Comparative Study of Response Surface Methodology and Artificial Neural Network for Modeling and Optimization of Extraction Process Parameters on Tetrapleura Tetraptera

Bioactive compounds in the fruits of tetrapleura tetraptera is widely used in food as a flavouring agent and for spices. in this study, bioactive compounds were extracted by solid-liquid extraction process and the yield was optimized by response surface methodology (rsm) and artificial neural network (ann). the process parameters optimized were the extraction temperature, particle size and extraction time. box-behnken design was used to study the effect of the process parameters on the extract yield. a quadratic model was obtained by rsm which was used to predict the extract yield. while for ann, bayesian regularization learning algorithm with hyperbolic function (tanh) for both hidden and output layers was the best model for predicting the extract yield. the performance of both models was established based on their r^2 and rmse values. (r^2 and rmse values were 0.9391 and 3.10 for rsm and 0.9637 and 0.8193 for ann respectively). ann gave the maximum extract yield of 29.15 % higher than that of rsm which evaluated a yield of 27.70 % with optimum conditions at extraction temperature of 90℃, particle size of 3.26 mm and extraction time of 50 mins. it was therefore concluded that ann is better than rsm in the modeling and optimization of the extraction process parameters.