Abstract：Fast and accurate identification of unknown hazardous fluids are of pivotal interest in public security and safety. Raman spectroscopy is a fast and sensitive non-contacting measurement technology. Its virtues have it has become one of the important technologies in the public security field in recent years. In this study, the Raman spectra of forty-two dangerous and common liquids were measured: five chemical warfare agents (including sarin, soman, tabun, VX, and mustard gas), and their fifteen precursors, hydrolysates (including dimethyl hydrogen phosphite, trimethyl phosphite, triethyl phosphite, ethyl methylphosphonochloridoate, methylphosphonic dichloride, methylphosphonic difluoride, chlorosarin, bis(diethylamino)phosphoryl chloride, 2-(diethylamino)ethanethiol, thiodiglycol, isopropyl alcohol, 3,3-dimethyl-2-butanol, methylphosphonic acid, isopropyl methylphosphonate, and pinacolyl methylphosphonate), five chemical warfare agents simulants (including trimethyl phosphate, triethyl phosphate, tributyl phosphate, dimethyl methylphosphonate, and diisopropyl methylphosphonate), fifteen toxic industrial compounds (including o-xylene, m-xylene, anisole, chlorobenzene, ethyl acetate, vinyl acetate, benzyl acetate, methanol, ethanol, 1-butanol, acetonitrile, acetone, hexane, 1,1,1-trichloroethane, and carbon tetrachloride), gasoline, and water. Raman spectroscopy detection method for these compounds using a portable Raman spectrometer equipped with a 785 nm excitation laser was developed to obtain high SNR scattering spectrum data. Structural assignments to Raman bands observed in the spectrum were also proposed. Six pattern recognition algorithms, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), naive bayesian (NB), classification tree (CT), and support vector machine (SVM) were studied to identify and classify Raman spectrum data. The results show that support vector machine and linear discriminant analysis model could realize the fast identification with a high recognition accuracy rate of 100%. However, considering the influence of non-standard spectrum, instrument conditions, and changes in the external environment on support vector machine recognition results, the linear discriminant analysis model seemed superior in identifying the aforementioned dangerous liquids. Meanwhile, all the testing process can be completed within 1~2 minutes without loss of samples. It can be concluded from this study that the combination of Raman spectroscopy with fingerprint characteristics and pattern recognition algorithm can be used for rapid screening of unknown compounds. Moreover, this method provides solutions for timely detection of customs clearance, guarantee of logistics security, and emergency response to chemical terrorist incidents.
Key words：Raman spectroscopy; Hazardous chemicals; Chemical warfare agents; Linear discriminant analysis
南迪娜，董力强，傅文翔，刘卫卫，孔景临. 基于拉曼光谱的危险液体快速识别研究[J]. 光谱学与光谱分析, 2021, 41(06): 1806-1810.
NAN Di-na, DONG Li-qiang, FU Wen-xiang, LIU Wei-wei, KONG Jing-lin. Fast Identification of Hazardous Liquids Based on Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1806-1810.
[1] WANG Hong-qiu, ZHANG Li, WANG Lu, et al(王红球, 张 丽, 王 璐, 等). J. Light Scattering(光散射学报), 2012, 24(4): 367.
[2] ZHANG Tao, HAO Feng-long, JIA Er-hui, et al(张 涛, 郝凤龙, 贾二惠, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(11): 3372.
[3] LI Tian-shu, YAO Qi-feng, LI Hong, et al(李天舒, 姚齐峰, 李 红, 等). Infrared and Laser Eng.(红外与激光工程), 2019, 48(7): 204.
[4] JIANG Xin, SHANG Zhao-cong, QIAN Yu-ting(蒋 鑫, 商照聪, 钱玉婷). Shandong Industrial Technology(山东工业技术), 2017，(19): 220.
[5] Christesen S D, Guicheteau J A, Curtiss J M, et al. Opt. Eng., 2016, 55(7): 074103.
[6] Ortiz-Rivera W, Pacheco-Londono L C, Hernández-Rivera S P. Sensing and Imaging: An International Journal, 2010, 11(3): 131.
[7] Hoffmann J A, Miragliotta J A, Wang J, et al. Int. Soc. Opt. Eng. Photonics, 2012, 8373: 83732.
[8] Wang J, Duan G, Liu G, et al. J. Hazard. Mater., 2016, 303: 94.
[9] Lafuente M, Pellejero I, Sebastián V, et al. Sens. Actuator A Chem., 2018, 267: 457.
[10] Christesen S D, Jones J P, Lochner J M, et al. Appl. Spectrosc., 2008, 62(10): 1078.
[11] Hakonen A, Rindzevicius T, Schmidt M S, et al. Nanoscale, 2015, 8(3): 1305.
[12] Choi S K, Jeong Y S, Koh Y J, et al. Bull. Kor. Chem. Soc., 2019, 40(3): 279.
[13] Kondo T, Hashimoto R, Ohrui Y, et al. Forensic. Sci. Int., 2018, 291: 23.
[14] Choi S K, Jeong Y S, Koh Y J, et al. Bull. Kor. Chem. Soc., 2019, 40(6): 483.
[15] Galan-Freyle N J, Figueroa-Navedo A M, Pacheco-Londoño Y C, et al. Anal. Chem., 2014, 2: 15.
[16] El Alami A, Lagarde F, Tamer U, et al. Vib. Spectrosc., 2016, 87: 27.
[17] Wu X, Zhang S, Guo X, et al. Environ. Pollut., 2019, 253: 731.
