Electronic nose simulation tool centred on PSpice

Electronic noses consist of an array of non-selective gas sensors with a pattern recognition engine. The sensors and pattern recognition methods depend on the specific application. The design process of electronic noses usually involves time-consuming measurements in a non-standard trial and error process. This paper addresses the problem by using an electronic nose simulation tool centred on PSpice. Using previously developed generic PSpice models for the response of gas sensors, a four-element tin oxide sensor array was simulated. The sensor model parameters were adjusted using calibrated response data from ethanol, methane and their mixtures, detected with real tin oxide sensors. To study the performance of the array, statistical error modelling and PSpice simulations were used in a Monte Carlo analysis coupled with principal component analysis. The results show that this simulation strategy is useful for analysing the effects of sampling errors, the changes in operation temperature, random errors and sensor drift. The importance of these errors is discussed in terms of the array discrimination ability. We conclude that this simulation strategy can help to systematise the design of electronic noses.