Classification of Electronic Nose Data Using the Least Squares Support Vector Machine

In this study, the response signals of three kinds of dry alfalfa volatile odors were collected by an electronic nose (E-nose), and the collected data were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). A least squares support vector machine (LS-SVM) model was established to classify and evaluate the data. For the combined E-nose algorithm, the classification accuracies of the PCA-LS-SVM and LDA-LS-SVM models are 85% and 100%, respectively. LDA as the input model has better classification accuracy than the PCA-based model. The results show that the combination of the LDA and LS-SVM algorithms using an E-nose signal is effective in identifying different drying alfalfa. The performance of the LDA-based LS-SVM model is slightly higher than that of the PCA-based LS-SVM model. It can be concluded that the E-nose system combined with the LDA-based model has great potential to distinguish different dry alfalfa. (paper)