cm-1 combined with principle component regression (PCR) offered the best quantitative model for prediction of RSF levels in EVOO. The coefficient of determination (R2 ) values obtained for the relationship between actual values of RSF and predicted values were of 0.9955 and 0.9915 in calibration and prediction models, respectively. The errors in calibration and prediction models were relatively low, accounting of 2.17% and 3.68%, respectively. The classification model between unadulterated or pure EVOO and adulterated EVOO with RSF was successfully carried out using DA at wavenumbers of 3100-1000 cm-1 without any samples mistakenly classified into the wrong group. FTIR spectroscopy in combination with chemometrics offered effective tools for authentication of EVOO against the adulteration practice.

Cognitive fluorescence sensing to monitor the storage conditions and locate adulterations of extra virgin olive oil

In the present research, storage conditions of extra virgin olive oil (EVOO) have been monitored using cost-effective fluorescence sensors integrated with intelligent algorithms. Three different Spanish pure EVOOs (Arbequina, Cornicabra, and Picual varieties), as well as samples adulterated with expired EVOO, were kept under different environmental conditions: presence or absence of light, bottle material (plastic or glass), and clearness of glass (uncolored or brown glass). To evaluate the effect of these conditions, 54 samples were prepared, and their emission spectra were measured 10 times during a 58-day period. The most statistically relevant information from these spectra was located by the relief-F feature selection method, which led to the design of machine learning-based models. With this aim, up to 158,250 artificial neural network-based models were designed and tested. It was possible to distinguish different storage conditions in terms of light exposure, bottle material, and clearness, as well as locate adulterations. All of the trained and optimized classifiers for all three EVOO varietals reached accuracies ranging between 91 and 100% after meticulous validations. The used technique is fast, portable, user-friendly, inexpensive, and non-destructive, and, therefore, it could be of great use for the olive oil sector as it enables real-time quality control and fraud detection.

Stimulated Brillouin scattering in combination with visible absorption spectroscopy for authentication of vegetable oils and detection of olive oil adulteration.

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil (EVOO) possesses a higher price than that of other vegetable oils. Adulteration of pure EVOO with other types of vegetable oils has attracted increasing attentions. In this work, a stimulated Brillouin scattering (SBS) combined with visible absorption spectroscopy method is proposed for authentication of vegetable oils and detection of olive oil adulteration. The results provided here have demonstrated that the different vegetable oils and adulteration oils exhibit significant differences in normalized absorbance values of two relevant wavelengths (455 and 670 nm) and frequency shifts of SBS. The normalized absorbance values of all spectra at the two relevant wavelengths of 670 nm and 455 nm linearly decrease with the increase of the adulteration concentration. The Brillouin frequency shifts exponentially increase with the increase of the adulteration concentration. Due to non-destructive and requiring no sample pretreatment procedure, this method can be effectively employed for authentication and detection of oils adulteration.

Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics.

Due to high price and nutritional values of extra virgin olive oil (EVOO), it is vulnerable to adulteration internationally. Refined oil or other vegetable oils are commonly blended with EVOO and to unmask such fraud, quick, and reliable technique needs to be standardized and developed. Therefore, in this study, adulteration of edible oil (sunflower oil) is made with pure EVOO and analyzed using fluorescence spectroscopy (excitation wavelength at 350 nm) in conjunction with principal component analysis (PCA) and partial least squares (PLS) regression. Fluorescent spectra contain fingerprints of chlorophyll and carotenoids that are characteristics of EVOO and differentiated it from sunflower oil. A broad intense hump corresponding to conjugated hydroperoxides is seen in sunflower oil in the range of 441-489 nm with the maximum at 469 nm whereas pure EVOO has low intensity doublet peaks in this region at 441 nm and 469 nm. Visible changes in spectra are observed in adulterated EVOO by increasing the concentration of sunflower oil, with an increase in doublet peak and correspondingly decrease in chlorophyll peak intensity. Principal component analysis showed a distinct clustering of adulterated samples of different concentrations. Subsequently, the PLS regression model was best fitted over the complete data set on the basis of coefficient of determination (R2), standard error of calibration (SEC), and standard error of prediction (SEP) of values 0.99, 0.617, and 0.623 respectively. In addition to adulterant, test samples and imported commercial brands of EVOO were also used for prediction and validation of the models. Fluorescence spectroscopy combined with chemometrics showed its robustness to identify and quantify the specified adulterant in pure EVOO.

