Abstract

This study investigated the stimulatory effect of plant oils (castor oil and olive oil) and surfactants (tween 20 and tween 80) on citric acid production from cassava bagasse using Aspergillus niger. The fermentation process was designed using Box-Behnken design while the effect of the oils and surfactants was optimised using response surface methodology (RSM) and artificial neural network (ANN). RSM analysis yielded a statistically significant quadratic model (p<0.05) which was used to predict an optimal citric acid concentration of 4.82 g/L at a castor oil concentration of 3% w/w, olive oil concentration of 2.45% w/w, tween 20 concentration of 1.5% w/w and tween 80 concentration of 0.8% w/w. ANN analysis showed that a multilayer full feed forward (MFFF) network with quick propagation (QP) and hyperbolic tangent transfer function (Tanh) yielded the best model for predicting citric acid production. The optimal ANN model predicted a citric acid concentration of 4.76 g/l at a castor oil concentration of 3% w/w, olive oil concentration of 1.84% w/w, tween 20 concentration of 1.5% w/w and tween 80 concentration of 0.67% w/w. The oils and surfactants were beneficial to citric acid production with both enhancing citric acid production by 23.7 and 9.8%, respectively. The predictive capacity of the RSM and ANN models was assessed based on their respective coefficient of determination (R2) and root mean square error (RMSE) values. These values were obtained as 1.00 and 0.005 for ANN and 0.99 and 0.018 for RSM, respectively. The higher R2 value and lower RMSE value of the ANN model shows that it is a better predictive tool compared to RSM.