## 3 Results
### Correlations Between HS-GC-IMS, FGC, and SPME/GC-MS Data  
To understand the relationships between the models, a PCA was developed considering simultaneously the 3 data sets previously discussed. The entire matrix (autoscaled data) was composed of 92 variables (41 + 24 + 27). The loading plot shown in Figure 10  
Figure 8: Score plot resulting from PCA developed from FGC data. The sample classified as VOO after one year of storage is highlighted by a red arrow.  
Figure 9: Loading plot obtained from the PCA using FGC data. Two variables highlighted in red are important to the clustering of stored samples, while the three in green are relevant to fresh samples.  
highlights the possible correlations between variables (VOCs obtained by using the three different analytical techniques). Furthermore, following the approach proposed by [PERSON] et al., 2016, a correlation matrix was seen between SPME-GC-MS and FGC [33]. Correlation coefficients greater than 0.7 were deemed acceptable, considering the elution order of VOCs in both analytical techniques (FGC and SPME-GC-MS; Table 3). The main aim of this elaboration was not to analyze FGC signals in depth, as the classification was untargeted, nor to consider using a data fusion approach in quality control. Instead, it was to investigate the possible differences or similarities in the variables that were most relevant to classify the oils during storage. The results of the correlation matrix are detailed in Table 3. Notably, peak number 21 exhibits a correlation with (\(Z\))-3-hexenyl acetate, and a VOC of relevance to the discrimination of fresh samples by SPME-GC-MS, as illustrated in Figure 6. The correlation between peak 4 and (\(\Sigma\))-3-ethyl-1,5-octadiene, identified by SPME-GC-MS, highlighted the importance of this volatile compound, generally known as a C\({}_{10}\) hydrocarbon derived from 1,3-penten radicals by a secondary LOX pathway. Although it does not appear to be a primary factor in the sample clustering based on SPME-GC-MS, as demonstrated in Figure 7, the comparable distribution of this compound in the loading plots obtained with FGC and SPME-GC-MS suggests that it plays a role in discriminating samples according to storage time. Accordingly, their oxidative degradation, rather than \"freshness\", would be related to the early steps of the LOX pathway. With this view, the increase in this compound, (\(\Sigma\))-3-ethyl-1,5-octadiene, observed over time could plausibly be the result of the autoxidation of linolenic acid in line with the findings reported by [PERSON] et al., who suggested that this compound is a potential marker of the degradation of quality during storage at room temperature [51].  
Figure 10: PCA of the 3 data sets (SPME-GC–MS, HS-GC–IMS, and FGC).  
To provide a comprehensive view, Figure 10 presents an overall loading plot considering all samples, analytical methods, and variables. This visual representation is intended to illustrate possible relationships between the variables analyzed by targeted and untargeted approaches. The proximity of variables obtained using different techniques suggests a positive correlation between them. As an example, (\(Z\))-3-hexenyl acetate, a compound identified by SPME-GC-MS and HS-GC-IMS, is located close to peak 21, a variable detected by FGC. Similarly, methanol, identified by SPME-GC-MS, is positioned close to peak 2. However, it was not possible to confirm this using HS-GC-IMS because methanol was not included among the 15 volatile markers analyzed for VOO classification in this study. The correlation matrix reported in Table 3 highlights the relationships among the data obtained by SPME-GC-MS and FGC; in particular, (\(Z\))-3-hexenyl acetate in SPME-GC-MS correlates with an FGC retention time, an important marker for clustering fresh oils.