Microencapsulation of Dragon Fruit Peel Extract by Freeze-Drying Using Hydrocolloids: Optimization by Hybrid Artificial Neural Network and Genetic Algorithm

Abstract

The freeze-drying encapsulation process was used to encapsulate dragon fruit peel extract with three distinct wall materials: maltodextrin, gum arabica, and gelatin. The process was modeled using a feed forward back propagation artificial neural network with four, eleven, and four neurons in the input, hidden, and output layers. Three of the four input neurons were concentrations of wall material (g), while the fourth was ultrasonication power. The four output neurons were encapsulation efficiency, antioxidant activity, hygroscopicity, and solubility of freeze-dried encapsulation powder. The procedure was optimized using hybrid artificial neural network (ANN) and genetic algorithm (GA) approach. The optimal wall material composition for encapsulation obtained by the integrated ANN and GA was 4.461 g maltodextrin, 3.863 g gum arabic, and 3.198 g gelatin. The optimal ultrasonication power for achieving a homogenous mixture was determined to be 123 W. At the optimal condition, the predicted values for the responses encapsulation efficiency, antioxidant activity, hygroscopicity, and solubility were found to be 88.143%, 81.702%, 6.924 g/100 g, and 32.841%, respectively. Under optimal conditions, the relative deviation between the predicted model and experimental outcomes was less than 2.077%. The thermal stability of the encapsulated powder followed the first order kinetic modeling. The results showed that the sample treated at pH of 7 was more thermally stable at 80 °C than the sample treated at pH of 3.6. The half-life time was found to be 140 min and 103 min for the sample treated at pH of 7 and 3.6, respectively.