## 3 Results and Discussion  
### Electronic Nose Analysis of Individual Bottles  
Figure 2 shows the temperature values recorded over the aging period using a data logger. The data suggest that the underwater samples had more stable temperatures, whereas greater variability was observed for the cellar-aged samples. The most notable temperature variations between the two aging methods occurred over a five-month period: from August to September 2023, with an average temperature difference (\(\Delta\)T\({}_{\mu}\)) of about 5.2 \({}^{\circ}\)C, and from May to July 2024, with an increased \(\Delta\)T\({}_{\mu}\) of about 7.4 \({}^{\circ}\)C.  
The E-nose responses, expressed as the conductance ratio G/G\({}_{0}\) (where G represents the sensor conductivity when exposed to the sample gas and G\({}_{0}\) represents the conductivity when exposed to the zero gas), were analysed for all 34 bottles (17 underwater-aged, S, and 17 collar-aged, C). To ensure consistent data, the time range of 75-80 s, during which the ten MOS sensors provided a stable signal response, was selected to construct the data pattern for the liquor samples. In recent years, numerous attempts have been made to apply electronic noses for wine classification [23; 24; 25; 26]. To evaluate the ability of the E-nose to differentiate between the two aging treatments, the constructed data pattern was subjected to statistical analysis using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The PCA biplot of the E-nose data pattern is shown in Figure 3.  
The results indicate a partial separation between S and C samples, with the first two principal components explaining 76% and 9% of the total variance, respectively. However, a partial overlap is observed between the two groups along the PC1 axis, suggesting that some samples share similar characteristics in relation to the variables that contribute most to this axis. The PC2 axis also contributes to the separation, although to a lesser extent. Specifically, a higher response was observed from the sensors W1C, W3C, and W5C for the S samples. These sensors are particularly sensitive to aromatic and aroma-aliphatic compounds [27], suggesting an increased presence of these compounds during underwater aging. The combination of PC1 and PC2 provides an appreciable separation between the two groups, indicating the potential of the electronic nose to discriminate between different aging methods. Figure 3: PCA biplot of the E-nose data pattern illustrating the separation of underwater-aged (S) and cellar-aged (C) samples, with the contribution of the responses of the10 E-nose MOS sensors (black circle). Figure 2: Temperature evolution measured with a probe inserted in the bottle cap during the 12 months of underwater aging (S) and cell aging (C). \(\Delta\)T\({}_{\mu}\) represents the average temperature variation during the months with the highest temperature fluctuation (August-September 2023; May–July 2024). Based on the PCA results, we examined the factor loadings of the selected principal components to evaluate the contribution of each sensor to the explained variance [22]. In particular, W6S was excluded from further analysis due to its loadings being distributed across multiple factors and its low contribution to the discrimination along the first two principal components (F1: 0.53, F2: \(-0.51\); \(p>0.05\)). Additionally, to reduce multicollinearity, W5C was excluded, as it showed high similarity to W1C in terms of component loadings, which was also evident from their overlap in the PCA plot (Figure 3), indicating redundancy [28]. LDA was then performed on the modified dataset to assess the classification accuracy of the E-nose in distinguishing between the two aging methods. The analysis assumed equal within-class covariance matrices and prior probabilities, with a significance level of 5%.