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.