QUALITATIVE AND QUANTITATIVE-ANALYSIS OF VOLATILE ORGANIC-COMPOUNDS USING TRANSIENT AND STEADY-STATE RESPONSES OF A THICK-FILM TIN OXIDE GAS SENSOR ARRAY

Quantitative analysis of gases, by means of semiconductor sensor array s and pattern-recognition techniques such as artificial neural network s, 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 sens ors, 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 system s, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through t he measure of its conductance rise time (Tr), when there is a step cha nge in the gas concentration Tr is characteristic of each gas/sensor p air, 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 an d quantify among ethanol, toluene and o-xylene [concentration range: 2 5, 50 and 100 ppm]. A principal component analysis is carried out to s how qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductan ce of the array components are processed with artificial neural networ ks. In a first stage, a feed-forward back-propagation-trained ANN disc riminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified c ompound. Processing data from the dynamic characterisation of the sens or array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state sig nals are used up to 100%. (C) 1997 Elsevier Science S.A.