Prediction of Physicochemical Properties of Raspberry Dried by Microwave-Assisted Fluidized Bed Dryer Using Artificial Neural Network

Abstract

The main target of this research is to dry raspberries in a microwave-assisted fluidized bed dryer. Artificial neural network (ANN) modeling was used in order to evaluate and predict the physicochemical properties of this fruit. In this research, the effects of five variables—microwave power (0, 300, and 600 W), temperature (55, 70, and 85°C), air flow rate (15, 20, and 25 m/s), starting time of microwave input (from the moment when the moisture content decreased until 334, 400, and 466 g water/g dry matter), and amount of loaded material (50, 100, and 150 g)—on nine outputs—drying time, rehydration capacity, density, porosity, hardness, water activity, phenolic compounds content, anthocyanins content, and the antioxidant activity of dried raspberries—were studied. A feed-forward multilayered perceptron trained by back-propagation algorithms for five independent variables was developed to predict these nine outputs. The optimal configuration of the neural network model had a hidden layer with nine neurons. The predictive ability of the ANN was compared using a separate data set of 52 unseen experiments based on root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2) for each output parameter. The optimum model was able to predict the nine output parameters with a coefficient of determination higher than 0.92. The results indicated that the experimental and ANN-predicted data sets were in good agreement, so it is feasible to use an ANN to predict the physicochemical properties of dried black raspberry.