首页> 外文期刊>Journal of the Brazilian Chemical Society >X-ray Scattering and Chemometrics as Tools to Assist in the Identification of Gunshot Residues by Wavelength Dispersive X-ray Fluorescence Spectrometry
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X-ray Scattering and Chemometrics as Tools to Assist in the Identification of Gunshot Residues by Wavelength Dispersive X-ray Fluorescence Spectrometry

机译:X射线散射和化学计量学作为工具,有助于通过波长分散X射线荧光光谱法识别枪口残留物

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Wavelength dispersion X-ray fluorescence spectrometry (WDXRF) is a non-destructive technique and therefore attractive for gunshot residues (GSR) analysis. It is well known for determination of inorganic constituents of samples. However, X-ray scattering region spectral data is not commonly used, although it may provide information about organic constituents and their interactions. This work employed X-ray scattering region and inorganic elements spectral data for a better characterization of GSR. Swabs containing residues collected from the hand of people who fired (shooters group) and also from the hands of people which did not fire (control group) with guns were analyzed directly by the WDXRF. Brake pad and people who perform activities that favor the accumulation of characteristic elements of GSR on their hands were chosen to compose the control group. Principal components analysis (PCA) discriminated the GSR according to the firearm/cartridge used. However, similar GSR clustering did not occur without X-ray scatter data, showing the importance of X-ray scattering spectrum for GSR evaluation. The k-nearest neighbors (k-NN) method correctly classified all samples from shooters and control groups, employing from 1 to 5 nearest neighbors. No anomalous behavior was detected by PCA and hierarchical cluster analysis (HCA).
机译:波长色散X射线荧光光谱法(WDXRF)是一种非破坏性技术,因此对于枪口残留物(GSR)分析而吸引。众所周知,用于测定样品的无机成分。然而,X射线散射区域光谱数据不常用,尽管它可以提供有关有机成分及其相互作用的信息。该工作采用X射线散射区域和无机元素光谱数据,以更好地表征GSR。直接由WDXRF直接分析从射击(射击者组)和没有射击(对照组)的人手的人手中收集的残留物的拭子。制动垫和执行有利于他们手上GSR的特征元素积累的活动的人员被选中以构成对照组。主要成分分析(PCA)根据所使用的枪械/墨盒歧视GSR。然而,没有X射线散射数据没有发生类似的GSR聚类,显示了GSR评估的X射线散射谱的重要性。 K-CORMATE邻居(K-NN)方法正确地分类了射击者和对照组的所有样本,采用1到5个最近的邻居。 PCA和分层集群分析(HCA)未检测到异常行为。

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