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Fast identification of ten clinically important micro‐organisms using an electronic nose
Author(s) -
Moens M.,
Smet A.,
Naudts B.,
Verhoeven J.,
Ieven M.,
Jorens P.,
Geise H.J.,
Blockhuys F.
Publication year - 2006
Publication title -
letters in applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.698
H-Index - 110
eISSN - 1472-765X
pISSN - 0266-8254
DOI - 10.1111/j.1472-765x.2005.01822.x
Subject(s) - electronic nose , identification (biology) , pattern recognition (psychology) , artificial neural network , isolation (microbiology) , artificial intelligence , computer science , set (abstract data type) , selection (genetic algorithm) , feature selection , data set , data mining , biological system , biology , bioinformatics , ecology , programming language
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.