Abstract
Purpose - 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. To improve the performance of E-nose in the detection of wound infection, a new optimization algorithm is proposed to realize a synchronous optimization of sensor array and classifier. Design/methodology/approach - An enhanced quantum-behaved particle swarm optimization based on genetic algorithm (G-QPSO) is proposed to realize a synchronous optimization of sensor array and classifier. 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 (RBF) network and support vector machine (SVM) are used for classification.Findings - The classification accuracy of E-nose is the highest when the weighting coefficients of 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 - 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. To improve the performance of E-nose in the detection of wound infection, a new optimization algorithm is proposed to realize a synchronous optimization of sensor array and classifier. Design/methodology/approach - An enhanced quantum-behaved particle swarm optimization based on genetic algorithm (G-QPSO) is proposed to realize a synchronous optimization of sensor array and classifier. 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 (RBF) network and support vector machine (SVM) are used for classification.Findings - The classification accuracy of E-nose is the highest when the weighting coefficients of 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.