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

An electronic nose system for monitoring the quality of potable water

Tools
- Tools
+ Tools

Gardner, J. W., Shin, Hyun Woo, Hines, Evor and Dow, Crawford S. (2000) An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B: Chemical, 69 (3). pp. 336-341. doi:10.1016/S0925-4005(00)00482-2 ISSN 0925-4005.

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/S0925-4005(00)00482-2

Request Changes to record.

Abstract

A measurement system has been developed for the testing of cyanobacteria in water, and it consists of three main stages: the odour sampling system, an electronic nose (e-nose) and a CellFacts instrument that analyses liquid samples. The e-nose system, which employs an array of six commercial odour sensors, has been used to monitor not only different strains but also the growth phase of cyanobacteria (i.e. blue-green algae) in water over a 40-day period. Principal components analysis (PCA), multi-layer perceptron (MLP), learning vector quantisation (LVQ) and Fuzzy ARTMAP were used to analyse the response of the sensors. The optimal MLP network was found to classify correctly 97.1% of the unknown nontoxic and 100% of the unknown toxic cyanobacteria. The optimal LVQ and Fuzzy ARTMAP algorithms were able to classify 100% of both strains of cyanobacteria samples. The accuracy of MLP, LVQ and Fuzzy ARTMAP in terms of predicting four different growth phases of toxic cyanobacteria was 92.3%, 95.1% and 92.3%, respectively. These results show the potential application of neural network based e-noses to test the quality of potable water as an alternative to instruments, such as liquid chromatography or optical microscopy.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Journal or Publication Title: Sensors and Actuators B: Chemical
Publisher: Elsevier Science SA
ISSN: 0925-4005
Official Date: 25 October 2000
Dates:
DateEvent
25 October 2000Published
Volume: 69
Number: 3
Page Range: pp. 336-341
DOI: 10.1016/S0925-4005(00)00482-2
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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