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Classification of Mixtures of Odorants from Livestock Buildings by a Sensor Array (an Electronic Tongue)

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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 online 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.

No MeSH data available.


Schematic diagram of back propagation neural network architecture.
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f1-sensors-07-00129: Schematic diagram of back propagation neural network architecture.

Mentions: One of the most widely used artificial neural networks is back propagation artificial neural network (BPNN), which is also called feed forward network. It comprises many processing elements, i.e. nodes, which are arranged in layers: an input layer, an output layer, and one or more layers in between, called hidden layers. A schematic diagram of BPNN with one hidden layer is shown in Fig. 1.


Classification of Mixtures of Odorants from Livestock Buildings by a Sensor Array (an Electronic Tongue)
Schematic diagram of back propagation neural network architecture.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756716&req=5

f1-sensors-07-00129: Schematic diagram of back propagation neural network architecture.
Mentions: One of the most widely used artificial neural networks is back propagation artificial neural network (BPNN), which is also called feed forward network. It comprises many processing elements, i.e. nodes, which are arranged in layers: an input layer, an output layer, and one or more layers in between, called hidden layers. A schematic diagram of BPNN with one hidden layer is shown in Fig. 1.

View Article: PubMed Central

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 online 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.

No MeSH data available.