## 1 Introduction
In another study by [PERSON] et al. (2017) [21], both Fourier transform infrared (FT-IR) and Raman spectroscopy were used to distinguish pure olive oil from those mixed with edible oils, using multivariate methods. Among the classification models used, Partial Least-Squares Discriminant analysis (PLS-DA) model gave the best results regarding FT-IR, while for Raman spectroscopy, the Support Vector Machine using parameter C (SVM-C) model was found to be more reliable. In addition, quantification of adulteration, where various amounts of adulterant were examined, was performed using the Partial Least Squares Regression (PLS-R) model. Raman gave better results regarding the R\({}^{2}\) value; on the contrary, the value of quantification error was better when evaluated with the FT-IR. The authors described the developed method as a \"global method\" that allows \"detection, discrimination and quantification\" of virgin olive oil, pure or adulterated. A preliminary step of transesterification was needed prior to analysis. [PERSON] et al. (2002) [22] and [PERSON] et al. (2010) [23] used FT-IR spectroscopy in combination with chemometrics (Discriminant Analysis (DA) and PLS) to evaluate adulteration of EVOO with hazelnut and other oils of minor quality. Both studies concluded that FT-IR could be used as an alternative, quick and economic method for the detection of hazelnut oil. However, [PERSON] et al. (2002) [22] showed that although classification between these two types was successfully achieved, adulteration was only detected at levels higher than 25%. [PERSON] et al. (2010) [24] studied the adulteration of EVOO with various vegetable oils, including palm oil, using FT-IR spectroscopy. DA was performed in order to classify pure and adulterated EVOO. Quantification of the adulterant was performed in EVOO-palm oil mixtures using PLS and PCA models. The obtained results were satisfactory, taking into account the calculated values derived from the models applied that correspond to R\({}^{2}\) equal to 0.999 for both models and RMSECV values equal to 0.285 and 0.373, respectively. The 1500-1000 cm\({}^{-1}\) fingerprint area was chosen to build the chemometric models. In another study, adulteration of EVOO with sunflower and soybean oil was determined with mid-IR spectroscopy. The authors used the PLS and PLS-DA methods to quantify adulteration at a range of 1-24% weight ratios. They focused on the 3035-2800 cm\({}^{-1}\), 1800-1700 cm\({}^{-1}\) and 1400-670 cm\({}^{-1}\) spectral regions for data interpretation. However, in order to reduce the number of the selected variables, the Variable Importance of Projection (VIP) method was performed, which permitted a reduction of approximately 75% of the variables used. Based on R\({}^{2}\), RMSEC, RMSECV and bias values, a strong correlation between the true and predicted values was achieved, confirming that the PLS-VIP method can be successfully used. In addition, a 100% correct classification of the samples was achieved with the PLS-DA analysis [25]. Mid-infrared spectroscopy (MIR) was used by [PERSON] et al. (2015) [26] in order to detect adulteration of EVOO with lampante virgin oil. Two calibration models were constructed, using PLS and PCR analysis, according to which quantification of the adulterant was achieved. [PERSON] et al. (2018), [27] examined several cases of adulteration using NIR. First, they tested the capacity of the method to distinguish between a known sample of EVOO (control) adulterated with corn oil (2.7-25% _w/w_). A good separation between EVOO samples, pure and adulterated, was achieved using the PCA method. In particular, the greater the degree of adulteration, the more major the differences between the tested samples. The authors also examined the case in which NIR spectroscopy could detect the adulteration of unknown EVOO samples. They used in total 37 EVOO samples (separated into 27 calibration and 10 validation models) with different qualitative characteristics, cost, degree of oxidation and fatty acid profile.