This paper focuses on using response surface methodology (RSM) and artificial neural network (ANN) to optimize the diameter of Gum tragacanth (GT)/poly(vinyl alcohol) (PVA) nanofibers. However, producing curcumin-loaded GT/PVA nanofibers with using these optimized conditions is another aim. RSM methodology based on four variables (voltage, feed rate, distance between nozzle and collector, and solution concentration) with three levels and ANN technique were compared for modeling the average diameter of nanofibers. In the RSM method, the individual and interaction effects between the parameters on the average diameter of nanofibers were determined using Box-Behnken design (BBD). Data sets of input–output patterns were used for training the multilayer perceptron (MP) neural networks trained with back-propagation algorithm for modeling purpose. Experimental results for both ANN and RSM techniques showed agreement with the predicted fiber diameter. High-regression coefficient between the variables and the response displayed that the performance of RSM for minimizing diameter of nanofibers was better than ANN. Based on response surface model, optimum conditions (polymer concentration of 4.2% (w/v), distance between the capillary and collector 20 cm, applied voltage of 20 kV and flow rate of 0.5 mL/h) were obtained for producing GT/PVA nanofibers with minimized diameter.
