Document Detail

Fast identification of ten clinically important micro-organisms using an electronic nose.
MedLine Citation:
PMID:  16441375     Owner:  NLM     Status:  MEDLINE    
AIMS: To evaluate the electronic nose (EN) as method for the identification of ten clinically important micro-organisms. METHODS AND RESULTS: A commercial EN system with a series of ten metal oxide sensors was used to characterize the headspace of the cultured organisms. The measurement procedure was optimized to obtain reproducible results. Artificial neural networks (ANNs) and a k-nearest neighbour (k-NN) algorithm in combination with a feature selection technique were used as pattern recognition tools. Hundred percent correct identification can be achieved by EN technology, provided that sufficient attention is paid to data handling. CONCLUSIONS: Even for a set containing a number of closely related species in addition to four unrelated organisms, an EN is capable of 100% correct identification. SIGNIFICANCE AND IMPACT OF THE STUDY: The time between isolation and identification of the sample can be dramatically reduced to 17 h.
M Moens; A Smet; B Naudts; J Verhoeven; M Ieven; P Jorens; H J Geise; F Blockhuys
Related Documents :
17760585 - A hybrid artificial neural network-numerical model for ground water problems.
15133955 - An optimization approach to recognition of epileptogenic data using neural networks wit...
16245245 - Predicting intradialytic hypotension from experience, statistical models and artificial...
15670285 - Comparison of standard and artificial neural network estimators of hemodialysis adequacy.
19069705 - Evidence based treatment of hemorrhoids.
20209105 - Body size evolution in insular speckled rattlesnakes (viperidae: crotalus mitchellii).
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Letters in applied microbiology     Volume:  42     ISSN:  0266-8254     ISO Abbreviation:  Lett. Appl. Microbiol.     Publication Date:  2006 Feb 
Date Detail:
Created Date:  2006-01-30     Completed Date:  2006-05-25     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8510094     Medline TA:  Lett Appl Microbiol     Country:  England    
Other Details:
Languages:  eng     Pagination:  121-6     Citation Subset:  IM    
Department of Chemistry, University of Antwerp, Wilrijk, Belgium.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Bacteria / growth & development,  isolation & purification*
Bacterial Typing Techniques / methods*
Electronics / methods,  standards
Neural Networks (Computer)*
Reagent Kits, Diagnostic
Sensitivity and Specificity
Reg. No./Substance:
0/Reagent Kits, Diagnostic

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

Previous Document:  Evaluation of Colilert-18 for the detection of coliforms and Escherichia coli in tropical fresh wate...
Next Document:  Quantitative detection of Streptococcus mutans in the dental plaque of Japanese preschool children b...