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Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children
Author(s) -
Soria Ana C.,
Wright Barry,
Goodall David M.,
Wilson Julie
Publication year - 2007
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200600381
Subject(s) - electropherogram , preprocessor , computer science , pattern recognition (psychology) , autism , urine , artificial intelligence , chromatography , chemistry , psychology , developmental psychology , capillary electrophoresis , biochemistry
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.