Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Data reduction in headspace analysis of blood and urine samples for robust bacterial identification

Tools
- Tools
+ Tools

Yates, J. W. T., Chappella, M. J., Gardner, J. W., Dow, Crawford S., Dowson, C., Hammood, A., Bolt, F. and Beeby, L. (2005) Data reduction in headspace analysis of blood and urine samples for robust bacterial identification. Computer Methods and Programs in Biomedicine, 79 (3). pp. 259-271. doi:10.1016/j.cmpb.2005.04.003 ISSN 0169-2607.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Official URL: http://dx.doi.org/10.1016/j.cmpb.2005.04.003

Request Changes to record.

Abstract

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 radia( 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. (c) 2005 Elsevier Ireland Ltd. All rights reserved

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Journal or Publication Title: Computer Methods and Programs in Biomedicine
Publisher: Elsevier Ireland Ltd.
ISSN: 0169-2607
Official Date: September 2005
Dates:
DateEvent
September 2005Published
Volume: 79
Number: 3
Number of Pages: 13
Page Range: pp. 259-271
DOI: 10.1016/j.cmpb.2005.04.003
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us