*3.2. Spectral Analysis of Olive Oil Samples*

The characteristic FT-IR absorption spectra of different grades of olive oil samples and their corresponding band assignments for specific functional groups are displayed in Figure 1a. Visual inspection of the spectra showed close resemblance in their spectral profiles throughout the mid-IR region (4000–700 cm−1 ) (Figure 1a), similar to those previously reported by Rohman and others (2017) [54]. Key absorbance signals included the band at 3010 cm−1 associated with =C–H stretching of cis olefins, the 2900–2800 cm−1 range related to–C–H symmetrical and asymmetrical stretching vibrations (CH2 and CH3), the band centered at 1746 cm−1 associated to the stretching vibrations of the ester carbonyl (–C=O) functional group of triglycerides, and the band at 1465 cm−1 associated with C–H bending (scissoring) vibration of the CH2 group. The band at 1377 cm−1 corresponds to the C–H bending (symmetrical) vibration of the CH3 group, and the shoulder band centered at 1417 cm−1 due to the rocking vibrations of the C-H bonds of cis-disubstituted olefins. Finally, the fingerprint region from 1200 to 1000 cm−1 represented the unique stretching and bending vibrations of –C–O and –CH2– vibrational modes. Overall, important spectral regions for revealing possible EVOO adulteration included the band intensities at 3010–2800 cm−1 related to the triglyceride fatty acid composition and level of unsaturation of the oils, and the relative proportion between the triglyceride ester-linkage (COOR) band at 1742 cm−1 and the C=O absorption of FFAs at 1711 cm−1 . An increase in the band intensity at 1711 cm−1 correlates with the increase in FFA content of oil [55].

**Figure 1.** (**a**) FT-IR spectrum and band assignments of different quality olive oils at frequency of 4000–700 cm−1 collected using a portable 5-reflections ZnSe crystal ATR system equipped with a temperature-controlled accessory. (**b**) Raman spectrum of different quality olive oils at frequencies of 200–1850 cm−1 collected using a compact benchtop Raman system working with 1064 nm excitation laser. EVOO: Extra virgin olive oil, VOO/OO: Blend of virgin olive oil and olive oil, EVOO + SO: Extra virgin olive oil + Sunflower oil. \*a.u.: Arbitrary units.

− − − − − − The Raman spectra for selected olive oil samples and their band assignments for specific functional groups are given in Figure 1b. The band at 1080 cm−1 was associated with C-C stretching vibration (-CH2-)*n*, while the band at 1263 cm−1 was associated with =C-H in-plane deformation of a conjugated cis double bond (cis-R-HC=CH-R) and related with monounsaturated fatty acids. The band at 1300 cm−1 was related to -C-H twisting motion (-CH2), and the band at 1439 cm−1 was associated with -C-H bending (-CH2) modes. The band at 1654 cm−1 was related to C=C stretching (cis-R-HC=CH-R) from polyunsaturated fatty acids. The band at 1745 cm−1 was associated with C=O stretching vibration (RC=OOR) [9,56]. Different pure olive oils (EVOO, VOO, OO) did not show major differences throughout the measured Raman spectrum (Figure 1b), but olive oil adulterated with other vegetable oils displayed marked differences (higher bands) in the band intensities at 1263 and 1654 cm−1 . As mentioned earlier, those bands correspond to monounsaturated and polyunsaturated fatty acids, and an increase in their band intensities has been related to an increasing weight percentage of unsaturated fatty acids in olive oils [9,56].

#### *3.3. Pattern Recognition Modeling Using FT-IR and Raman Spectroscopy* −

The FT-IR and Raman spectral data were analyzed using soft independent modeling of class analogy (SIMCA) for the authentication of EVOO and detection of adulteration, either by blending with other vegetable oils or replacing of EVOO with lower olive oil grades, such as refined, pomace, or lampante olive oils. Single-class and multi-class pattern recognition strategies were assessed either by using a binary (authentic EVOO vs. VOO/OO blends and EVOO adulterated with vegetable oils) or multiple (authentic EVOO, VOO/OO blends and EVOO adulterated with vegetable oils) class approach based on the information provided by the Aydin Commodity Exchange Laboratories and California Olive Oil Council, along with our reference tests' results.

A multi-class approach was implemented for the FT-IR spectral data that comprised three different groups including EVOO, VOO/OO blends, and adulterated olive oil with vegetable oils. The class projection plot (Figure 2a) showed compact clusters for the EVOO and VOO/OO blends, indicating similar chemical composition among samples in their class, while the marked compositional differences in EVOO adulterated with different vegetable oils were reflected by the large spread of samples in the class projection plot. A SIMCA parameter that correlated to the chemical differences between classes was the interclass distances (ICD) and gave values ranging from 2.6 (EVOO & VOO/OO blends) to 6.1 (VOO/OO blends & EVOO with other vegetable oils) (Table 2). In the SIMCA models, two different classes with an ICD >3 are considered significantly different from each other [36]. Overall, all classes were largely independent of one another, requiring three to five PCs to explain 99% of the variance within groups and the cross-validation showed zero misclassifications, which indicates that the model should be robust and minimizes over-fitting. The SIMCA discriminating power plot (Figure 2c) showed that the clustering of different olive oil grades and adulteration were explained by the bands centered at 2920 and 2850 cm−1 , corresponding to CH2 asymmetric and symmetric stretching vibrations, and 1742, 1711, and 1098 cm−1 ,which correspond to the stretching vibrations of the carbonyl bonds (–C=O) in acylglycerides, and the 1670 cm−1 band, related to the olefinic trans C=C stretching vibrations. − − −

**Figure 2.** *Cont.*

**Figure 2.** (**a**) Soft independent modeling of class analogy (SIMCA) 3D projection plots of spectral data for olive oil samples collected by (**a**) portable FT-IR and (**b**) compact benchtop Raman spectrometers. EVOO: Extra virgin olive oil, VOO/OO: Blend of virgin olive oil and olive oil. (**c**) SIMCA discriminating plot based on the mid-infrared and Raman spectra of olive oils using an FT-IR and a Raman spectrometer, showing bands and regions responsible for class separation.

a EVOO: Extra virgin olive oil, b VOO/OO: Blend of virgin olive oil and olive oil, c Adulterated EVOO with other vegetable oils (corn, sunflower, soybean, and canola oil).

