## 3 Results and Discussion
### S3 Analysis Results  
Once data were acquired, sensors responses were checked first. Since the first measures of each session were very different from the others, they were discarded. Consequently, there is a different number of replicas for each sample. Most likely, experimental conditions of first acquisitions were not the same as the following measures in terms of the temperature of the auto-sampler oven that the vials  
Figure 2: Comparison of acetic acid, butanoic acid, hexanoic acid, octanoic acid and n-decanoic acid amount between 12-months and 24-months samples. Results are presented in terms of mean \(\pm\) standard deviation of the mean. were put in, as explained in Section 2.3. S3 Analysis. In Table 2, a detailed description of the number of samples that were considered for the following analysis is shown. The choice to extract \(\Delta\)R/R\({}_{0}\) as a feature was made after viewing the sensors responses. In Figure 3, the resistance value, as a function of time during the injection phase, is presented for four sensors that represent the four types of MOX in the S3. They are CuO, SnO\({}_{2}\)Au-RGTO, SnO\({}_{2}\)Au+Au-Nanowire and TGS2602. Since the starting point is equal for all the measures, the variation of normalized resistance exhibit that all the sensors are capable of distinguishing samples with different concentrations of rind (samples colors: red for 100% rind, green for 0%, blue for 18%, cyan for 26% and black for 45%). In addition, they show also the ability to recognize the two ripening degrees, characterized with a solid line for 24-months samples and with a dotted line for the others. Finally, responses to the working processes are highlighted using a thicker line for WR samples as compared to SR ones. TSG2602 and SnO\({}_{2}\)Au+Au nanowires seem to be the best MOX to identify ripening and rind working process at fixed concentration, since minimum resistance varies mostly for samples with the same rind percentage. Conversely, CuO and SnO\({}_{2}\)Au (RGTO) responses are more useful to recognize \"pure\" samples (0% and 100% of rind) from mixtures, since in the first case \(\Delta\)R is bigger. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**SeasoningPercentage** & **0\%** & \multicolumn{2}{c}{**18\%**} & \multicolumn{2}{c}{**26\%**} & \multicolumn{2}{c}{**45\%**} & \multicolumn{2}{c}{**100\%**} \\ \hline  & & **WR** & **SR** & **WR** & **SR** & **WR** & **SR** & **WR** & **SR** \\ \hline _12 months_ & - & 11 & 12 & 13 & 11 & 12 & 14 & - & 14 \\ _24 months_ & 12 & 14 & 14 & 13 & 13 & 11 & 13 & 13 & - \\ \hline \end{tabular}
\end{table} 
Table 2: Considered samples divided for ripening stage, rind percentage and rind working processes (WR = washed-rind, SR = scraped-rind). Figure 3: _Cont._  
Figure 3: CuO, SnO\({}_{2}\)Au-RGTO, SnO\({}_{2}\)Au+Au-Nanowire and TGS2602 responses as functions of time. Samples colors: Red for 100% rind, green for 0%, blue for 18%, cyan for 26% and black for 45%. Solid line for 24-months samples and dotted line for 12-months. Finally, thicker lines represent WR and the others SR. In Figure 4, boxplots of TGS2602 response that include \(\Delta\)R/R\({}_{0}\) for each sample are shown. This sensor represents the general trend that can be observed in all the sensors. Obviously, since different sensing materials are used, there are differences in the highlighted groups that overlap. On the left part of the figure, there are 24-months seasoned samples, in the upper part, grated cheese with SR, while in the lower, PR with WR. In the right part, there are 12-months ripened samples, and they follow the same trend. The first boxplot is relative to samples of 0% rind; its \(\Delta\)R/R\({}_{0}\) is different with respect to all the other groups, but it is more similar to WR grated PR, both at seasoning stage. This result reflects the fact that they are characterized by a greater amount of humidity. After checking the general sensors performances, JB-test was applied to the dataset.