Artifical neural networks in modeling osmotic dehydration of foods

Tortoe, C., Orchard, J., Beezer, J., Tetteh, J.

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.

English

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