|
|
|
|
|
|
Qualitative and Quantitative Identification of a Dangerous Liquid Mixture by Raman Spectroscopy |
ZHANG Tao, HAO Feng-long, JIA Er-hui, ZHANG Qing-sheng, ZHAO Ying, LI Pei-he |
First Research Institute of the Ministry of Public Security, Beijing 102200, China |
|
|
Abstract The qualitative and quantitative analysis of dangerous liquid mixtures by Raman spectroscopy has always been a difficult problem in field application. To solve this problem, this paper aoalyzes the changes of peak position, peak value and peak shape of Raman spectra after mixing, and innovatively constructs the mapping relationship from mixture components to mixture Raman spectroscopy. The mapping relation describes that the Raman characteristic peak response of the mixture is only related to the Raman characteristic peak response of each component and the mixing ratio of each component in the mixture. Based on the inverse matrix calculation, the mixing ratio of each component can be inversely deduced from the Raman spectra of the mixtures. So, in this paper the qualitative and quantitative identification method of dangerous liquid mixtures is proposed. The main steps include: First, collected Raman spectroscopy. Second, processed spectral data and obtained Raman characteristic peaks. Third, calculated the positive and negative matching coefficient between database standard samples and test samples. Finally, if the matching coefficients of both positive and negative characteristic peaks were high enough to satisfy certain threshold conditions, the test samples could be identified as a certain purity. If not a purity, the test samples would be analyzed as a mixture. In this part, the compositions whose negative matching coefficient of Raman spectra characteristic peaks is high will be determined as the compositions of the mixture, and proportion of mixture components is calculated. In the experimental part, acetone, toluene, trichloromethane, ethanol and their mixtures were selected to study. When the mixture was mixed with acetone and ethanol at a ratio of 3∶7, the calculated values of the mixtures by calculation using the method proposed in this paper were 0.245 7 for acetone and 0.706 0 for ethanol. When the mixture sample was composed of toluene and trichloromethane in a ratio of 3∶7, the calculated values of the mixtures were 0..323 4 for toluene, 0.763 0 for trichloromethane. When the mixture sample was composed of acetone, toluene and ethanol in a ratio of 4∶3∶3, the calculated values of the mixtures were 0.795 9 for acetone, 0.303 5 for toluene and 0.287 5 for ethanol. The results show that the calculated values of the mixed components were basically in agreement with the actual values, and the qualitative and quantitative identification method of Raman spectroscopy for dangerous liquid mixtures can accurately determine the composition of each mixture and the proportion of each component in the mixture from the mixed Raman spectroscopy when the components of dangerous liquid mixture are two or three. It has great application value for the field identification of dangerous liquid mixtures.
|
Received: 2018-09-18
Accepted: 2019-02-26
|
|
|
[1] Ciro Augusto Fernandes Penido,Marcos Tadeu Tavares Pacheco,Igor K Lednev,et al. J. Raman Spectrosc. ,2016,47(1):28.
[2] Nuntawong N,Eiamchai P,Somrang W,et al. Sensors and Actuators B: Chemical,2017,239:139.
[3] WANG Hong-qiu,ZHANG Jian-hong,ZHANG Li(王红球,张建红,张 丽). The Journal of Light Scattering(光散射学报),2014,26(3):276.
[4] ZHAO Jing-you,WANG Yong,ZHANG Guang-nan(赵璟悠,王 勇,张冠男). Journal of Criminal Investigation Police University of China(中国刑警学院学报),2018,(3):116.
[5] Muehlethaler C,Leona M,Lombardi J R. Anal. Chem. ,2016,88:152.
[6] PENG Ying,ZHANG Zhi-min,LU Hong-mei,et al(彭 颖,张志敏,卢红梅,等). Journal of Instrumental Analysis(分析测试学报),2017,(5):627.
[7] XIONG Zhi-xin,YANG Hao,MO Wei-lin,et al(熊智新,杨 浩,莫卫林,等). Journal of Analytical Science(分析科学学报),2018, 34(6):772.
[8] LI Bin,ZHANG Tao,JIA Er-hui(李 彬,张 涛,贾二惠). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2015,35(2):429.
[9] HU Zhi-yu,WANG Qiang(胡志裕,王 强). Journal of Test and Measurement Technology(测试技术学报),2016, 30(5): 400. |
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[3] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
[4] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
[5] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
[6] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[7] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[8] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[9] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[10] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[11] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
[12] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[13] |
ZHU Hua-dong1, 2, 3, ZHANG Si-qi1, 2, 3, TANG Chun-jie1, 2, 3. Research and Application of On-Line Analysis of CO2 and H2S in Natural Gas Feed Gas by Laser Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3551-3558. |
[14] |
LIU Jia-ru1, SHEN Gui-yun2, HE Jian-bin2, GUO Hong1*. Research on Materials and Technology of Pingyuan Princess Tomb of Liao Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3469-3474. |
[15] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
|
|
|
|