Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization

Abstract

Purpose

The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose.

Design/methodology/approach

Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization.

Findings

OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection.

Research limitations/implications

Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection.

Practical implications

In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC.

Originality/value

The innovative concept paves the way for the application of Enose.

Citation

Feng, J., Tian, F., Jia, P., He, Q., Shen, Y. and Fan, S. (2014), "Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization", Sensor Review, Vol. 34 No. 4, pp. 389-395. https://doi.org/10.1108/SR-10-2012-710