The predictive performance of the multi-class calibration model was determined by using an independent validation set that included fifteen EVOOs, five VOO/OO blends, and nine EVOOs adulterated with other vegetable oils. By including the information of additional classes (i.e., VOO/OO blends and EVOO with other vegetable oils), the sensitivity and specificity of the SIMCA models were 100% for all the oil classes (Table 3). Since authentication studies are often approached as a one-class classification analysis, the adulterants are usually unknown [57]. A one-class SIMCA model was developed for EVOO based on the infrared spectra of genuine samples, and any adulterated samples were classified as outliers when tested against the PCA model boundaries. The performance of the calibration models was evaluated by using an independent validation set that consisted of 15 authentic EVOO and 74 non-authentic (VOO/OO and EVOO with other vegetable oils) samples. All EVOO samples were correctly predicted (TP = 15 and FN = 0) as belonging to its target class, resulting in 100% sensitivity, indicating that the one-class model was capable of accurately identifying authentic EVOO samples. On the other hand, eight of the non-authentic samples were predicted as EVOO (FP = 8, TN = 66), resulting in 89% specificity (Table 3), revealing that the model had adequate ability to detect adulterated samples. The one-class model correctly predicted all EVOO mixed with cheaper vegetable oils, while eight out of twenty-seven VOO/OO were predicted as belonging to the EVOO class.

**Table 3.** Sensitivity and specificity values of SIMCA multi- and single-class models obtained from FT-IR and Raman spectroscopy.

a EVOO: Extra virgin olive oil, b VOO/OO: Blend of virgin olive oil and olive oil, c Adulterated EVOO with other vegetable oils (corn, sunflower, soybean, and canola oil).

A similar approach was taken for the Raman spectral data collected from the oils to detect EVOO adulteration. The class projection plot is given in Figure 2b. The multi-class SIMCA model gave ICDs ranging from 0.9 to 7.0, with the largest dissimilarity of spectral features obtained between authentic EVOO and its mixtures with other vegetable oils (ICD = 7.0), while the ICD differentiating EVOO from VOO and its blends with refined olive oils was 0.9 (Table 4). Wold and Sjöström (1977) described that distances between class models larger than one indicate real differences, and if two models are not independent, the interclass distance is close to zero [58]. The classes required three to five PCs to explain 98% of the variance within groups, and the cross-validation showed zero misclassifications. The SIMCA discriminating power plot (Figure 2c) was dominated by the bands centered at 1652 and 1306 cm−1 , associated with the alkene νC=C stretch and in-phase methylene twisting vibrations, respectively. The minor bands at 920 and 856 cm−1 were attributed with bending vibrations of trans (C=C) and stretching vibrations of methylene chain skeleton, respectively [8]. An independent validation set was used to evaluate the predictive performance of the SIMCA models. Sensitivity evaluated the capability of our classification model to identify EVOO, while specificity determined the ability of our model to discriminate the adulterated or mislabeled samples. The sensitivity and specificity values for the single and multi-class models for Raman spectroscopy are given in Table 3. The multi-class model gave 100% sensitivity and specificity, which means that models generated by Raman spectra could effectively detect authentic EVOO samples from adulterated oils with excellent accuracy. Although the ICD separating the pure EVOO from VOO and its blends with refined olive oils was 0.9, the model gave perfect predictions. SIMCA single class models developed from Raman models correctly predicted all authentic EVOO (TP = 15 and FN = 0; 100% sensitivity). However, out of the 74 validation samples that were either mislabeled (lower olive oil grades) or adulterated with other vegetable oils, the one-class model failed to identify 25 samples that were predicted as pure EVOO (FP = 25, TN = 49; sensitivity = 66%). A total of 12 VOO/OO blends and 13 adulterated samples were classified as EVOO.

**Table 4.** Interclass distances between three classes of olive oils based on the SIMCA class projections for the Raman spectra collected in the 250–1850 cm−1 region.

a EVOO: Extra virgin olive oil, b VOO/OO: Blend of virgin olive oil and olive oil, c Adulterated EVOO with other vegetable oils (corn, sunflower, soybean, and canola oil).

Similar to our findings, Li et al. (2018), Philippidis et al. (2017), and Zhang et al. (2011) were also be able to differentiate olive oils from vegetable oils including waste cooking oil, sunflower, rapeseed, soybean, corn, and canola oil by using Raman spectroscopy [8,9,56]. However, we report for the first time the discrimination of EVOO from their different grades (VOO and OO). Our data showed the challenges in detecting EVOO from OO, as very few unique compounds, monochloropropanediol esters, and glycidyl esters formed in the refining process can be used as markers for authentication [59]. By including the additional features from the class assigned to VOO and OO samples to the supervised model allowed to improve the discriminability of the classifiers providing the best accuracy for authentication of EVOO without false positives. Furthermore, EVOO adulterated with pomace olive oil showed marked FT-IR and Raman spectral differences allowing straightforward detection by pattern recognition analysis.