# Innovative Sensor Approach to Follow _Campylobacter jejuni_ Development
## 3 Results and Discussion
### S3 Results  
First step of S3 data analysis consisted of checking which of the eight sensors were performing more. Sensor responses were normalized in order to highlight the variation of the resistance once sensing materials were exposed to VOCs. Five sensors showed the best performances: two RGTO (SnO\({}_{2}\)Au and SnO\({}_{2}\) heated at 300 \({}^{\circ}\)C), SnO\({}_{2}\)Au nanowire, copper oxide and TGS2611. Resistance variation over time for all measures is shown in Figure 1 for three kinds of sensors, i.e., RGTO, nanowire and commercial MOX.  
CuO sensing material exhibits an increase in resistance with respect to the R\({}_{0}\) value, while TGS2611 and SnO\({}_{2}\) RGTO have an opposite behavior due to their n-type semiconductor characteristic. However, all of them are characterized by the decreasing of \(\Delta\)R with the growth of time. This trend is more evident for CuO and TGS2611 sensors. Conversely, RGTO has an increase of \(\Delta\)R for the first five samples, while from the seventh specimen it has the same kind of response as the other two, even if it is less accentuated. This tendency could be explained considering that the number of microorganisms grows over time very quickly; it has been shown that they double their number in BHI broth in 75 min (average value) [33]. Hence, many VOCs could be used by _C. jejuni_ to feed, thus subtracting them from the headspace. At the same time, new compounds are emitted from bacteria and pass in the gaseous phase, as shown in Tables 2 and 3. It is important to underline that for 20 h not only the alcohols have increased in number and amount, but also other compounds. Among them, the one present in greater quantity is pyrindan, a bicyclic compound containing a pyridine ring. The reduction of \(\Delta\)R/R\({}_{0}\) value could be due to action of this compound. Furthermore, it can be noticed that the first sample of the analysis produced a \(\Delta\)R significantly different in respect to the others; it is higher for all sensors, especially for RGTO and nanowire sensors. Since this could be the result of a different conditioning, it has been discarded for the following analysis.  
Figure 2 refers to PCA that has been performed using the five aforementioned sensors. The first two Principal Components (PC) were used for a total explained variance equal to 99.08% (91.77% in PC1 and 7.31 in PC2). It is possible to identify two different trends. For _C. jejuni_ samples, first four samples (0.5-2.5 h) are characterized by descending scores along PC2 axis, while the other specimens (3-20 h) are distributed essentially along PC1 ascending scores. Furthermore, the distance between points decreases as time goes on and it can be explained by the typical growth curve of microorganism. Indeed, it is characterized by four phases: (A) lag phase (bacteria adapt themselves to growth conditions and are yet not able to divide), (B) log phase (cell doubling), (C) stationary phase (growth rate and stationary rate are equal due to a growth-limiting factor such as the depletion of an essential nutrient) and (D) death phase (bacteria die). In PCA, lag phase corresponds to the first four points that move along PC2 axis, log phase to the following eleven samples (3-8 h) and stationary phase to the remaining ones (8.5-20 h). Instead, control samples assume higher scores along both PCs axes with increasing time. It is due to the slow release in the headspace of some VOCs contained in the BHI broth. The only exception is the fourth control sample that does not follow the parabolic trend of the others, but it is closer to _C. jejuni_ points.  
Figure 1: Resistance variations of three sensors once exposed to VOCs. From top to bottom: copper oxide nanowire, TGS2611 and tin oxide Rheotaxial Growth and Thermal Oxidation (RGTO). On the \(x\)-axis there is time in seconds, on the \(y\)-axis normalized resistance.