## 1 Introduction
Both techniques were combined with PLS and PCR analysis, and the best cross-validation results according to R\({}^{2}\) and RMSECV values were obtained with the MIR-PLS model (R\({}^{2}\) = 0.98, RMSECV = 2.9) (31). FT-NIR spectroscopy was also applied by [PERSON] et al. (2016)(20), who classified OO samples (labeled as EVOO and non-EVOO), using the univariate conformity index (CI) and the SIMCA model. As reference and training sets for the CI and SIMCA models, monovarietal and oil from different varieties were used. Spectra were recorded at 4000-1250 cm\({}^{-1}\) while significant spectral differences were observed at the region 9002-4552 cm\({}^{-1}\) when the CI model was applied and at the region 5350-5150 cm\({}^{-1}\), when the SIMCA model was used. Better discrimination results were obtained with the SIMCA model. Similarly, another research team tried to distinguish EVOO from lower-quality oils using the Handheld NIRS Analysis. A PCA analysis was first performed to select the most adequate variables, and the spectral region chosen by the authors based on characteristic spectral differences between the studied samples was 1570-1350 cm\({}^{-1}\). Various classification models were built, including one-class k-nearest neighbors (OCKNN), one-class support vector machine (OCSVM) and SIMCA. The sensitivity, accuracy and specificity of the SIMCA model was 100%, followed by the OCkNN model (33). [PERSON] et al. (2002)(24) studied the authenticity of EVOO using the Attenuated Total Reflection (ATR) technique. Authors used DA, and specifically, the 3100-2800 cm\({}^{-1}\) and 1800-900 cm\({}^{-1}\) spectral regions, and 100% discrimination between the samples of EVOO adulterated or pure was achieved at 20 mL/L of the adulterant. Quantification of sunflower was performed by the PLS analysis. Similar results were also obtained by [PERSON] et al. (2019) [PERSON] et al. (2019) by also using the ATR technique and PLS analysis. The spectral region studied was between 3200-650 cm\({}^{-1}\), and a unique prediction model was constructed, able to detect adulteration of EVOO with refined soybean oil, refined sunflower oil, refined corn oil and refined canola oil, independent of the adulterant and the degree of adulteration. _Spectroscopic Techniques Used to Evaluate the Authenticity of EVOO and VOO According to Their Variety and Geographical and Bolanical Origin: Focus on Infrared and Raman Spectroscopy_  
Raman spectroscopy was used for the geographical discrimination of EVOO from Italy on a minor number of samples (38 samples). In this study, isotope ratio mass spectrometry (IRMS) and resonant Raman spectroscopy (RRS) were used and the development of the chemometric model was based on LDA. Correct classification rate was 82% [PERSON] et al. (2019). Moreover, [PERSON] et al. (2015) [PERSON] et al. (2015) classified 49 samples of EVOO from different areas of Morocco using MIR spectroscopy in combination with PCA and PLS-DA analysis. Similarly, [PERSON] et al. 2011 [PERSON] et al. 2011 [PERSON] et al. (2011) classified Morocco VOOs with MIR spectroscopic analysis. First, using cluster analysis (CA) in combination with the average paired distance algorithm, samples were divided into four clusters according to their geographical origin; classification with CA was further certified with PCA analysis. Additionally, authors classified EVOO samples according to their harvest time, but from the same geographic area using PLS-DA. For the optimal selection of the wavelengths to include in the classification model, Martens' Uncertainty test was performed, which led to a better R\({}^{2}\) and RMSEP value, with respect to the analysis performed prior wavelength selection. [PERSON] et al. (2003), [PERSON] et al. (2003) used the NIR technique to distinguish between cultivars of EVOO produced in nearby regions. They performed a PLS, factorial discriminant analysis (FDA) and k-nearest neighbor analysis (k-NN).