# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 3 Results and Discussion
### Gas-Sensing Setup Characterization  
The developed sensing setup composed of a set of four different sensors located in the gas chamber of the device was characterized by conducting a series of measurements under exposure to diabetes biomarkers such as acetone and ethanol. Figure 6 shows the gas-sensing characteristics under exposure to various acetone concentration (20-200 ppm) of each of the gas sensor _S1_-S4 within the array. For a better perspective, all curves are shown on the same scale. As can be observed, the sensors exhibit a response to acetone, however, with a largely differentiated sensitivity as expected. Moreover, the relatively short recovery time is to be dealt with in the devices firmware. The sharp lines are the artifacts related to measurement conditions that switched the effect of the gas-dosing system.  
Figure 5: User interface screens of the developed portable gas analyzer. Splash screen (**a**), main menu (**b**), preheat setting (**c**), and executing (**d**) measurement screen presenting the acetone concentration and the raw data from each sensor (**e**). Alternatively, the analyzer can be controlled through a serial interface (**f**).  
Despite the S1 sensor being the one dedicated for acetone sensing due to its high sensitivity, there were others acting in a similar fashion. Figure 7 shows the gas-sensing responses of sensor S3, which was non-dedicated to acetone detection, however, as can be observed under exposure to acetone, it reacted with resistance changes. This cross-sensitivity effect is a disadvantage of metal-oxide based sensors and has to be taken into account within the firmware.  
Finally, Figure 8 shows the calibration curves of all four sensors in the gas sensor array in the 2-20 ppm range of acetone. The sensors S1-S3 reacted with acetone in the measurement range and their response can be fitted using an exponential function with high R\({}^{2}\) values of 0.91, 0.95, and 0.95, respectively. On the other hand, Sensor S4 exhibited a constant response (\(\sim\)5.5), which was related to the humidity level. Sensors S1 and S2 are dedicated to acetone measurement while S3 and S4 are not. Despite that, sensor S3 exhibits a cross-sensitivity issue.  
Figure 6: The gas-sensing characteristics of each particular gas sensor in the gas-sensing chamber under exposure to acetone in the 20–200 ppm range. Optimal operating temperature (given by the manufacturer) was ensured and 50% relative humidity was stabilized.  
Figure 7: The resistance changes of sensor S3 (non-dedicated to acetone detection) in a gas sensor array under exposure to 2–20 ppm of acetone. Optimal operating temperature (given by the manufacturer) was ensured and 50% relative humidity was stabilized.  
It is important to note, however, that for the purpose of measurements the developed sensing array is to be considered as a whole, in other words, its multi-dimensional response characteristics can be characterized and calibrated into the firmware. Further processing blocks such as a neural network are to be trained beforehand and fed in with the measurement data. The key point is to select sensors in such a way that they exhibit variable (including high) sensitivity on the diabetes markers as well as on other breath constituents. Such a combination along with proper signal processing enables measurements of low acetone concentrations with a high level of confidence.