Bacteria classification based on feature extraction from sensor data

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Abstract

Data evaluation and classification have been made on measurements by an electronic nose on the headspace of samples of different types of bacteria growing on petri dishes. The chosen groups were: Escherichia coli, Enterococcus sp., Proteus mirabilis, Pseudomonas aeruginosa, and Staphylococcus saprophytica. An approximation of the response curve by time was made and the parameters in the curve fit were taken as important features of the data set. A classification tree was used to extract the most important features. These features were then used in an artificial neural network for classification. Using the ‘leave-one-out’ method for validating the model, a classification rate of 76% was obtained.

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