## 1 Introduction
Validation samples were mixed with various adulterants, including corn, sunflower, canola and soybean oils. Adulterants were detectable with NIR spectroscopy; however, limits of detection were higher than 2.7%. Specifically for corn and sunflower oils, the adulteration limit was 20%, while for canola and soybean oil, it was 10 and 15%, respectively. The fact that the chemical and physical characteristics of an EVOO depend on various parameters such as geographic origin and climate conditions, which consequently influence its chemical profile, including volatile and fatty acid constituents, make more complicate the detection of adulteration, especially when no control sample is provided. Nevertheless, the authors claimed that based on fatty acid content and specifically on palmitic, stearic, oleic, linoleic and linolenic acids, if limits of quantification are precisely determined, adulteration is detectable. However, usually, this is achieved by adding a relatively high percentage of the adulterant. EVOO mixed with corn and sunflower oil were correctly classified as pure or not using PCA and soft dependent modeling of class analogies (SIMCA) classification method. Similarly, adulteration of EVOO with canola oil or soybean oil was studied. In this case, observations were focused on the fatty acid composition and specifically on linoleic acid (LA). The level of this fatty acid is higher at the adulterated agent with respect to EVOO; thus, LA is considered as a candidate marker to distinguish between EVOO and minor quality olive oil. NIR spectroscopy was used by [PERSON] et al. (2019)(28) in combination with PCA and PLS1-DA. The authors managed to adequately classify (100%) Argentinean EVOOs according to their geographical origin when NIR was combined with PLS1-DA analysis and also detect adulteration with oils of minor quality. [PERSON] and [PERSON] (2019)(29) used FT-NIR in combination with bootstrapping soft shrinkage (BOSS) algorithm and PLS analysis to successfully detect adulteration of EVOO with a variety of adulterants, including peanut, sunflower seed oil, soybean, sesame and corn oil. Adulteration was detected and quantified at a level of 5-50%. The authors used different algorithms in order to select the most appropriate wavenumbers, such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV); however, they concluded that the best algorithm to use is the BOSS fitted in the PLS model, as it gave the best value of R\({}^{2}\) (0.99) and the lowest RMSECV value (1.44%). NIR, Mid-infrared spectroscopy (MIR) and Raman spectroscopy were compared in terms of their capacity to predict adulteration of EVOO with soybean oil. In addition, the fatty acid composition of EVOO and soybean was first analyzed with GC-FID. Authors observed that palmitoleic acid (POA) was only found in the EVOO samples; thus, this compound was suggested as a label to quantify adulteration. However, on the contrary to GC-FID analysis, POA is evident in both EVOO and soybean oil at Raman spectroscopy. Nonetheless, quantitative differences of LA were observed, and thus LA was used for the quantification in combination with PLS analysis. The full spectrum was used to analyze the results. Although RMSECV and RMSEP values were higher when MIR spectroscopy was used, the authors concluded that all the methods performed are suitable for detecting EVOO adulteration. This statement was also confirmed by a \(t\)-test which showed no statistical differences between the predicted values and the true ones (30). Similarly, MIR and NIR spectroscopy were used to detect adulteration of EVOO with refined and deodorized oils. The spectral region finally chosen by the authors was 1500-900 cm\({}^{-1}\) and 6150-4500 cm\({}^{-1}\) for MIR and NIR, respectively.