Please use this identifier to cite or link to this item: https://csirspace.foodresearchgh.site/handle/123456789/432
Title: Artifical neural networks in modeling osmotic dehydration of foods
Authors: Tortoe, C.
Orchard, J.
Beezer, J.
Tetteh, J.
Keywords: Osmotic dehydration;Foods;Neural network;Arficial neural network
Issue Date: 2008
Publisher: Blackwell Publishing
Citation: Journal Of Food Processing And Preservation, 32, 270-285
Abstract: 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
URI: https://csirspace.foodresearchgh.site/handle/123456789/432
ISSN: 0145-8892
Journal Name: Journal Of Food Processing And Preservation
Appears in Collections:Food Research Institute

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