In this study, a comparative optimization of biotransformation of benzaldehyde to L-Phenylacetylcarbinol via free cells of Saccharomyces cerevisae using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) was done. A polynomial regression model was developed and RSM optimum process was determined. In developing ANN model, performance of ANN is heavily influenced by its network structure, five-level-five-factors design was applied, which generated 50 experimental runs from CCD design of RSM. The inputs for the ANN were cell mass (wet. wt), incubation duration (min), concentration of acetaldehyde (mg/100 ml), concentration of benzaldehyde (mg/100 ml), and β-cyclodextrin level (%): X5. The learning algorithms used was QP with MNFF and the transfer function was Tanh. The RMSE, R2, AAD and predicted values were used to compare the performance of the RSM and ANN models. The extrapolative fitness of ANN model was found to be higher than RSM extrapolative fitness model. Thus, it can be concluded that even though RSM is mostly used method for experimental optimization, the ANN methodology present a better alternative.
This study contributes in the existing literature to science and engineering. This study uses new estimation methodology for the conversion of benzaldehyde to L-PAC. This study originates new formula to enhance the concentration of L-PAC. This study is one of very few studies which have investigated the use of β-CD to improve the L-PAC formation. The paper contributes the first logical analysis in modeling and optimization of L-PAC formation. The paper’s primary contribution is finding that L-PAC production can be enhanced using statistical software. This study documents the superiority of artificial neural network over response surface methodology.
T. F. Adepoju, S. K. Layokun, O. J. Ojediran, and C. Okolie, "An innovative approach to biotransformation of benzaldehyde to L-PAC via free cells of saccharomyces cerevisae in the presence of ?-cyclodextrin," International Journal of Science and Engineering Research, vol. 12, pp. 372-385, 2013.
J. Neuberg and L. Libermann, "Zur kenntnis der carboligase II muteilung," Biochemische Zeitschnuff, vol. 127, pp. 327-339, 1921.
D. Bas and I. H. Boyaci, "Modeling and optimization I: Usability of response surface methodology," Journal of Food Engineering, vol. 78, pp. 836-845, 2007a.
S. Seraman, A. Rajendran, and V. Thangavelu, "Statistical optimization of anticholesterolemic drug lovastatin production by the red mold monascus purpureus," Food and Bioproducts Processing, vol. 88, pp. 266-276, 2010.
S. J. Kalil, F. Maugeri, and M. I. Rodrigues, "Response surface analysis and simulation as a tool for bioprocess design and optimization," Process Biochem., vol. 35, pp. 539–550, 2000.
S. B. Imandi, V. V. R. Bandaru, S. R. Somalanka, S. R. Bandaru, and H. R. Garapati, "Application of statistical experimental designs for the optimization of medium constituents for the production of citric acid from pineapple waste," Bioresource Technology, vol. 99, pp. 4445-4450, 2008.
M. Basri, R. N. Z. R. A. Rahman, A. Ebrahimpour, A. B. Salleh, E. Ryantin Gunawan, and M. Basyaruddin, "Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester," BMC Biotechnology, vol. 7, p. 53, 2007.
E. G. Kana, J. K. Oloke, A. Lateef, and A. Oyebanji, "Comparative evaluation of artificial neural network coupled genetic algorithm and response surface methodology for modeling and optimization of citric acid production by Aspergillus Niger MCBN297," Chemical Engineering Transaction, vol. 27, pp. 1974-9791, 2012.
A. Long, P. James, and O. P. Ward, "Aromatic aldehydes as substrate for yeast and yeast alcohol dehydrogenase," Biotechnol. Bioeng., vol. 33, pp. 657-660, 1989.
V. B. Shukla and P. R. Kulkarni, "Biotransformation of benzaldehyde in to L-phenylacetylcarbinol (L-PAC) by free cells of torulaspora delbrueckii in presence of beta-cylodetrin," Braz. Arch. Biol. Technol., vol. 45, pp. 265-268, 2002.
E. Oguz and M. Ersoy, "Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and neural network modeling," Chem. Eng. J., vol. 164, pp. 56–62, 2010.
E. S. Elmolla, M. Chaudhuri, and M. M. Eltoukhy, "The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process," Journal of Hazardous Materials, vol. 179, pp. 127–134, 2010.
M. Khajeh, M. Kaykhaii, and A. Sharafi, "Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples," J. Ind. Eng. Chem. Available: http://dx.doi.org/10.1016/j.jiec.2013.01.033, 2013.
M. Mourabet, A. El-Rhilassi, M. Bennani-Ziatni, and A. Taitai, "Comparative study of artificial neural network and response surface methodology for modelling and optimization the adsorption capacity of fluoride onto apatitic tricalcium phosphate," Universal Journal of Applied Mathematics, vol. 2, pp. 84-91, 2014.
X. Guan and H. Yao, "Optimization of viscozyme L-assisted extraction of oat bran protein using response surface methodology," Food Chemistry, vol. 106, pp. 345–351, 2008.