title: Fast identification of ten clinically important micro-organisms using an electronic nose.
abstract: OBJECTIVE
  To evaluate the electronic nose (EN) as method for the identification of ten clinically important micro-organisms.
  
  
  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.
  
  
  CONCLUSIONS
  The time between isolation and identification of the sample can be dramatically reduced to 17 h.
authors.affiliation: Department of Chemistry, University of Antwerp, Wilrijk, Belgium.
authors.forename: M
authors.initials: M
authors.lastname: Moens
authors.forename: A
authors.initials: A
authors.lastname: Smet
authors.forename: B
authors.initials: B
authors.lastname: Naudts
authors.forename: J
authors.initials: J
authors.lastname: Verhoeven
authors.forename: M
authors.initials: M
authors.lastname: Ieven
authors.forename: P
authors.initials: P
authors.lastname: Jorens
authors.forename: H J
authors.initials: HJ
authors.lastname: Geise
authors.forename: F
authors.initials: F
authors.lastname: Blockhuys
mesh_terms: D001419:Bacteria / Q000254:growth & development / Q000302:isolation & purification*; D015373:Bacterial Typing Techniques / Q000379:methods*; D004581:Electronics / Q000379:methods / Q000592:standards; D006801:Humans; D016571:Neural Networks, Computer; D011933:Reagent Kits, Diagnostic; D012680:Sensitivity and Specificity
chemical_list: D011933:Reagent Kits, Diagnostic