Skip navigation

Artificial neural networks in modelling osmotic dehydration of foods

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.

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.

Item Type: Article
Uncontrolled Keywords: artificial neural networks, osmotic dehydration
Subjects: Q Science > QD Chemistry
S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science > Department of Pharmaceutical, Chemical & Environmental Sciences
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
Related URLs:
Last Modified: 09 Dec 2016 14:26
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/2166

Actions (login required)

View Item View Item