Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array

TLDR: Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.

Abstract: Quantitative analysis of gases, by means of semiconductor sensor arrays and pattern-recognition techniques such as artificial neural networks, has been the goal of a great deal of work over the last few years. However, the lack of selectivity, repeatability and drifts of the sensors, have limited the applications of these systems to qualitative or semi-quantitative gas analysis. While the steady-state response of the sensors is usually the signal to be processed in such analysis systems, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through the measure of its conductance rise time (Tr), when there is a step change in the gas concentration. Tr is characteristic of each gas/sensor pair, concentration-independent and shows higher repeatability than the steady state measurements. An array of four thick-film tin oxide gas sensors and pattern-recognition techniques are used to discriminate and quantify among ethanol, toluene and o-xylene [concentration range: 25, 50 and 100 ppm]. A principal component analysis is carried out to show qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductance of the array components are processed with artificial neural networks. In a first stage, a feed-forward back-propagation-trained ANN discriminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified compound. Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.