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| Research article summary (published 27 Feb 2007): |
Data processing in metabolic fingerprinting by CE-UV: application to urine samples from autistic children.
Full Abstract
Metabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV-Vis detection.
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Author information
Author/s: Soria, Ana C (AC); Wright, Barry (B); Goodall, David M (DM); Wilson, Julie (J);
Affiliation: Department of Chemistry, University of York, York, UK.
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Electrophoresis (Electrophoresis), published in Germany. (Language: eng)
Reference: 2007-Mar; vol 28 (issue 6) : pp 950-64
Dates: Created 2007/05/03; Completed 2007/07/30;
PMID: 17370305, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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