Identification of Luminescent Markers for Gunshot Residues: Fluorescence, Raman Spectroscopy, and Chemometrics

Gunshot residue (GSR) is an evidence of major importance in firearm-related crimes. The recent introduction of nontoxic ammunition has made impossible the characterization of GSR particles by the current methods employed by forensic experts. To overcome this drawback, the introduction of luminescent...

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Veröffentlicht in:Analytical chemistry (Washington) 2019-10, Vol.91 (19), p.12444-12452
Hauptverfasser: Carneiro, Caroline R, Silva, Carolina S, de Carvalho, Marcela Albino, Pimentel, Maria Fernanda, Talhavini, Márcio, Weber, Ingrid T
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Sprache:eng
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Zusammenfassung:Gunshot residue (GSR) is an evidence of major importance in firearm-related crimes. The recent introduction of nontoxic ammunition has made impossible the characterization of GSR particles by the current methods employed by forensic experts. To overcome this drawback, the introduction of luminescent markers was proposed, allowing on-site visual detection of luminescent gunshot residue (LGSR) at the crime scene. Three different luminescent markers coordinated with europium for specific and selective encoding of ammunition have been proposed. To promote a variety of versatile tools for GSR analysis, spectroscopic techniques combined with chemometric methods can be applied to achieve a reliable, fast, and nondestructive means to identify LGSR and discriminate among the different markers. Luminescence (emission and excitation), normal, and resonance Raman spectroscopies associated with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were evaluated. The classification model using the complementary information on emission and excitation spectra, a.k.a. data fusion, provided a 100% correct classification for all markers. A comprehensive study has been developed to show that the insertion of luminescent markers enables not only the easy localization of GSR residues but also the possibility of ammunition encoding through the use of multivariate classification methods.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.9b03079