用于法医和安全应用的爆炸物激光解吸离子迁移谱仪

来源: Giorgio Felizzato et al 发布日期: 2025-01-06 访问量: 236

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在犯罪现场调查中检测爆炸物对法医学至关重要。本研究探讨了激光解吸（LD）离子迁移谱（IMS）作为一种新方法的应用，该方法利用了MaSaTECH开发的新IMS原型。方法：LD取样技术采用激光二极管模块蒸发表面上的爆炸痕迹，无需样品制备即可通过IMS进行即时分析。化学计量学方法，包括多元数据分析，被用于数据处理和解释，包括原始IMS血浆图的预处理和各种模式识别技术，如线性判别分析（LDA）和支持向量机（SVM）。结果：通过在不同材料上使用纯炸药（TNT、RDX、PETN）和炸药产品（SEMTEX 1A、C4）进行实验，验证了IMS原型的有效性
标签: IMS；化学计量学；多元数据分析；数据预处理；爆炸物；犯罪现场调查；法庭的；环境和安全应用

Laser Desorption-Ion Mobility Spectrometry of Explosives for Forensic and Security Applications

Giorgio Felizzato ^1,2 ,Martin Sabo ^3,4, Matej Petrìk ^3,4, Francesco Saverio Romolo ¹

¹Department of Law, University of Bergamo, Via Moroni 255, 24127 Bergamo, Italy
²Department of Drug Science and Technology, University of Turin, Via Giuria 9, 10125 Torino, Italy
³MaSa Tech, s.r.o., Sadová 3018/10, 916 01 Stará Turá, Slovakia
⁴Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovičova 2, Bratislava 4, 842 16 Bratislava, Slovakia
*Author to whom correspondence should be addressed.
Molecules 2025, 30(1), 138; https://doi.org/10.3390/molecules30010138

Abstract

Background: The detection of explosives in crime scene investigations is critical for forensic science. This study explores the application of laser desorption (LD) ion mobility spectrometry (IMS) as a novel method for this purpose utilising a new IMS prototype developed by MaSaTECH. Methods: The LD sampling technique employs a laser diode module to vaporise explosive traces on surfaces, allowing immediate analysis by IMS without sample preparation. Chemometric approaches, including multivariate data analysis, were utilised for data processing and interpretation, including pre-processing of raw IMS plasmagrams and various pattern recognition techniques, such as linear discriminant analysis (LDA) and support vector machines (SVMs). Results: The IMS prototype was validated through experiments with pure explosives (TNT, RDX, PETN) and explosive products (SEMTEX 1A, C4) on different materials. The study found that the pre-processing method significantly impacts classification accuracy, with the PCA-LDA model demonstrating the best performance for real-world applications. Conclusions: The LD-IMS prototype, coupled with effective chemometric techniques, presents a promising methodology for the detection of explosives in forensic investigations, enhancing the reliability of field applications.

在犯罪现场调查中检测爆炸物对法医学至关重要。本研究探讨了激光解吸（LD）离子迁移谱（IMS）作为一种新方法的应用，该方法利用了MaSaTECH开发的新IMS原型。方法：LD取样技术采用激光二极管模块蒸发表面上的爆炸痕迹，无需样品制备即可通过IMS进行即时分析。化学计量学方法，包括多元数据分析，被用于数据处理和解释，包括原始IMS血浆图的预处理和各种模式识别技术，如线性判别分析（LDA）和支持向量机（SVM）。结果：通过在不同材料上使用纯炸药（TNT、RDX、PETN）和炸药产品（SEMTEX 1A、C4）进行实验，验证了IMS原型的有效性。研究发现，预处理方法显著影响分类准确性，PCA-LDA模型在现实应用中表现出最佳性能。结论：LD-IMS原型结合有效的化学计量学技术，为法医调查中的爆炸物检测提供了一种有前景的方法，提高了现场应用的可靠性。

Keywords: IMS; chemometrics; multivariate data analysis; data pretreatment; explosives; crime scene investigation; forensic; environmental and security applications

关键词：IMS；化学计量学；多元数据分析；数据预处理；爆炸物；犯罪现场调查；法庭的；环境和安全应用

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2. Materials and Methods
2.1. Instruments
In this article, a new IMS prototype developed by MaSaTECH (www.masatech.eu) was tested within the RISEN project (www.risen-h2020.eu) for the detection of explosives during crime scene investigations. The IMS instrument is based on the Original Equipment Manufacturer–Advanced Ion Mobility Spectrometer (OEM-AIMS by MaSaTECH, Stará Turá, Slovakia) integrated into a protective case together with a long-lifetime battery (6 h of work) and a small membrane pump (Pfeiffer Vacuum, Korneuburg, Austria) see Figure 1 below. The small membrane pump keeps the IMS drift tube at sub-atmospheric pressure (600 mbar), which allows continuous aspiration of environmental air. The LD module, with a wavelength of 532 nm (green) and a power of 1 Watt, was placed in front of the sniffing capillary. The focused laser beam promotes sample evaporation, and then the sample is immediately aspirated and analysed by IMS.

