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Application of Radial Basis Function Network with a Gaussian Function of Artificial Neural Networks in Osmo-dehydration of Plant Materials

Author(s): C. Tortoe | J. Orchard | A. Beezer | J. Tetteh

Journal: Journal of Artificial Intelligence
ISSN 1994-5450

Volume: 4;
Issue: 4;
Start page: 233;
Date: 2011;
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Keywords: osmotic dehydration | multilinear regression | apple | Artificial neural networks | banana | mass transfer

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.

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