Probing temperature able to improve Raman spectroscopic discrimination of adulterated olive oils

An effective method for improving Raman spectroscopic identification of adulterated extra virgin olive oil (EVOO) was proposed. It was based on probing an optimal sample temperature where the spectral differentiation among components was enhanced, since temperature-induced spectral variations of individual components expected to be diversely different depends on their molecular structures. Initially, oleic and linoleic acids were separately frozen, and Raman spectra were consecutively collected at regular intervals as the temperature increased. The spectral variations of oleic and linoleic acids were dissimilar with each other due the difference in their molecular structures. In the temperature-varied spectra of pure EVOOs and adulterated EVOOs, both the magnitudes and patterns of the spectral differences between the two groups differed, and the measurement at −36.4°C resulted in the lowest discrimination error of 8.0% based on linear discriminant analysis using principal component scores. The spectral features of the two groups became more discernible at this temperature due to the dissimilar temperature-dependent molecular vibrations of the components, which improved the authentication accuracy.

Non-thermal plasma as preparative technique to evaluate olive oil adulteration

In recent years adulteration of pure extra virgin olive oil (EVOO) with other types of vegetable oils has become an important issue. In this study, non-thermal plasma (NTP) is investigated as an innovative preparative analytical technique enabling classification of adulterated olive oil from an ascertained authentic batch of olive oil in a more sensitive manner. Non-thermal plasma discharges are a source of highly oxidative species such as singlet oxygen, and atomic oxygen. It was assumed that NTP-induced oxidation triggers unique lipid oxidation mechanisms depending on the specific composition of the oil matrix and minor constituents. In this work EVOO samples were adulterated with sunflower oil (1–3%) and submitted to NTP treatment. Results showed that while untreated samples could not be classified from the authentic olive oil reference, NTP treatments of 60min (Ar/O2 0.1%) on the oil batches resulted in the formation of a unique set of secondary volatile lipid oxidation products enabling classification of adulterated oil samples.

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25°C) and the other group was thermally treated in a thermostatic water bath at 75°C for 8h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720nm at 20nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75°C–25°C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration.

Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis

We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25°C) and the other group was thermally treated in a thermostatic water bath at 75°C for 8h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20nm, 40nm, 60nm and 80nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75–25°C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

The Use of FTIR Spectroscopy and Chemometrics for Rapid Authentication of Extra Virgin Olive Oil

AbstractThe authenticity of high value edible fats and oils including extra virgin olive oil (EVOO) is an emerging issue, currently. The potential employment of Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of multivariate calibration and discriminant analysis has been exploited for rapid authentication of EVOO from canola oil (Ca‐O). The optimization of two calibration models of partial least square (PLS) and principle component regression was performed in order to quantify the level of Ca‐O in EVOO. The chemometrics of discriminant analysis (DA) was used for making the classification between pure EVOO and EVOO adulterated with Ca‐O. The individual oils and their blends were scanned on good contact with ZnSe crystals in horizontal attenuated total reflectance, as a sampling technique. The wavenumbers of 3,028–2,985 and 1,200–987 cm−1 were used for quantification and classification of EVOO adulterated with Ca‐O. The results showed that PLS with normal FTIR spectra was well suited for quantitative analysis of Ca‐O with a value of the coefficient of determination (R2) &gt; 0.99. The error, expressed as root mean square error of calibration obtained was relatively low, i.e. 0.108 % (v/v). DA can make the classification between pure EVOO and that adulterated with Ca‐O with one misclassified reported.

Quantification and Classification of Corn and Sunflower Oils as Adulterants in Olive Oil Using Chemometrics and FTIR Spectra

Commercially, extra virgin olive oil (EVOO) is subjected to be adulterated with low-price oils having similar color to EVOO. Fourier transform infrared (FTIR) spectroscopy combined with chemometrics has been successfully used for classification and quantification of corn (CO) and sunflower oils (SFOs) in EVOO sets. The combined frequency regions of 3027–3000, 1076–860, and 790–698 cm−1 were used for classification and quantification of CO in EVOO; meanwhile, SFO was analyzed using frequency regions of 3025–3000 and 1400–985 cm−1. Discriminant analysis can make classification of pure EVOO and EVOO adulterated with CO and SFO with no misclassification reported. The presence of CO in EVOO was determined with the aid of partial least square calibration using FTIR normal spectra. The calibration and validation errors obtained in CO's quantification are 0.404 and 1.13%, respectively. Meanwhile, the first derivative FTIR spectra and PLS calibration model were preferred for quantification of SFO in EVOO with high coefficient of determination (R 2) and low errors, either in calibration or in validation sample sets.

