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Title: Classification of mixtures of odorants from livestock buildings by a sensor array (an electronic tongue)
Authors: Abu-Khalaf, Nawaf
Iversen, Jens Jørgen Lonsmann
Keywords: electronic tongue; odorants; classification; back propagation artificial neural network (BPNN); average classification rate (ACR);
Issue Date: 2007
Publisher: Sensors
Citation: Abu-Khalaf, N., & Iversen, J. J. (2007). Classification of mixtures of odorants from livestock buildings by a sensor array (an electronic tongue). Sensors, 7(1), 129-143.
Abstract: Abstract An electronic tongue comprising different numbers of electrodes was able to classify test mixtures of key odorants characteristic of bioscrubbers of livestock buildings (n-butyrate, iso-valerate, phenolate, p-cresolate, skatole and ammonium). The classification of model solutions indicates that the electronic tongue has a promising potential as an on- line sensor for characterization of odorants in livestock buildings. Back propagation artificial neural network was used for classification. The average classification rate was above 80% in all cases. A limited, but sufficient number of electrodes were selected by average classification rate and relative entropy. The sufficient number of electrodes decreased standard deviation and relative standard deviation compared to the full electrode array.
Appears in Collections:Sciences and Agricultural Technology Faculty

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