A KECA identification method based on GA for E-nose data of six kinds of Chinese spirits

Abstract

In order to improve the correct identification rate of six types of Chinese spirits using electronic nose (E-nose), the Kernel entropy component analysis (KECA) identification method combined with Genetic algorithm (GA) was proposed. Firstly, integral value (INV), relative steady-state average value (RSAV) and wavelet energy value (WEV) were extracted and employed to represent the E-nose data. Secondly, radial basis function (RBF) was selected as the kernel function, then the kernel parameter η of RBF was optimized by the matrix similarity measurement method and the GA. The corresponding optimized kernel parameter η was 16.8608 (matrix similarity measurement) and 67.9039 (GA), respectively. When the first 125 kernel entropy components were selected for Fisher discriminant analysis (FDA), the correct identification rate of FDA (KECA + FDA) combined with GA were 97.62 % and 98.81 % for the training set and testing set, respectively; the correct identification rate of FDA (KECA + FDA) combined with matrix similarity measurement were 93.58 and 91.67 % for the training set and testing set, respectively. Therefore, the kernel parameter η determined by GA was significantly better than that of matrix similarity measurement. Finally, the correct identification rate of FDA and KECA + FDA was compared, and the results of FDA were only 82.14 % and 79.92 % for the training set and testing set, respectively. The identification results of FDA were far worse than that of KECA + FDA. The KECA + FDA method combined with GA was suitable for the identification of the six types of Chinese spirits by E-nose.