Main Article Content
artificial neural network, osmotic dehydration, pumpkin, RSM
In this study, the response surface methodology (RSM) was used to optimise osmo-dehydration of pumpkin cubes. Effect of different parameters including osmotic solution temperature in the range of 5 to 50 °C, the immersion time from 0 to 180 min and the concentration of osmotic solution (from 5% salt + 50% sucrose w/v to 15% salt + 50% sucrose w/v) on water loss (WL), solid gain (SG), weight reduction and final moisture content were investigated by central composite design. The optimum condition for osmotic dehydration was found to be at a temperature of 5 °C, an immersion time of 180 min and an osmotic solution concentration of 15% salt + 50% sucrose w/v. At this optimum condition WL, SG, weight reduction and moisture content were found to be 70.7 g/100 g initial sample, 10.2 g/100 g initial sample, 59.06 g/100 g initial sample and 0.64 g water/g dry matter, respectively. The comparison of the obtained results by artificial neural network and RSM modelling showed that the artificial neural approach has a higher ability in comparison with RSM modelling in predicting final moisture content (R2=0.998 and 0.992, respectively).
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