The influence of the temperature coefficient of resistance in the cheinoresistive response of inherently conductive polymer (ICP) sensors in the performance of an artificial neural network (ANN) e-natural olfactory sensor emulator1 (e-NOSE) system is evaluated. Temperature was found to strongly influence the responses of the chemoresistors, even over modest ranges (ca. 2 °C). An e-NOSE array of eight ICP sensor elements, a relative humidity (RH ± 0.1%) sensor, and a resistance temperature device (RTD ± 0.1°C) was tested at five different RH levels while the temperature was allowed to vary with the ambient. A temperature correction algorithm based on the temperature coefficient of resistance β for each material was independently and empirically determined then applied to the raw sensor data prior to input to the ANN. Conversely, uncorrected data was also 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 sensor data was found to be 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 once temperature was inclusively passed to the ANN.
- Artificial neural networks
- Chemoresistive sensors
- E-natural olfactory sensor emulator (e-NOSE)
- Temperature dependence
ASJC Scopus subject areas
- Electrical and Electronic Engineering