## 3 Results and Discussion
### Sensory Analysis
Coratina olive oil obtained by adding three different concentrations of oxygen (0.2 L/min for COR2, 0.4 L/min for COR3, and 0.8 L/min for COR4) during crushing phase in comparison with control test (no oxygen added for COR1)  
On the other hand, the intensity of \"bitter\" and \"pungent\" sensory notes tends to decrease as the concentration of oxygen applied during crushing increases, in parallel with the reduction of the phenolic compounds that are responsible for these attributes [85; 86]. Figure 4: Representation of objects and variables (bi-plot) on the plane of the two principal components (PCA) relating to the results obtained through sensory evaluations of all virgin olive oils of cv. Coratina and the attributes evaluated. The variables and objects are marked in red and blue, respectively. The four quadrants are coded as Q1–Q4  
Figure 3: Representation of objects and variables (bi-plot) on the plane of the two principal components (PCA) relating to the results obtained through sensory evaluations of all virgin olive oils of cv. Ogliarola (objects) and attributes (variables) evaluated at the panel test. The variables and objects are marked in red and blue, respectively. The four quadrants are coded as Q1–Q4  
The distribution of the samples in the four quadrants allows us to describe them and highlight the possible dependencies as a function of the variables present in the same quadrants. The PCA model shown in Figure 3 explains 90.0% of the total variance with two principal components in the data (the first and second components explaining 65.0% and 25.0%, respectively). From the bi-plot relating these components, a clear separation of samples emerges along the second component (from negative to positive score values) in relation to the oxygenation treatment/non-treatment applied, with the control sample characterized by negative score values (quadrant 3). The samples treated with increasing oxygen concentration were characterized by positive score values relative to principal component 2, and the sample treated with an oxygen addition of 0.2 L/min (OG2 in Figure 3) was plotted in a different quadrant (quadrant 2) compared to the other treated samples (quadrant 1). On the other hand, from the first component of the bi-plot (reading from negative to positive score values), it was also observed that oils were extracted differently according to the concentration of oxygen used. Then, samples OG1 and OG2 were characterized by negative values of principal component 1, while samples OG3 and OG4 were characterized by positive score values. Additionally, from the bi-plot of this PCA model, it can be inferred that the location of the OG2 sample in the plot can be explained by the variables \"green apple\", \"arithchoke\", \"herbaceous\", and \"hay\", while the control sample was related to the attributes \"pungent\" and \"floreal.\" This relationship was in accordance with the higher values of these sensory attributes for these two samples, as observed in Figure 1.  
Similarly, another PCA model was built with all the variables obtained from the sensory analysis of Coratina virgin olive oils, shown in Figure 4. In this case, the two principal components explained 96.1% of the total variance of the data (67.5% for the first component and 28.6% for the second). The plot also highlighted how the principal discrimination of the objects is mainly due to the oxygenation treatment carried out, as shown by the reading along the second component (from negative to positive values). According to the plot, the COR2 sample was related to the attributes \"arithcoke\" and \"herbaceous\", which were characterized by higher intensity as observed in Figure 2. The plot also demonstrated that the oxygen treatment in COR4 (0.8 L/min of added oxygen) produced a different sensory profile, given that this sample was the only one with positive score values for principal component 2.