Research on practical electronic nose algorithm for the detecting of volatile chemicals

DONG Zhi-gang1; LI Min-qiang1; LUO Tao1; LIU Jin-huai1

doi:10.13873/j.1000-97872011.01.040

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Research on practical electronic nose algorithm for the detecting of volatile chemicals

更新时间：2025-09-11

- Research on practical electronic nose algorithm for the detecting of volatile chemicals
- Vol. 30, Issue 1, Pages: 29-30+34(2011)
- 作者机构：
  
  1. 安徽省仿生感知与先进机器人重点实验室
  2. 中国科学院合肥物质科学研究院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.01.040
  CLC： TP212
- Published：2011
- 稿件说明：
移动端阅览
[1]董志钢,李民强,罗涛,刘锦淮.用于易挥发性化学品检测的实用电子鼻算法研究[J].传感器与微系统,2011,30(01):29-30+34.

DONG Zhi-gang1, LI Min-qiang1, LUO Tao1, et al. Research on practical electronic nose algorithm for the detecting of volatile chemicals[J]. 2011, 30(1): 29-30+34.
[1]董志钢,李民强,罗涛,刘锦淮.用于易挥发性化学品检测的实用电子鼻算法研究[J].传感器与微系统,2011,30(01):29-30+34. DOI： 10.13873/j.1000-97872011.01.040.

DONG Zhi-gang1, LI Min-qiang1, LUO Tao1, et al. Research on practical electronic nose algorithm for the detecting of volatile chemicals[J]. 2011, 30(1): 29-30+34. DOI： 10.13873/j.1000-97872011.01.040.

摘要

由气体传感器阵列构建的电子鼻系统在易挥发性化学品的快速检测中起着重要的作用。利用12路气体传感器阵列采集易挥发性化学品蒸气的响应曲线

通过研究具有一定抗干扰能力的具有现场实用意义的电子鼻识别算法

提出了一种基于BP神经网络的算法来提取特征向量。所需计算量小

稳定性高

受浓度影响小;在此基础上进行神经网络的训练并对样本进行测试

达到了90%的平均识别率

验证了算法具有实用性。

Abstract

Electronic nose made up of gas sensors array now is playing an important role in the detection of volatile chemicals.Aimed at the research on practical electronic nose recognition algorithm which has anti-jamming ability and can be used in local detecting

response curves of 9 kinds of volatile gases of chemicals are acquired by the array made up of 12 gas sensors.To make it more practical

a new method of algorithm is proposed for feature extraction based on the BP neural network.It needs less calculation

has high stability and is not easily influenced by the gas concentration.On the basis of the network training and test on the samples

and the average recognition rate reaches 90%

and it approves that this algorithm is practical.

关键词

Keywords

references

基于SnO 2 气体传感器农药残留的动态检测及定性定量分析 [J]. 黄行九,孟凡利,刘锦淮,皮宗新,余增亮. 分析测试学报 . 2004(04)

论走私、贩卖易制毒化学品案件的特点及防范对策 [J]. 苗翠英,张晶. 公安大学学报 . 2002(01)

电子鼻技术新进展及其应用前景 [J]. 高大启,杨根兴. 传感器技术 . 2001(09)

神经网络、模糊系统及其在运动控制中的应用[M]. 中国科学技术大学出版社 , 丛爽著, 2001

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