## 3 Results and Discussion
### Electronic Nose Measurements  
The chromatograms of representative samples of each group are shown in Figure 6. Throughout this manuscript, \"1-A\" as an identifier after the RI refers to column MXT-5, and \"2-A\" for column MXT-1701. Acetaldehyde (433-1-A, 485-2-A), ethanol (459-1-A, 554-2-A), propanal (485-1-A, 59-2-A), hexanal (801-1-A, 893-2-A), and 1-hexanol (859-1-A, 977-2-A), all indicative of a fruity odour, were the most dominant volatiles based on the RIs of the chromatogram peaks. Fatty acids, usually linoleic acid and linoleic acid, are converted by oxidative degradation with lipoxygenase or hydroperoxide lyase to volatile aldehydes such as hexanal [61]. The aldehydes are then converted to alcohol by the action of alcohol dehydrogenases [62].  
Figure 5: PCA score and loadings plots of the electronic tongue measurements of the tested smoothie samples. (**a**) PCA score plot (PC1-PC2) coloured by sample types, (**b**) PCA score plots (PC1-PC3) coloured by the applied pressure level, storage, and treatment types, respectively, and (**c**) PCA loadings plot (\(n=36\)).  
Principal component analysis of the multivariate data from the electronic nose measurement showed a dominant odour difference between the fresh and stored samples (Figure 7). The stored samples had a greater odour variance. The control group differed from the treated samples in both the fresh and stored samples. The overlap of the two treated groups shows the similarity in odour, but it can be noted that the 60 \({}^{\circ}\)C-250 MPa groups were the most different from the control groups, either fresh or stored. The discriminant factor analysis (Figure 8) showed the same pattern, but due to the supervised approach, the group separations were larger. The automatic sensor selection function of the  
Figure 6: Chromatograms recorded on the two columns of the Heracles Neo 300 electronic nose for one representative sample of each group. (**a**) Fresh samples, column 1-A, (**b**) samples 14 th day, column 1-A, (**c**) fresh samples, column 2-A, (**d**) samples 14 th day, column 2-A.  
Alpha Soft program was used, and the bar graph of the selected virtual sensors is shown in Figure 9. The bi-plot of Figure 10 shows the orientation of the group when the discriminant factor analysis was run with the selected sensors, and the loading vectors of the sensor variables are projected onto the plot. The results indicate loss of some of the volatiles during storage; the largest decrease during storage was observed for RI 1087.37-2-A associated with benzaldehyde, while ethanol (554.19-2-A) and ethylhexanoate (1064.98-2-A) also showed a general decrease. Octanal (1099.29-2-A) showed an increase in all stored samples. Propanal (485.43-1-A) and acetaldehyde (485.82-2-A) increased during the storage in the 60 \({}^{\circ}\)C-250 MPa and control groups, respectively.  
Figure 8: Result of the discriminant factor analysis performed with all virtual sensors of the electronic nose.  
Figure 7: Score plot of the principal component analysis performed with all virtual sensors of the electronic nose.  
Figure 10: Result of the discriminant factor analysis performed with the selected virtual sensors indicating the impact of each sensor on the classification performance (the asterisks indicate the size of the vectors showing the effect of each sensor).  
Figure 9: The average intensity values and standard deviations (dashed area) of the most distinctive virtual sensors for the six groups of samples.  
The advantage of using virtual sensors derived from chromatograms, as applied in this study, lies in the possibility of identifying the volatiles associated with specific sensors, i.e., the retention indices of the chromatogram peaks. This allows the user to identify the odourants of the sample from international databases or their own recorded data, which is rarely possible in the case of other sensor-based electronic nose approaches.