Effect of ANN and semi-empirical models on dried Annona muricata leaves

Annona muricata leaves were dried using tray dryer at various temperatures (40 °C, 50 °C, and 60 °C) to evaluate the efficacy of drying and its easy utilization in food fortification and health supplement. Eight mathematical models were used to define the drying data and Page model showed the best fit. ANN model was utilized to adopt the network for the drying kinetics. Feed forward backpropagation network and Levenberg–marquardt’s training algorithm along with TANSIGMOID transfer function gave the best result for estimation of moisture content and moisture ratio. Furthermore, correlative study on ANN and semi-empirical model was performed. A drying temperature of 60 °C gave significant results for the mass transfer parameters. L*, a*, b*, hue, and chrome values were observed. Micro structural effects based on the drying conditions were also examined. Hence, 60 °C showed greater drying rate, lesser time, sustainable chlorophyll, and color value, and thereby selected for the drying of A. muricata leaves.