## 3 Results and Discussion
### Multivariate Characterization of Samples Using PCA and PLS-DA  
Given that the olives were affected by mild frost instead of severe frost and some univariate statistical differences were observed, the next step was to evaluate if the temperature of \(-3\)\({}^{\circ}\)C during 12 h caused enough differences in the composition of phenols and volatile compounds, and other quality parameters, to be observable in a multivariate statistical analysis. Thus, for this purpose, the data were analyzed by principal component analysis (PCA). These consisted of 48 compounds and quality indexes (variables). As a first approach, the most informative variables classifying oils from fresh and frozen olives were selected by considering the model contribution and residuals of the variables (R\({}^{2}\)VX). The residuals inform the extent to which each variable is modelled. Thus, R\({}^{2}\)VX represents the fraction of X variation which is modeled in the component [60], ranging from 0 to 1, and a R\({}^{2}\)VX < 0.5 was considered as selection criterion to select those compounds (variables) that are well explained. Figure 3 shows the R\({}^{2}\)VX for each compound. The initial reproducibility/prediction (Q\({}^{2}\)VX) was also shown. This parameter indicates the fraction of the variation of a variable that can be predicted by a component as estimated by cross-validation [61]; however, in this case, only R\({}^{2}\)VX accounted for selecting variables, since this was a first descriptive approximation, and prediction performance was studied later. Thus, considering the explained variation, the following variables were removed for having R\({}^{2}\)VX < 0.5 (Figure 3): methyl luteoline, aglycone of oleoeuropein dialdehidic, aglycone of ligustroside dialdehidic, K232, and (_E_)-2-heptenal, K270. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mays**} & \multicolumn{4}{c}{**Mays**} & \multicolumn{4}{c}{**Mays**} & \multicolumn{4}{c}{**Mays**} & \multicolumn{4}{c}{**Mays**} & \multicolumn{4}{c}{**Mays**} \\ \hline
**Value** & \multicolumn{4}{c}{**Jame**} & \multicolumn{4}{c}{**Jame**} & \multicolumn{4}{c}{**Jay**} & \multicolumn{4}{c}{**Jay**} & \multicolumn{4}{c}{**Jay**} & \multicolumn{4}{c}{**Jay**} & \multicolumn{4}{c}{**[PERSON]**} & \multicolumn{4}{c}{**Jay**} \\ \cline{2-13}
**Compounds** & **F  
Once the 6 compounds mentioned above were omitted, the PCA was repeated. The result pointed out that 80.2% of the total variance was explained with 5 factors having an explained variance of 35.2% (F1), 19.1% (F2), 12.9% (F3), 7.03% (F4), and 5.9% (F5). Figure 4 shows the scores and loading plots for F1 and F2. Factor 2 showed a separation of the samples from fresh and frozen olives. The direct (or inverse) contribution of variables and observations could be associated by superimposing the hyperplanes of scores. Thus, the samples from the fresh olives (in green color) had a direct relationship with the variables in the upper quadrants, compared with those treated at \(-3\)\({}^{\circ}\)C (in blue color). The comparison of scores and loading plots shows the influence of the phenols (e.g., tyrosol, hydroxityrosol, dialdehyde form of decarboxymethyl ligestroside aglycon, dialdehyde form of decarboxymethyl oleuropein aglycon and aldehyde, and hydroxylic form of oleuropein aglycone) in the samples from fresh olives in the early season (May) and the contribution of peroxide value in the advanced ripening season. The influence of volatile compounds characterizing oils from fresh olives compared with frozen olives was also observed. Considering the differences in composition and quality characteristics between oils from fresh and frozen olives during ripening, PLS-DA was applied. Figure 5 shows the results of this analysis. The PLS-DA model retained 4 factors, explaining 94.4% (R\({}^{2}\)Y) of the variance (discrimination between the two groups) and a validity/reproducibility of 90.1% (Q\({}^{2}\)). Thus, the groups were clearly separated according to the temperature treatment of the olives.