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A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Pengfei Jia (College of Communication Engineering Chongqing, Chongqing University, Chongqing, China)
Fengchun Tian (College of Communication Engineering Chongqing, Chongqing University, Chongqing, China)
Shu Fan (College of Communication Engineering Chongqing, Chongqing University, Chongqing, China)
Qinghua He (Department of Orthopedic and Traumatic Surgery, Center for War Wound and Trauma of PLA, Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, China)
Jingwei Feng (College of Communication Engineering Chongqing, Chongqing University, Chongqing, China)
Simon X. Yang (Advanced Robotics and Intelligent Systems Lab, School of Engineering, University of Guelph, Guelph, Ontario, Canada)

Sensor Review

ISSN: 0260-2288

Publication date: 10 June 2014

Abstract

Purpose

The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy.

Design/methodology/approach

An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification.

Findings

The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection.

Research limitations/implications

To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose.

Practical implications

In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring.

Originality/value

The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Keywords

Acknowledgements

The authors are thankful to the China Postdoctoral Science Foundation funded project (Project No: 20090461445), the Clinical Research Fund of Third Military Medical University (Project No: 2007XG077) and the Fundamental Research Funds for the Central Universities (Project No: CDJXS12161102) for supporting this work.

Citation

Jia, P., Tian, F., Fan, S., He, Q., Feng, J. and X. Yang, S. (2014), "A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization", Sensor Review, Vol. 34 No. 3, pp. 304-311. https://doi.org/10.1108/SR-02-2013-630

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Publisher

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Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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