Abstract
The effects were investigated of edible coating and drying temperature (50, 65 and 80 °C) on the properties of dehydrated pineapple cubes. A comparative study was performed using mathematical models, multiple linear regression (MLR) and artificial neural networks (ANNs) to predict moisture ratio ( MR ) and drying rate ( DR ). Kinetic drying, effective moisture diffusion ( D eff ) and activation energy were examined. Midilli et al. model was the best fit to predict MR . Higher air temperature reduced drying time and moisture content, while D eff increased for uncoated and coated dried pineapples of 1.69 × 10 −9 to 5.57 × 10 −9 m 2 /s and 1.60 × 10 −9 to 5.95 × 10 −9 m 2 /s with the activation energy ( E a ) of 37.68 and 41.61 kJ/mol, respectively. Interestingly, the edible coating did not significantly affect D eff and E a , but it retained ascorbic acid. Moreover, ANNs model was appropriate for the prediction of the MR and DR of dehydrated pineapple cubes, as this model had the highest R 2 and accuracy with the lowest RMSE and MAE. The ANNs models with topology of 3–14-14-1 for MR and 3-7-7-1 for DR predictions were the optimal to estimate the drying process of uncoated and coated fruits with satisfactory accuracy and benefit for food industry.
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