Artificial neural networks in modelling osmotic dehydration of foods
Tortoe, Charles, Orchard, John, Beezer, Anthony and Tetteh, John (2008) Artificial neural networks in modelling osmotic dehydration of foods. Journal of Food Processing and Preservation, 32 (2). pp. 270-285. ISSN 1745-4549 (online) (doi:10.1111/j.1745-4549.2008.00178.x)Full text not available from this repository.
Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression
coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox
apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.
|Uncontrolled Keywords:||artificial neural networks, osmotic dehydration|
|Subjects:||Q Science > QD Chemistry
S Agriculture > S Agriculture (General)
|School / Department / Research Groups:||Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
School of Science
Faculty of Engineering & Science > School of Science
School of Science > Department of Pharmaceutical, Chemical & Environmental Sciences
Faculty of Engineering & Science > School of Science > Department of Pharmaceutical, Chemical & Environmental Sciences
|Last Modified:||19 Apr 2012 16:36|
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