Electronic Noses Using Quantitative Artificial Neural Networ

The present paper covers a new type of electronic nose(e-nose) with a four-sensor array,which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error(RE) in changing quantitative analysis into the artificial neural network (ANN).. Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, liquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.