Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit
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Department of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, 49138-15739 Gorgan, Iran
Food Science Laboratory, Faculty of Sciences and Technology-University of Algarve, Campus Gambelas, Building 8, 8005-139 Faro, Portugal
Young Researchers and Elites Club, Shahre Qods Branch, Islamic Azad University, 37541-374 Shahre Qods, Iran
Young Researchers and Elites Club, Gorgan Branch, Islamic Azad University, Gorgan, Iran
Department of Analytical Chemistry, Urmia Payam Noor University, 57168-38831 Urmia, Iran
Young Researchers and Elite Club, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
Publication date: 2016-05-06
Int. Agrophys. 2016, 30(2): 185–191
Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y1), osmotic solution temperature (y2), and immersion time (y3) were considered as the input parameters and solid gain value (x1) and water loss value (x2) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.