Application of radial basis function network with a Gaussian function of artificial neural networks in osmo-dehydration of plant materials
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Tortoe, C., Orchard, J., Beezer, A. and Tetteh, J. (2011) Application of radial basis function network with a Gaussian function of artificial neural networks in osmo-dehydration of plant materials. Journal of Artificial Intelligence, 4 (4). pp. 233-244. ISSN 1994-5450 (Print), 2077-2173 (Online) (doi:https://doi.org/10.3923/jai.2011.233.244)
Full text not available from this repository.Abstract
The study presents a critical evaluation of Artificial Neural Networks (ANNs) in food processing by successfully predicting the mass transfer in three plant materials. The used of ANNs in osmo-dehydration was evaluated using two varieties of apple (Malus domestica Borkh) of Golden Delicious and Cox, banana cultivar Cavendish and potato (Solanum tuberosum L.) variety Estima. In the ANNs, the radial basis function (RBF) network with a Gaussian function employing the orthogonal least square (OLS) learning method was used. A single hidden layer of few neurones (NHL = 20) resulted in the neural network being limited in its ability to model the process efficiently and the coefficient of determination (R2) was 0.76 for water loss. Increased neurones (NHL = 100) the network was improved significantly (R2 = 0.84) for water loss. Subsequent increase of the neurones to 120 (NHL = 120) showed a significant improvement of the network (R2 = 0.91) for sucrose gain. The mass transfer in the three plant materials were successfully predicted by the ANN models indicating the ability of ANN to model both linear and non-linear models as an advantage over empirical equations for quality predictions in food processing.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural networks, mass transfer, osmotic dehydration, multilinear regression, apple, banana |
| Subjects: | Q Science > Q Science (General) S Agriculture > S Agriculture (General) |
| Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department Faculty of Engineering & Science > School of Science (SCI) |
| Related URLs: | |
| Last Modified: | 17 Oct 2016 09:11 |
| URI: | http://gala.gre.ac.uk/id/eprint/7600 |
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