Artificial neural network and multiple linear regression modelling for prediction of moisture content of red beetroots during ultrasound assisted vacuum drying

The drying process is complex because of mass and heat changes. Many factors influence the drying procedure. The present work aimed to evaluate the possibility of artificial neural networks (ANN) and multiple linear regression (MLR) to characterize the moisture ratio of red beetroot slices during ultrasound assisted vacuum drying for multiple inputs. For models, inputs were temperature (T, 50o C), time (t, 0-900 min), weight (w, 13.01±0.25 g), pressure (p, 0.05 MPa) and pretreatment (PR: 0/1) and the moisture ratio (MR) was the output. The ANN models was obtained due to impact of the hidden layer's neuron number and size. MLR model was obtained with stepwise method. As performance criteria of all models, R2 , RMSE, and MAPE values were taken into account. The best ANN model was obtained by three layers (5 inputs, 15 neurons in one hidden layer and 1 output) with RMSE of 0.0117, MAPE of 2.293% and R2 of 0.9996 for all data. The results showed that the ANN model including temperature, time weight, pressure and pretreatment) is a more effective predictive tool since it can yield better outcomes than stepwise MLR model including weight, pressure and pretreatment. The applicability of models to various drying circumstances is restricted because the trials were carried out at a one temperature and vacuum pressure (50 °C and 0.05 MPa). Models with more capacity for generalization should be created by experimenting with various pressure and temperature circumstances. Models can be also enhanced for improved evaluation by utilizing various input combinations.