Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network

Sohn, Jae Ho and Smith, Rod and Yoong, Ernest and Leis, John W. and Galvin, Geordie (2003) Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network. Biosystems Engineering, 86 (4). pp. 399-410. ISSN 1537-5110


An Artificial Neural Network (ANN) and an electronic nose, AromaScan, were used to predict the piggery odour concentrations emanating from an effluent pond and to develop a confident, rapid, and cost-effective technique for odour measurement. Odour samples from five different piggery effluent ponds were analysed using the AromaScan and dynamic dilution olfactometry. The resulting sensor data were used to train the artificial neural network to correlate the responses to the odour concentrations measured by olfactometry.
Effectiveness was evaluated through simulation with various pre-processing techniques and network architectures. The simulation results have shown that a two-layer back-propagation neural network, which has a tan-sigmoid transfer function in the hidden layer and a linear transfer function in the output layer, could be trained to predict piggery odour concentrations with high value of the correlation coefficient R of 0.984 under the best network performance. The results from the application of scaling and principal component analysis suggest that these techniques are necessary not only to avoid the failure of the network caused by saturation but also to enhance performance. An early stopping technique was shown to provide benefits to the network performance in terms of a decrease in computation time and overfitting. It was found that the optimal number of hidden neurons for the network was 20. Odour concentration of unknown samples were able to be predicted with significant accuracy

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author version not held.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Agricultural, Civil and Environmental Engineering
Date Deposited: 15 Aug 2010 04:31
Last Modified: 13 Jun 2016 23:36
Uncontrolled Keywords: electronic nose; air flow-rate; emission; gas recovery efficiency; liquid effluent; odour; olfactometry
Fields of Research : 09 Engineering > 0999 Other Engineering > 099902 Engineering Instrumentation
09 Engineering > 0999 Other Engineering > 099901 Agricultural Engineering
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9601 Air Quality > 960199 Air Quality not elsewhere classified
Identification Number or DOI: 10.1016/j.biosystemseng.2003.09.003
URI: http://eprints.usq.edu.au/id/eprint/8229

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