Comparison between Artificial Neural Network model and mathematical models for drying kinetics of osmotically dehydrated and fresh figs under open sun drying

Abstract

AbstractIn this study, drying kinetics of figs (Ficus carica L) under open sun drying (OSD) was compared with using Artificial Neural Network (ANN) model and mathematical models. Pulsed vacuum osmotic dehydration (PVOD) was applied as a pretreatment. In experiments, whole figs (Sarılop variety) were immersed in sucrose solution at 50°Brix and 50 °C with a ratio of solution/food was 4:1 for 180 min. Vacuum impregnation was applied at 253 mbar for 15 min. Shrinkage effect was considered, also. Results showed that PVOD reduced drying time of figs. The mean of Deff values of PVOD treated and fresh figs were obtained as 2.55 × 10−10 m2/s and 2.36 × 10−10 m2/s, respectively. It was found that present model was the best drying model with the highest correlation coefficient (R2) values among the mathematical models whereas ANN model had a higher correlation coefficient (R2 = 0.9999) values than the mathematical models for both PVOD treated and fresh figs.Practical applicationsDried figs have an economic income for Turkey and Turkey is the biggest dried fig producer and exporter in the World. Drying of figs is very important phenomena because figs have a short harvesting time. Figs are widely dried under open sun because this method has low operating cost. OD method is used before drying step and provides reducing drying time. This study presents experimental results of osmotically dehydrated and fresh figs under open sun drying. Drying kinetics of figs were modeled with empirical and semiempirical models under shrinkage consideration. Shrinkage of fresh and osmotically dehydrated figs during the drying were mathematically modeled thus these correlations can be helpful for researchers. Also, a new mathematical model was established and this model had more accuracy than other mathematical models. The ANN model was used for the prediction of drying curves of figs in different operating conditions.