| Electronic Noses Using Quantitative Artificial Neural Network |
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| 作者姓名： | WEN Li-jing BIAN Li-ping LU Yu ZHANG Mei-zhuoYU Li-ping and YANG Peng-yuan* * |
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| 作者单位： | Department of Chemistry，Fudan University，Shanghai 200433，P. R. China |
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| 基金项目： | Ministry of Science and Technology of China（Contract ＃96－A23－03－07）and partially by NationalNatural Science Foundation of China（No．29485009）． |
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| 摘 要： | 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, iiquefied-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.
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Electronic Noses Using Quantitative Artificial Neural Network |
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| WEN Li-jing,BIAN Li-ping,LU Yu,ZHANG Mei-zhuoYU Li-ping and YANG Peng-yuan* *.Electronic Noses Using Quantitative Artificial Neural Network[J].Chemical Research in Chinese University,2001(4). |
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| Authors: | WEN Li-jing BIAN Li-ping LU Yu ZHANG Mei-zhuoYU Li-ping and YANG Peng-yuan |
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| Institution: | WEN Li-jing,BIAN Li-ping,LU Yu,ZHANG Mei-zhuoYU Li-ping and YANG Peng-yuan* * |
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| 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, iiquefied-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. |
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| Keywords: | Keywords E-nose ANN Relative error Quantitative analysis |
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