The applicability of temperature correction to chemoresistive sensors in an e-NOSE-ANN system

The influence of the temperature sensitivity of chemoresistive, inherently-conductive-polymer (ICP) sensors on the performance of an artificial neural network (ANN) e-NOSE system is evaluated Temperature was found to strongly influence the responses of the chemoresistors. An e-NOSE array of eight ICP sensor elements, a relative humidity (RH) sensor and a resistance temperature device (RTD) was tested at five different RH levels while the temperature was allowed to vary naturally. A temperature correction algorithm based on the temperature coefficient of resistance, β, for each material was independently determined and applied to raw sensor data prior to input to the ANN. Conversely, uncorrected data was passed to the ANN. The performance of the ANN was evaluated by determining the error found between the actual humidity versus the calculated humidity. The error obtained using raw input data was 10.5% and using temperature corrected data 9.3%. This negligible difference demonstrates that the ANN was capable of adequately addressing the temperature dependence of the chemoresistive sensors.