Updating a Synchronous Fluorescence Spectroscopic Virgin Olive Oil Adulteration Calibration to a New Geographical Region

Detecting and quantifying extra virgin olive adulteration is of great importance to the olive oil industry. Many spectroscopic methods in conjunction with multivariate analysis have been used to solve these issues. However, successes to date are limited as calibration models are built to a specific set of geographical regions, growing seasons, cultivars, and oil extraction methods (the composite primary condition). Samples from new geographical regions, growing seasons, etc. (secondary conditions) are not always correctly predicted by the primary model due to different olive oil and/or adulterant compositions stemming from secondary conditions not matching the primary conditions. Three Tikhonov regularization (TR) variants are used in this paper to allow adulterant (sunflower oil) concentration predictions in samples from geographical regions not part of the original primary calibration domain. Of the three TR variants, ridge regression with an additional 2-norm penalty provides the smallest validation sample prediction errors. Although the paper reports on using TR for model updating to predict adulterant oil concentration, the methods should also be applicable to updating models distinguishing adulterated samples from pure extra virgin olive oil. Additionally, the approaches are general and can be used with other spectroscopic methods and adulterants as well as with other agriculture products.

DETECTION OF OLIVE OIL ADULTERATION WITH RAPESEED AND SUNFLOWER OILS USING MOS ELECTRONIC NOSE AND SMPE‐MS

ABSTRACT The study analyzed the effectiveness of two types of electronic nose systems to detect adulteration of extra virgin olive oil (EVOO) with rapeseed and sunflower oils. Tested methods included volatile analysis with the electronic nose based on MOS sensors (HS‐E nose) and by direct coupling of SPME to MS (SPME‐MS). Volatile compounds were analyzed also by SPME‐GC/MS. Samples of EVOO were mixed with different proportions, ranging from 5 to 50% (v/v), of seed oils and fingerprints of volatile profiles of all samples were generated. In order to obtain as much chemical information as possible and to find a volatile marker to detect adulterations of EVOO with seed oils, principal component analysis (PCA) and partial least squares (PLS) analyses were applied to the data. The application of PCA and PLS analyses to the data from two electronic noses and SMPE‐GC/MS were sufficient to differentiate the adulterated samples from pure EVOO. Excellent results were obtained in the prediction of the percentage of adulteration by PLS analysis. SPME‐GC‐MS analysis with subsequent PCA yielded good results; however, it was time‐consuming. The two electronic noses, with subsequent PCA treatment of data, offering the advantages of rapidity and reliability, enabled detection of olive oil adulteration with different contents of seed oils. PRACTICAL APPLICATIONSVirgin olive oil is highly appreciated by consumers due to its nutritional benefits. Thus, its adulteration with low‐grade olive oils or cheaper vegetable oils could potentially be very profitable for sellers or raw material suppliers and may yield large economic profits. In this way, authentication of virgin olive oils has become an interesting subject from both commercial and health perspectives. It has been proved that the two proposed types of electronic nose systems facilitate reliable detection of rapeseed and sunflower oils in extra virgin olive oil. Both MOS and MS electronic noses are faster than the conventional SMPE‐GC/MS analysis. These well‐correlated methodologies, offering the advantages of rapidity and reliability, opened up a new way of detecting adulteration of virgin olive oils.

Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil

Fourier transform infrared (FTIR) spectroscopy has been developed for analysis of extra virgin olive oil (EVOO) adulterated with palm oil (PO). Measurements were made on pure EVOO and that adulterated with varying concentrations of PO (1.0–50.0% wt./wt. in EVOO). Two multivariate calibrations, namely partial least square (PLS) and principle component regression (PCR) were optimized for constructing the calibration models, either for normal spectra or its first and second derivatives. The discriminant analysis (DA) was used for classification analysis between EVOO and that adulterated with PO and the other vegetable oils (palm oil, corn oil, canola oil, and sunflower oil). Frequencies at fingerprint region, especially at 1500–1000 cm−1, were exploited for both quantification and classification. Either PLS or PCR at first derivative spectra revealed the best calibration models for predicting the concentration of adulterated EVOO samples, with coefficient of determination (R2) of 0.999 and root mean standard error of cross validation (RMSECV) of 0.285 and 0.373, respectively. DA was able to classify pure and adulterated samples on the basis of their FTIR spectra with no misclassified group obtained. In addition, DA was also effective enough to classify EVOO samples as the distinct group from the evaluated other vegetable oils.