Prediction by Artificial Neural Networks (ANN) of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius)

Abstract

The predictive ability of Artificial Neural Network (ANN) on the effect of the concentration (30, 40, 50 y 60 % w/w) and temperature (30, 40 y 50°C) of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95).

Keywords

• : Artificial Neural Networks (ANN)
• effective diffusivity
• yacon
• osmotic dehydration