Abstract：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 μg·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 260～320 nm and the emission wavelength was 300～380 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.
王书涛，刘 娜，程 琪，车先阁，李明珊，崔 凯，王玉田. 三维荧光光谱结合GA-SVM对多环芳烃的分类鉴别[J]. 光谱学与光谱分析, 2020, 40(04): 1149-1155.
WANG Shu-tao, LIU Na, CHENG Qi, CHE Xian-ge, LI Ming-shan, CUI Kai, WANG Yu-tian. Classification and Identification of Polycyclic Aromatic Hydrocarbons by Three-Dimensional Fluorescence Spectroscopy Combined with GA-SVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1149-1155.
[1] SHI Xiao-feng, ZHANG Xin-min, YAN Xia, et al(史晓凤，张心敏，严 霞，等). Acta Optica Sinica(光学学报)，2018, 38(7): 0724001.
[2] Kang Yan, Xie Huijun, Li Bo, et al. Chemical Engineering Journal, 2019, 357: 280.
[3] Li Ruifei, Hua Pei, Zhang Jin, et al. Science of the Total Environment，2019, 633: 438.
[4] Lin Yuanchung, Li Yaching, Kassian T T Amesho, et al. Science of the Total Environment，2019, 660: 188.
[5] LIU Xiao-xue(刘小雪). Jilin University(吉林大学)，2016
[6] DU Yun, ZHENG Ya-nan, WANG Shu-tao(杜 云，郑亚南，王书涛). Optics and Precision Engineering(光学精密工程)，2018，26（9）：2212.
[7] Zhang Yanhao, Chen Yanyan, Li Ruijin, et al. Talanta, 2019, 195: 757.
[8] WANG Shu-tao, ZHENG Ya-nan, WANG Zhi-fang, et al(王书涛，郑亚楠，王志芳，等). Acta Photonica Sinica(光子学报)，2017，46（9）：930002.
[9] Yang Y Z, Peleato N M, Legge R L, et al. Environmental Scieance-Water Research& Technology, 2019，5（2）：３１５..
[10] WANG Yu-tian, LIU Ting-ting, LIU Ling-fei, et al(王玉田，刘婷婷，刘凌妃，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(8): 2441.
[11] WANG Shu-tao, WU Xing, ZHU Wen-hao, et al(王书涛，吴 兴，朱文浩，等). Acta Optica Sinica(光学学报)，2019, 39(5): 0530002.
[12] Moura, Heloise O M A, Camara Anne B F, Santos Marfran C D, et al. Analytical and Bioanalytical Chemistry, 2019, 411(11): 2301.
