Chemical Research in Chinese Universities ›› 2001, Vol. 17 ›› Issue (4): 380-386.

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Electronic Noses Using Quantitative Artificial Neural Networ

WEN Li-jing, BIAN Li-ping, LU Yu, ZHANG Mei-zhuo, YU Li-ping, YANG Peng-yuan   

  1. Department of Chemistry, Fudan University, Shanghai 200433, P. R. China
  • Received:2001-09-10 Online:2001-12-24 Published:2011-08-04
  • Supported by:

    Supported by the Ministry of Science and Technology of China(Contract # 96-A23-03-07) and partially by National Natural Science Foundation of China(No.29485009).

Abstract: 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.

Key words: E-nose, ANN, Relative error, Quantitative analysis