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利用便携式近红外光谱和多元数据分析技术快速现场鉴定完整爆炸物。

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis.

机构信息

Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.

Netherlands Forensic Institute (NFI), Laan van Ypenburg 6, 2497 GB Den Haag, The Netherlands.

出版信息

Sensors (Basel). 2023 Apr 7;23(8):3804. doi: 10.3390/s23083804.


DOI:10.3390/s23083804
PMID:37112149
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10146942/
Abstract

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

摘要

在爆炸前犯罪现场,对于快速、现场、易于使用、非侵入式的完整高能材料的化学识别,一直存在法医学和安全方面的需求。仪器小型化、数字数据的无线传输和云存储以及多元数据分析等最新技术进步,为近红外(NIR)光谱在法医学中的应用创造了新的、非常有前途的选择。本研究表明,除了滥用药物外,便携式 NIR 光谱与多元数据分析相结合,还为识别完整的高能材料和混合物提供了极好的机会。NIR 能够表征法医爆炸物调查中广泛的感兴趣的化学物质,包括有机和无机化合物。对实际法医案件样本的 NIR 特征分析令人信服地表明,该技术能够处理法医爆炸物调查中遇到的化学多样性。在 1350-2550nm NIR 反射光谱中包含的详细化学信息允许在给定的高能材料类别内正确识别化合物,包括硝基芳烃、硝基胺、硝酸酯和过氧化物。此外,还可以对包含 PETN(季戊四醇四硝酸酯)和 RDX(三硝基三嗪烷)的塑料配方等高能材料混合物进行详细的特征描述。所呈现的结果表明,高能化合物和混合物的 NIR 光谱具有足够的选择性,可以防止广泛的与食物相关的产品、家用化学品、用于制造自制爆炸物的原材料、滥用药物和有时用于制造虚假简易爆炸装置的产品出现假阳性结果。然而,对于经常遇到的烟火混合物,如黑火药、闪光粉和无烟火药,以及一些基本的无机原料,NIR 光谱的应用仍然具有挑战性。另一个挑战是遇到受污染、老化和降解的高能材料或劣质 HME(自制炸药)的案例样本,其光谱特征与参考光谱有很大差异,可能导致假阴性结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/aac19b1032c9/sensors-23-03804-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/56fc5177f0e8/sensors-23-03804-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/ce022cc7eb35/sensors-23-03804-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/b4e460c2f207/sensors-23-03804-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/aac19b1032c9/sensors-23-03804-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/beb46d75081e/sensors-23-03804-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/51a88306bd72/sensors-23-03804-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/7dfecf005615/sensors-23-03804-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/bd282078777a/sensors-23-03804-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/2937568ede96/sensors-23-03804-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/55b0c1c87150/sensors-23-03804-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/56fc5177f0e8/sensors-23-03804-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/ce022cc7eb35/sensors-23-03804-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/b4e460c2f207/sensors-23-03804-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab60/10146942/aac19b1032c9/sensors-23-03804-g010.jpg

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A systematic review of sensors to combat crime and routes to further sensor development.

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本文引用的文献

[1]
Portable near infrared spectroscopy for the isomeric differentiation of new psychoactive substances.

Forensic Sci Int. 2022-12

[2]
On-site forensic analysis of colored seized materials: Detection of brown heroin and MDMA-tablets by a portable NIR spectrometer.

Drug Test Anal. 2022-10

[3]
A calibration friendly approach to identify drugs of abuse mixtures with a portable near-infrared analyzer.

Drug Test Anal. 2022-6

[4]
Electrochemical determination of nitroaromatic explosives using glassy carbon/multi walled carbon nanotube/polyethyleneimine electrode coated with gold nanoparticles.

Talanta. 2022-2-1

[5]
Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression.

ACS Omega. 2021-8-27

[6]
Providing illicit drugs results in five seconds using ultra-portable NIR technology: An opportunity for forensic laboratories to cope with the trend toward the decentralization of forensic capabilities.

Forensic Sci Int. 2020-12

[7]
Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers.

Chemistry. 2021-1-21

[8]
Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.

Drug Test Anal. 2020-10

[9]
Reliability of Aerosol Jet Printed Fluorescence Quenching Sensor Arrays for the Identification and Quantification of Explosive Vapors.

ACS Omega. 2017-10-9

[10]
The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays.

Chem Rev. 2018-9-12

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