Appraisal of Artificial Neural Network and Response Surface Methodology in Modeling and Process Variable Optimization of Oxalic Acid Production from Cashew Apple Juice: A Case of Surface Fermentation

Abstract

This study assessed the effects and interactions of cashew apple juice (CAJ) concentration, pH, time, methanol concentration, and NaNO3 concentration on oxalic acid fermentation in a central composite design. The efficacies of artificial neural network (ANN) and response surface methodology (RSM) in modeling and optimizing the process were evaluated using correlation coefficient (R), coefficient of determination (R2), and absolute average deviation (AAD). The highest oxalic acid production observed was 120.66 g/L under optimum values of a CAJ concentration of 291 g/L, pH of 6.9, time of 10.82 days, methanol concentration of 2.91% (v/v), and NaNO3 concentration of 1.05 g/L that were numerically predicted by the developed RSM quadratic model. Using the developed ANN model coupled with rotation inherit optimization, the highest oxalic acid production observed was 286.75 g/L under the following optimum values: CAJ of 291 g/L, pH of 6.5, time of 12.64 days, methanol concentration of 3.82% (v/v), and NaNO3 concentration of 2.41 g/L. The results showed that the ANN model (R = 0.9996, R2 = 0.9999, AAD = 0.21%) was better than the RSM model (R = 0.9986, R2 = 0.9973, AAD = 1.00%) for optimizing oxalic acid fermentation. The use of the ANN model led to a 2.4-fold increase in oxalic acid yield over the RSM model.

Keywords

• Cashew apple juice
• Fungi
• Modeling
• Central composite design
• Oxalic acid