Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit

Abstract

The objectives of this study were to use image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit slices. Kiwifruits were dehydrated implementing four different sucrose concentrations, at three processing temperatures and during four osmotic time periods. A multilayer neural network was developed by using the operation conditions as inputs to estimate water loss, solid gain, and color changes. It was found that artificial neural network with 16 neurons in hidden layer gives the best fitting with the experimental data, which made it possible to predict solid gain, water loss, and color changes with acceptable mean-squared errors (1.005, 2.312, and 2.137, respectively). These results show that artificial neural network could potentially be used to estimate mass transfer kinetics and color changes of dehydrated kiwifruit.