## 3 Results and Discussion
### Discrimination of Olive Aroma Categories Using an Electronic Device  
The radial plot of the sensor's responses to the aromas of Californian-style black olives with natural flavoured hydrocolloids is shown in Figure 3. As can be seen, each table olive category had a particular fingerprint that corresponded to each of the responses of the sensor to the aromas of the samples. In the standard samples, most of the sensor responses were stronger than the others because the values were near to 1, while for the extra category, the sensor responded with less intensity to the aromas of the samples, except for the BME680 gas measurement. Thus, we can deduce that the profiles of the aromas of each sample after analysis with the E-nose were completely different and could be distinguished by this electronic device.  
Then, the 11 signals recorded by the sensors of the E-nose were reduced to two different components (Figure 4). The principal component analysis showed that the responses of the E-nose to the different samples from each category could be dist  
Figure 3: Radial plot of the sensor ’s responses to Californian-style black olives stuffed with hydrocolloids. BME680: gas measurement (\(\Omega\)); SGP30: eCO\({}_{2}\) (ppm), organic volatile compounds (ppb), H\({}_{2}\) and ethanol; CCS811: eCO\({}_{2}\) (ppm), organic volatile compounds (ppb) and sensor resistance (\(\Omega\)); iAQ-Core: eCO\({}_{2}\) (ppm), organic volatile compounds (ppb) and sensor resistance (\(\Omega\)).  
Figure 2: Linear regression for burn defects and \(\Sigma\) phenols in Californian-style black olive stuffed with hydrocolloids.  
PC1 explained 82.1% of the variance while PC2 explained 15.1%. Thus, the odour profiles of the samples detected by the electronic device were enough to discriminate between samples of different categories. Data from the extra category occupy the right-hand side of the plot and correspond to positive PC1 scores, while the select and standard categories showed positive PC2 scores and are in the upper two quadrants of the figure. Thus, it can be seen that the different samples from the different categories could be distinguished.  
Therefore, when good discrimination was observed in the previous PCA, another classification model, PLS-DA, was used to establish a predictive classification to determine the different classification categories for the table olives studied. Table 4 shows the results of the confusion matrix, which was able to correctly classify almost all the samples. Only 9.0% of the samples in the select category were classified in a different category (extra). This may have been due to the standard deviation itself during the measurement with the electronic equipment. However, the classification model was satisfactory since it was able to predict the category of Californian-style black olives with 90.9% accuracy.  
Researchers [2; 22] have classified Californian-style black olives subjected to five different thermal sterilisation treatments based on the aromatic profiles of the samples. These researchers reported a strong burn odour from the samples subjected to more aggressive thermal sterilisation treatments. In our case, such severe burn smells were not detected because, in our study, the olives were flavoured with a natural hydrocolloid filler that was able to mask the intense burn smell from the most intense thermal sterilisation treatments.