As a cost saving drying technology, osmotic dehydration is not receiving much attention in the food industry due to the poor understanding of the counter current flow phenomena associated with it. Therefore, it is very important to investigate the underlying principles of the counter current flow to improve industrial implementation of the technology. Osmotic dehydration experiments had been reported plant and animal materials. Minimal improvement on amount and rate of water loss and corresponding solid gain had been reported in the presence of sodium chloride and agitation especially for the first thirty minutes of osmotic dehydration. Simulation of cell membrane using artificial cell had showed that the presence of starch in food materials retards the diffusion of water. A multilinear regression (MLR) model had been developed for water loss and solid gain during osmotic dehydration of the plant and animal materials. These models took into account the effect of temperature, concentration, time of immersion, sample size, sample type and agitation. Temperature was the most important factor whereas agitation was the least. Artificial neural networks (ANNs) (using the radial basis function (RBF) network with a Gaussian function) had been used successfully to model osmotic dehydration. When predictions of experimental data from MRL and ANN were compared, better agreement was found for ANN models than MLR models. A new method, thermocalorimetry, was developed to study osmotic dehydration. Scanning Electron Microscopy (SEM) micrographs revealed that osmotic treatment has a significant effect on the structural properties (cell wall and middle lamella) for the different plant materials. These successfully reports buttress the need for employment of osmotic dehydration in food industries.
Key words: Osmodehydration, multilinear regression, artificial neural networks, radial basis function, scanning electron microscopy, visking osmometer.
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