Optimisation Of The Separation Of Amino Acids By Capillary Electrophoresis Using Artificial Neural Networks

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dc.contributor.author Van Gramberg, Amanda en_US
dc.contributor.author Beavis, Alison en_US
dc.contributor.author Blanes, Lucas en_US
dc.contributor.author Doble, Philip en_US
dc.contributor.editor Grady Hanrahan and Frank A. Gomez en_US
dc.date.accessioned 2012-02-02T03:16:31Z
dc.date.available 2012-02-02T03:16:31Z
dc.date.issued 2010 en_US
dc.identifier 2010002344 en_US
dc.identifier.citation Van Gramberg Amanda et al. 2010, 'Optimisation Of The Separation Of Amino Acids By Capillary Electrophoresis Using Artificial Neural Networks', in NA (ed.), John Wiley & Sons, Inc, United States, pp. 169-180. en_US
dc.identifier.issn 978-0-470-39329-1 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/14390
dc.description.abstract Many factors can affect the separation performance of a capillary electrophoresis (CE) electrolyte, such as the buffer, surfactant and organic modifier concentrations, pH, capillary temperature, and applied voltage (1). The efficient manipulation of these factors is critical to optimize the resolution of a given analysis in the shortest time frame. During the method development process, an analyst will usually attempt a separation based on a previously reported method that is similar or the same as the requirements of the analysis at hand. If the separation is inadequate, a univariate approach (2) is often employed to attempt to improve the separation. This involves altering one parameter at a time in a systematic way, and viewing the results by plotting the effect of the parameter on the migration time of the analytes. In this way, suitable electrolyte compositions may be found that separates all of the analytes. If suitable conditions are not found, a second electrolyte parameter is chosen and altered in a similar manner. This univariate procedure is then repeated until a suitable condition is found. This method of optimization is time-consuming, and it is unknown if the optimum is truly the global optimum. en_US
dc.language en_US
dc.publisher John Wiley & Sons, Inc en_US
dc.relation.isbasedon NA en_US
dc.title Optimisation Of The Separation Of Amino Acids By Capillary Electrophoresis Using Artificial Neural Networks en_US
dc.parent Chemometric Methods in Capillary Electrophoresis en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation United States en_US
dc.identifier.startpage 169 en_US
dc.identifier.endpage 180 en_US
dc.cauo.name SCI.Chemistry and Forensic Sciences en_US
dc.conference Verified OK en_US
dc.for 170205 en_US
dc.personcode 10317170 en_US
dc.personcode 990445 en_US
dc.personcode 105015 en_US
dc.personcode 010494 en_US
dc.percentage 100 en_US
dc.classification.name Neurocognitive Patterns and Neural Networks en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 010494 en_US

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