APPLICATION OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORK (ANN) FOR ACHIEVING DESIRE BA IN THE BIOTRANSFORMATION OF BENZALDEHYDE USING FREE CELLS OF SACCHAROMYCES CEREVISAE AND THE EFFECT OF Β-CYCLODEXTRIN

This work dwells on the production of benzene alcohol (BA) from the biotransformation of benzaldehyde using free cells of Saccharomyces cerevisae and effects of β-Cyclodextrin. Meanwhile, the properties of BA produced was evaluated. The effects of five variables considered in this research work were evaluated using RSM and ANN. The root mean square error, the coefficient of determination, the adjusted coefficient of determination and the predicted values were used to compare the performance of the RSM and ANN models. The RMSE and R2 of RSM and ANN were 2.00 and 0.0739; 0.9898 and 0.99206, respectively. The R2 adj. and the predicted values of RSM and ANN were found to be 0.98416 and 0.9889 and 327.259 mg/100 ml and 351.50 mg/100 ml. The quality of BA showed that at room temperature, BA was colourless liquid with density 1.030 kg/dm3, the boiling point and refractive index was found to be 204 ± 2 0C and 1.5453, respectively. The results indicated the ANN model to have higher predictive capability than RSM model. Thus, the ANN methodology presents a better alternative than the RSM model. The quality of produced BA was found to be in line with Analytic grade values

Effects of β-Cyclodextrin on Phenyl Methanol Production and Its Optimization

An Artificial Neural Network (ANN) was engaged to optimize the effect of β-Cyclodextrin on the production of Phenyl methanol (PM) from biotransformation of benzaldehyde by free cells of yeast. In developing ANN model,performance of ANN is heavily influenced by its network structure, five-level-five-factors design was applied, which generate 50 experimental runs. The inputs for the ANNs are cell weight (wet. wt): X1, incubation time (min): X2, Acetaldehyde conc. (mg/100 ml): X3, benzaldehyde conc. (mg/100 ml): X4, and β-level (%): X5. The learning algorithms used was QP with MNFF, the transfer function was Tanh. Meanwhile, RMSE was determined to be 3.0739. The coefficient of determination R2 and the adj. R2 were found to be 0.99206 and 0.98419, respectively. It was observed that 900 (mg/100 ml) benzaldehyde with 1000 (μg/100 ml) acetaldehyde in the presence of 1.8% β-cyclodextrin gave the highest yield (351.5 mg/100 ml) of PM. Hence, it can be concluded that yeast (Saccharomyces cerevisae) can tolerate higher levels of acetaldehyde and benzaldehyde due to the effects of β-cyclodextrin

APPLICATION OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORK (ANN) FOR ACHIEVING DESIRE BA IN THE BIOTRANSFORMATION OF BENZALDEHYDE USING FREE CELLS OF SACCHAROMYCES CEREVISAE AND THE EFFECT OF Β-CYCLODEXTRIN

This work dwells on the production of benzene alcohol (BA) from the biotransformation of benzaldehyde using free cells of Saccharomyces cerevisae and effects of β-Cyclodextrin. Meanwhile, the properties of BA produced was evaluated. The effects of five variables considered in this research work were evaluated using RSM and ANN. The root mean square error, the coefficient of determination, the adjusted coefficient of determination and the predicted values were used to compare the performance of the RSM and ANN models. The RMSE and R2 of RSM and ANN were 2.00 and 0.0739; 0.9898 and 0.99206, respectively. The R2 adj. and the predicted values of RSM and ANN were found to be 0.98416 and 0.9889 and 327.259 mg/100 ml and 351.50 mg/100 ml. The quality of BA showed that at room temperature, BA was colourless liquid with density 1.030 kg/dm3, the boiling point and refractive index was found to be 204 ± 2 0C and 1.5453, respectively. The results indicated the ANN model to have higher predictive capability than RSM model. Thus, the ANN methodology presents a better alternative than the RSM model. The quality of produced BA was found to be in line with Analytic grade values