Development of a UHPLC method for the detection of organic gunshot residues using artificial neural networks

Taudte, RV; Roux, C; Bishop, D; Blanes, L; Doble, P; Beavis, A

Development of a UHPLC method for the detection of organic gunshot residues using artificial neural networks

Taudte, RV

Roux, C

Bishop, D

Blanes, L Doble, P

Beavis, A

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Publication Type:: Journal Article
Citation:: Analytical Methods, 2015, 7 (18), pp. 7447 - 7454
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Taudte, RV https://orcid.org/0000-0003-4617-6063	en_US
dc.contributor.author	Roux, C https://orcid.org/0000-0003-3610-420X	en_US
dc.contributor.author	Bishop, D https://orcid.org/0000-0002-6533-4410	en_US
dc.contributor.author	Blanes, L	en_US
dc.contributor.author	Doble, P https://orcid.org/0000-0002-8472-1301	en_US
dc.contributor.author	Beavis, A https://orcid.org/0000-0001-6259-7699	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Analytical Methods, 2015, 7 (18), pp. 7447 - 7454	en_US
dc.identifier.issn	1759-9660	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36897
dc.description.abstract	© The Royal Society of Chemistry. The introduction of lead and heavy-metal free ammunition to the market challenges the current protocol for gunshot residue (GSR) investigations, which focuses on the inorganic components. Future proofing GSR analysis requires the development and implementation of new methods for the collection and analysis of organic GSR (OGSR) into operational protocols. This paper describes the development and optimisation of an ultra high performance liquid chromatography method for the analysis of 32 compounds potentially present in OGSR. An artificial neural network was applied to predict the retention times of the target analytes for various gradients for rapid determination of optimum separation conditions. The final separation and analysis time for the 32 target analytes was 27 minutes with limits of detection ranging from 0.03 to 0.21 ng. The method was applied to the analysis of smokeless powder and samples collected from the hands of a shooter following the discharge of a firearm. The results demonstrate that the method has the potential for use in cases involving GSR.	en_US
dc.relation.ispartof	Analytical Methods	en_US
dc.relation.isbasedon	10.1039/c5ay00306g	en_US
dc.title	Development of a UHPLC method for the detection of organic gunshot residues using artificial neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	18	en_US
utslib.citation.volume	7	en_US
utslib.for	0301 Analytical Chemistry	en_US
utslib.for	0399 Other Chemical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
utslib.copyright.status	open_access
pubs.issue	18	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© The Royal Society of Chemistry. The introduction of lead and heavy-metal free ammunition to the market challenges the current protocol for gunshot residue (GSR) investigations, which focuses on the inorganic components. Future proofing GSR analysis requires the development and implementation of new methods for the collection and analysis of organic GSR (OGSR) into operational protocols. This paper describes the development and optimisation of an ultra high performance liquid chromatography method for the analysis of 32 compounds potentially present in OGSR. An artificial neural network was applied to predict the retention times of the target analytes for various gradients for rapid determination of optimum separation conditions. The final separation and analysis time for the 32 target analytes was 27 minutes with limits of detection ranging from 0.03 to 0.21 ng. The method was applied to the analysis of smokeless powder and samples collected from the hands of a shooter following the discharge of a firearm. The results demonstrate that the method has the potential for use in cases involving GSR.

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http://hdl.handle.net/10453/36897