Classification and Identification of Polycyclic Aromatic Hydrocarbons by Three-Dimensional Fluorescence Spectroscopy Combined with GA-SVM

As an aromatic compound, Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in human production and life. They have strong carcinogenicity and threaten human lives and health. Therefore, it is necessary to implement a simple, efficient, universal and accurate method to detect polycyclic aromatic hydrocarbons. According to the common types of polycyclic aromatic hydrocarbons, solid powdery substances of polycyclic aromatic hydrocarbon naphthalene (NAP), fluorene (FLU) and acenaptene (ANA) were selected as experimental samples. Of all the samples NAP, FLU, ANA powder were carried out by 1 g and dissolved in a small amount of methanol (spectral grade) solution, then transferred them to 100 mL of deionized water solution, getting a configure PAHs standard solution. The experiment was carried out by the FS920 fluorescence spectrometer. In order to avoid the Rayleigh scattering effect generated by the fluorescence spectrometer itself, the initial emission wavelength was set to lag the excitation wavelength by 10 nm. It could obtain the fluorescence spectrum of the aqueous solution of ANA, NAP and FLU, on the basis of the standard solution, a 0. 1 mu g . mL(-1) aqueous solution of a simple substance was placed. Then, different volumes of ANA, NAP and FLU were mixed to form two mixed solutions, each of them formed a mixed solution of 16 different concentration ratios, and then took different volumes. The three solutions were mixed with each other, they were shaken and finally a total of 48 mixed solutions of different volume ratios were formed. Finally, the experimental data were input into Matlab to obtain the fluorescence spectrum of the mixed solution of naphthalene, anthracene and anthracene naphthalene. It was found that the excitation wavelength of the mixed solution was in the wavelength range of 260 similar to 320 nm and the emission wavelength was 300 similar to 380 nm, and the position of the optimal emission wavelength was similar. Most of the excitation wavelengths corresponding to the fluorescence peaks overlap. Support vector machine (SVM) based on genetic algorithm (GA) optimization was applied to the species detection of PAHs mixture, because the shortage of species in which the fluorescence spectrum cannot directly react with the mixture solution. The data were randomly scrambled, and the genetic evolutionary algorithm havd a termination evolution algebra of 200. Training data and prediction data are 36 and 12, respectively. Under the optimal conditions , the average accuracy of the training result was 95.42%. The experimental results were evaluated by comparing with traditional support vector machine and BP neural network. The results showed that SVM based on genetic algorithm optimization has potential for the smaller classification error and can distinguish the mixture more accurately.