Predicting of osmotic dehydration kinetics of pumpkin by means of intelligent artificial neural network in static situation

Pumpkin samples were osmotically dehydrated through three different salt concentrations (5 % w/w, 10% w/w and 15% w/w and 50% w/w) sucrose solution at 25°c and 50°c. Osmotic solution - fruit weight ratio was 1:20. Artificial neural network was used for estimating water loss and solid gain values undergoing osmotic dehydration. Perceptron artificial neural network with gradient descent optimization algorithm was utilized by hyperbolic tangent function for outcomes prediction. The results indicated that artificial neural network with one hidden layer depicted the best fitting when the hidden layer had 10 and 18 neurons for solid gain and water loss respectively. Also, it was found that artificial neural network with 6-6 and 22-22 neurons in two hidden layers stated the optimum outcomes to approximate water loss and solid gain, respectively.