Exploratory data analysis for industrial safety application

We tested the detection properties of four MOX sensors toward different ozone mixtures to identify sets of sensing layers and interfering compounds concentrations most suitable for a reliable detection of ozone. The measurement campaign lasted I year divided in four sessions. We collected a substantial amount of measurements (more than 500) with diverse interfering gases: ammonia, ethanol, ethylene, carbon monoxide and humidity. Due to the dimension of the data set it could not be analyzed using the conventional methods generally applied for characterizing gas sensors: evaluating the sensor performance by visual inspection of the sensors responses is unfeasible. For this reason we systematically applied the exploratory data analysis methodology. We used some simple but effective statistical techniques to insight the data. This approach allows us to draw sound conclusions about the causes of variation in the data, e.g. time (sensors' long-term stability) or interfering effects of different chemical compounds. All the analysis techniques employed in this work are implemented in a software package developed at our laboratory. We concluded that the two best stable and sensitive sensors are based on WO3 and SnO2 (An catalyzed). We ranked the contributions of different gases on sensor responses, deducing that out sensors are suitable to detect steps of 50 ppb of ozone when ethylene is less than 10 ppm. Carbon monoxide does not affect the measurements still, the strongest interfering compound is humidity that needs to be controlled or parallely measured also in a preliminary stage. (c) 2008 Elsevier B.V. All rights reserved.