## 3 Results and Discussion
### S3 Analysis Results
Only 4 of the eight parameters followed a normal distribution (\(p<0.05\)); they correspond to the features extracted by the two tin oxide nanowires and RGTO sensors. This was the main reason for choosing PLS. In Figure 5, PLS score plot was made, considering the first two latent variables (LV) for a total explained variance equal to 99.95% (99.87% for LV1 and 0.08% for LV2). The plot measures are divided by seasoning degree. It can be observed that the 24-months class is in the central part of the graph, while the other one is divided in the left and right part. For this reason, classification techniques were used in a hierarchical way. In addition, another motive for this choice was to simplify classification models, since this is a 15-class problem. Hence, in the first step, classifiers were used to distinguish the seasoning degree; in a second step, for each ripening state the different working processes were discriminated; finally, ring percentage was taken into account. In Figure 6, a scheme of the steps is shown. Regarding ANNs structures, three different ones were considered, one for each step. In the first case, a two-layers architecture with 3 neurons in the input layer and 1 in the output layer was considered. For the second stage, the same number of layers was used, but two neurons were put in the first one. Finally, the third ANN had the same structure as the previous ones, but with 6 neurons in the input layer. For all the neurons, hyperbolic tangent sigmoid transfer function was chosen. Figure 4: Boxplots of TGS2602 feature \(\Delta\)R/R\({}_{0}\). Four groups are highlighted: In blue, 24-months SR; in green, 24-months WR; in black, 12-months SR; and in orange, 12-months WR. In Table 3, overall classification rate of the two classifiers is put side by side. In general, ANN classification rates are better than those of PLS-DA. Indeed, ANN is able to recognize correctly all the samples based on seasoning and find working processes. PLS-DA performances are lower, although it can reach good classification rates. The distinction between mid percentage shows that both classifiers can classify samples with SR better than those with WR. A possible explanation for this result could be the different amount of humidity: WR samples have a higher content of humidity because of water treatment and this could cause the occupation of the adsorption sites by water molecules instead of the ones that characterize the volatile fingerprint of the samples. Figure 5: PLS score plot for all the measures divided by seasoning degree: in red circle, 12-months; in green square, 24-months. Total explained variance equal to 99.95% in first two LV. Figure 6: Step-by-step scheme for classification analysis. To the author's knowledge, only few researches have been carried out regarding find composition of grated cheeses. The preparation of the samples in some works was carried out through the grating process, although the aim was to classify the different varieties of cheeses, like Swiss [55] or Emmental cheese [56]. For the latter, it has been tried unsuccessfully to find the \"rind-taste\" off-flavor [57]; in this case, the lack of positive results could be due to non-volatile compounds that change only the taste but not the aroma. As regards PR, cheese aroma authenticity and rind percentage recognition have been achieved with an electronic nose equipped with SnO\({}_{2}\) and ZnO sensors made at SENSOR Laboratory of University of Brescia [58]. In this case, the tool was also able to distinguish samples with little differences in terms of rind percentage, such as 18% and 19%. However, unlike this study, only one type of ripening was considered (12-months) and the different working processes were not taken into consideration. Finally, the comparison with this study allows for the assessment of the utility of S3 for fraud detection, since the results point in the same direction.