摘要
快速准确识别不明危险液体在公共安全领域需求明显。拉曼光谱技术因具有快速、灵敏、可非接触式检测等优点,成为近年来此领域的研究热点。以沙林、梭曼、塔崩、维埃克斯、芥子气等化学毒剂,磷酸三甲酯、磷酸三乙酯、磷酸三丁酯、甲基膦酸二甲酯、甲基膦酸二异丙酯等化学毒剂模拟剂,亚磷酸二甲酯、亚磷酸三甲酯、亚磷酸三乙酯、甲基膦酰氯乙酯、甲基膦酰二氯、甲基膦酰二氟、氯沙林、二乙胺基磷酰氯、2-二乙胺基乙硫醇、硫二甘醇、异丙醇、频呐基醇、甲基膦酸、甲基膦酸异丙酯、甲基膦酸频呐基酯等化学毒剂前体、中间产物、水解产物以及有毒工业化学品如邻二甲苯、间二甲苯、苯甲醚、氯代苯、乙酸乙酯、乙酸乙烯酯、乙酸苄酯、甲醇、乙醇、乙腈、丙酮、1,1,1-三氯乙烷、正己烷、正丁醇、四氯化碳等和汽油、水等42种危险液体和常见溶剂为研究对象,使用配备785 nm激光器的便携式拉曼光谱仪,针对上述化合物建立了拉曼光谱检测方法,获得了高信噪比的散射光谱数据,对谱图特征进行了分析。采用线性判别分析(LDA)、二次判别分析(QDA)、k近邻(kNN)、朴素贝叶斯(NB)模型、分类决策树(CT)、支持向量机(SVM)6种模式识别算法对上述拉曼光谱数据进行识别归类。研究结果表明,支持向量机、线性判别分析模型具有100%的识别准确率,考虑到实际使用过程中非标准谱图、仪器条件以及外界环境改变等因素会对支持向量机识别结果产生影响,将线性判别分析模型确定为危险液体的快速识别方法。全部测试过程在1~2 min内即可完成且不损耗样品,成功实现水和危险品汽油与其他有毒液体的区分。该研究揭示了具有指纹谱特征的拉曼光谱结合模式识别算法可用于化合物的快速筛查,为及时发现通关夹带,保证物流安全以及化学恐怖事件应急处置等提供了技术支撑。
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.
作者
南迪娜
董力强
傅文翔
刘卫卫
孔景临
NAN Di-na;DONG Li-qiang;FU Wen-xiang;LIU Wei-wei;KONG Jing-lin(State Key Laboratory of NBC Protection for Civilian,Beijing 102205,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2021年第6期1806-1810,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61775017)资助。
关键词
拉曼光谱
化学危险品
化学毒剂
线性判别分析
Raman spectroscopy
Hazardous chemicals
Chemical warfare agents
Linear discriminant analysis
