No 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.

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.

No abstract