# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## 2 Results and Discussion  
### Sensor Responses  
Once the data were acquired, sensor responses were checked for the measurements of each session. Moreover, the response curves for the given olive oil samples obtained from the individual sensor were studied to observe and detect any relevant features. This step allowed us to eliminate the sensors whose response did not yield useful information. In Figure 1, it is possible to see a typical response of a n-type MOX sensor; in this case, normalized resistance variation of a SnO2+Au nanowire is shown. Normalization of the signals has been performed computing the ratio between the first value of resistance recorded and all the values of the same measure. For this reason, all signals start from a value equal to 1. Sensors responses are grouped based on their belonging to one of the 4 samples analyzed in the first phase: olive oil, non-PDO EVOOs and Garda PDO EVOO. Furthermore, signals are shown in sequence along the time axis; since each measurement lasts 600 s, time ranges from 0 s to 2400 s.  
All the curves present a decreasing value of resistance during VOC analysis, while it reaches the baseline when filtered ambient air is fluxed inside the sensor chamber. It is evident from the graph that the three categories of analyzed olive oil differ in terms of the minimum value of resistance; this value is reached during the VOC exposition. The olive oil (in blue) is the one with the minimum variation, while non-PDO EVOOs in green and orange reached similar values, and all of them differ from the Garda PDO sample. Since the first goal was the discrimination between olive oil samples, non-PDO  
Figure 1: Typical resistance variation of a n-type sensor once exposed to oil volatile compounds. On the x-axis is time (s), on the y-axis is the normalized resistance. EVOOs and PDO EVOOs, the observed normalized resistance variation was considered as the first feature to be extracted from data. Then, the first derivative of the signal over time was considered. The first derivative can give information regarding the speed at which the resistance varies over time, which is an index of the speed of the reaction that take place over sensing layers. Indeed, since different oils are characterized by different numbers and amounts of VOCs, and sensors have different affinity toward them, it is plausible to think that the maximum speed of resistance change can be used as a marker for different oil typologies. In Figure 2, the first derivatives of SnO\({}_{2}\) RGTO sensor are shown. The sequence representation of the signals follows the same logic described for Figure 1. In this case, the maximum speed is the minimum (negative) value, since derivatives were calculated using finite differences, a method widely used to approximate numerical derivatives. It is clear that this RGTO sensor reaches the minimum value of resistance at different speeds, depending on the oil under analysis. The olive oil (blue) reaches the lowest negative value, while the Garda PDO EVOO reaches the highest value. In the middle, there are two non-PDO EVOO samples; unlike what was seen in Figure 1, in this case, there is a clear difference between them. Even though this is not important for the designated aim, it could be very useful in future analyses in cases where it is necessary for a device to be able to recognize different non-EVOOs. To evaluate which of the 14 features (7 sensors \(\times\) 2 types of features) were suitable for the task, Principal Component Analysis (PCA) was performed. In Figure 3, score (top) and loading (bottom) plots are shown; the total explained variance was equal to 88.55%. A clear distinction between the olive oil sample (red circles) and the EVOOs can be noticed. On the contrary, there is a partial overlap between the Garda PDO (blue cluster) and the other two EVOOs (in green). This result could mainly be due to different production processes that distinguish a regular olive oil from an extra virgin one, but also to the different cultivar composition of the analyzed samples.