Optimization of the separation of organic explosives by capillary electrophoresis with artificial neural networks

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dc.contributor.author Casamento, S
dc.contributor.author Kwok, B
dc.contributor.author Roux, C
dc.contributor.author Dawson, M
dc.contributor.author Doble, P
dc.date.accessioned 2009-12-21T02:28:46Z
dc.date.issued 2003-09
dc.identifier.citation Journal of Forensic Sciences, 2003, 48 (5), pp. 1075 - 1083
dc.identifier.issn 0022-1198
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/3535
dc.description.abstract The separation of 12 explosives by capillary electrophoresis was optimized with the aid of artificial neural networks (ANNs). The selectivity of the separation was manipulated by varying the concentration of sodium dodecyl sulfate (SDS) and the pH of the electrolyte, while maintaining the buffer concentration at 10 mM borate. The concentration of SDS and the electrolyte pH were used as input variables and the mobility of the explosives were used as output variables for the ANN. In total, eight experiments were performed based on a factorial design to train a variety of artificial neural network architectures. A further three experiments were required to train ANN architectures to adequately model the experimental space. A product resolution response surface was constructed based on the predicted mobilities of the best performing ANN. This response surface pointed to two optima; pH 9.0-9.1 and 60-65 mM SDS, and pH 8.4-8.6 and 50-60 mM SDS. Separation of all 12 explosives was achieved at the second optimum. The separation was further improved by changing the capillary to an extended cell detection window and reducing the diameter of the capillary from 75 μm to 50 μm. This provided a more efficient separation without compromising detection sensitivity.
dc.language eng
dc.title Optimization of the separation of organic explosives by capillary electrophoresis with artificial neural networks
dc.type Journal Article
dc.parent Journal of Forensic Sciences
dc.journal.volume 5
dc.journal.volume 48
dc.journal.number 5 en_US
dc.publocation PA USA en_US
dc.identifier.startpage 1075 en_US
dc.identifier.endpage 1083 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 0399 Other Chemical Sciences
dc.personcode 910324
dc.personcode 010494
dc.personcode 960382
dc.percentage 100 en_US
dc.classification.name Other Chemical Sciences en_US
dc.classification.type FOR-08 en_US
dc.description.keywords Artificial neural network
dc.description.keywords Capillary-electrophoresis
dc.description.keywords Explosives
dc.description.keywords Forensic science
dc.description.keywords Optimization
pubs.embargo.period Not known
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 Chemistry and Forensic Science
pubs.organisational-group /University of Technology Sydney/Strength - Forensic Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)

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