Document Detail

Data reduction in headspace analysis of blood and urine samples for robust bacterial identification.
MedLine Citation:
PMID:  15975689     Owner:  NLM     Status:  MEDLINE    
This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.
J W T Yates; M J Chappell; J W Gardner; C S Dow; C Dowson; A Hamood; F Bolt; L Beeby
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Computer methods and programs in biomedicine     Volume:  79     ISSN:  0169-2607     ISO Abbreviation:  Comput Methods Programs Biomed     Publication Date:  2005 Sep 
Date Detail:
Created Date:  2005-08-26     Completed Date:  2006-01-23     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8506513     Medline TA:  Comput Methods Programs Biomed     Country:  Ireland    
Other Details:
Languages:  eng     Pagination:  259-71     Citation Subset:  IM    
School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
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MeSH Terms
Bacteremia / microbiology*
Bacteria / classification*
Discriminant Analysis
Mass Spectrometry / methods*
Neural Networks (Computer)
Urine / microbiology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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