在这篇文章中，MaSaTECH（www.MaSaTECH.eu）开发的一种新的智能IMS系统原型在RISEN项目（www.RISEN-h2020.eu）中进行了测试，用于在犯罪现场调查中检测爆炸物。IMS仪器基于原始设备制造商——先进离子迁移谱仪（OEM-AIMS by MaSaTECH, Stará Turá, Slovakia），与长寿命电池（工作6小时）和小型隔膜泵（Pfeiffer Vacuum, Korneuburg, Austria）集成在便携箱中，见下图1。小型隔膜泵使IMS漂移管保持在低于大气压（600毫巴）的压力下，从而允许连续吸入环境空气。将波长为532nm（绿色）、功率为1瓦的LD模块放置在嗅探毛细管前方。聚焦的激光束促进样品蒸发，然后样品立即被IMS抽吸和分析。

Figure 1. The IMS MaSaTECH prototype without the protective case (on the left) and the LD-IMS prototype in the protective case (on the right).

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4. Conclusions

The effectiveness of the new prototype of laser desorption IMS developed by MaSaTECH for the detection of explosive traces distributed on surfaces typically found during crime scene investigations has been explored and demonstrated. In addition, the study included a chemometrics approach for data analysis in order to enhance the accuracy and reliability of detection.

The chemometrics workflow involved various steps, including pre-processing of raw data, unsupervised and supervised pattern recognition, and model assessment through cross-validation. The pre-processing method, including denoising, baseline correction, and scaling, played a crucial role in enhancing the quality of the data and the quality of the models.

Linear discriminant analysis (LDA) stood out as the most effective classification method using the autoscaled plasmagrams. An accurate classification model was obtained by grouping classes of explosives based on their active compounds. Furthermore, other classification models such as partial least squares discriminant analysis (PLS-DA), logistic regression (LR), and support vector machines (SVMs) were evaluated. The PLS-DA model accuracy obtained was numerically lower than that of the PCA-LDA model. Logistic regression was applied following principal component analysis (PCA) to address the correlation among drift times. The use of the “Newton-cg” kernel for setting up the model provided an accuracy of 90% when the data were pre-processed by the SNV. High accuracy was achieved when autoscaled plasmagrams were employed with linear, radial basis function, and sigmoid kernels.

In sum, the IMS prototype developed by MaSaTECH coupled with the chemometrics approach described in this article was shown to be a promising methodology for the detection of explosives during crime scene investigations.

MaSaTECH开发的激光解吸智能IMS系统新原型用于检测犯罪现场调查中通常发现的表面上分布的爆炸痕迹的有效性得到了探索和证明。此外，该研究还包括一种用于数据分析的化学计量学方法，以提高检测的准确性和可靠性。

化学计量学工作流程涉及多个步骤，包括原始数据的预处理、无监督和有监督的模式识别，以及通过交叉验证进行模型评估。预处理方法，包括去噪、基线校正和缩放，在提高数据质量和模型质量方面发挥了至关重要的作用。

线性判别分析（LDA）是使用自动缩放等离子图进行分类的最有效方法。通过根据爆炸物的活性化合物对爆炸物进行分类，获得了一个准确的分类模型。此外，还评估了偏最小二乘判别分析（PLS-DA）、逻辑回归（LR）和支持向量机（SVM）等其他分类模型。所获得的PLS-DA模型精度在数值上低于PCA-LDA模型。在主成分分析（PCA）之后应用逻辑回归来解决漂移时间之间的相关性。当SNV对数据进行预处理时，使用“Newton-cg”内核建立模型提供了90%的准确性。当使用具有线性、径向基函数和S形核的自动缩放等离子图时，实现了高精度。

总之，MaSaTECH开发的IMS原型与本文所述的化学计量学方法相结合，被证明是犯罪现场调查中检测爆炸物的一种有前景的方法。

原文献链接：Laser Desorption-Ion Mobility Spectrometry of Explosives for Forensic and Security Applications

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