An artificial neural network for predicting the physiochemical properties of fish oil microcapsules obtained by spray drying

The aim of this work was to develop an artificial neural network (ANN) to predict the physiochemical properties of fish oil microcapsules obtained by spray drying method. The relation amongst inlet-drying air temperature, outlet-drying air temperature, aspirator rate, peristaltic pump rate, and spraying air flow rate with 5 performance indices, namely capsules' residual moisture content, particle size, bulk density, encapsulation efficiency, and peroxide value was bridged by using ANN. A multilayer perceptron ANN was developed to predict the performance indices based on the input variables. The optimal ANN model was found to be a 5-10-5 structure with tangent sigmoid transfer function, Levenberg-Marquardt error minimization algorithm, and 1,000 training epochs. This optimal network was capable to predict the outputs with R² values higher than 0.87. It was concluded that ANN is a useful tool to investigate, approximate, and predict the encapsulation characteristics of fish oil.