The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose.
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.
OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection.
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.
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.
The innovative concept paves the way for the application of Enose.
This work was financially supported by the Fundamental Research Funds for the Central Universities (Project No. CDJXS12162212).
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
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