Comparison of artificial neural network and response surface methodology performance on fermentation parameters optimization of bioconversion of cashew apple juice to gluconic acid
International Journal of Food Engineering
Biochemical Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
The study examined the impact and interactions of cashew apple juice (CAJ) concentration, pH, NaNO<inf>3</inf> concentration, inoculum size and time on gluconic acid (GA) production in a central composite design (CCD). The fermentation process and parameters involved were modeled and optimized using artificial neural network (ANN) and response surface methodology (RSM). The ANN model established the optimum levels as CAJ of 250 g/l, pH of 4.21, NaNO<inf>3</inf> of 1.51 g/l, inoculum size of 2.87% volume and time of 24.41 h with an actual GA of 249.99 g/l. The optimum levels predicted by RSM model for the five independent variables were CAJ of 249 g/l, pH of 4.6, NaNO<inf>3</inf> of 2.29 g/l, inoculum size of 3.95% volume, and time of 38.9 h with an actual GA of 246.34 g/l. The ANN model was superior to the RSM model in predicting GA production. The study demonstrated that CAJ could serve as the sole carbon source for GA production. © 2015 by De Gruyter 2015.
Carbon; Fruit juices; Fruits; Fungi; Models; Neural networks; Optimization; Surface properties; Cashew apple juice; Central composite designs; Fermentation process; Gluconic acids; Independent variables; Parameters optimization; Response surface methodology; Sole carbon source; Fermentation; Anacardium occidentale; Fungi; Malus x domestica