Discrimination of polycyclic aromatic hydrocarbons based on fluorescence spectrometry coupled with CS-SVM

Abstract

A new approach for discrimination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on fluorescence coupled with CS-SVM. Two groups of experiments were carried out on PAHs with similar spectra. The first one was PAHs any two among benzo[k]fluoranthene (BkF), benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP). The second one was naphthalene (NAP) and fluorene (FLU) with similar spectra. The BaP-BkF mixture, BaP-BbF mixture and BbF-BkF mixture with similar fluorescence properties, cuckoo search algorithm (CS) optimizing support vector machine (SVM) was used to discriminate these three mixtures. By comparison with the basic grid search algorithm (GS), genetic algorithm (GA) and particle swarm optimization algorithm (PSO) optimizing SVM, it was found that fitting degree of CS was the best and the convergence speed was also the fastest. The test sample classification accuracy of CS-SVM can reach 100%, which was higher than that of GS-SVM, GA-SVM and PSO-SVM. In order to verify the validity of the proposed approach, all the above methods were applied to discriminate NAP and FLU with extremely similar spectra. The test sample classification accuracy can reach 100%. The satisfying results indicated that the proposed approach had potential to be an alternative approach for discriminating PAHs in environment.