Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles
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Abstract
In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.
- J. Y. Hao, W. Han, S. Huang, B. Xue and X. Deng, “Micro-Wave Assisted Extraction of Artemisinin from Artemisia annua L.” Separation and Purification Technology, Vol. 28, No. 3, 2000, pp. 191-196. doi:10.1016/S1383-5866(02)00043-6
- M. Soylak, M. Tuzen, A. S. Souza, M. G. A. Korn and S. L. C. Ferreira, “Optimization of Microwave Assisted Digestion Procedure for the Determination of Zinc, Copper and Nickel in Tea Samples Employing Flame Atomic Absorption Spectrometry,” Journal of Hazardous Materials, Vol. 149, No. 2, 2007, pp. 264-268. doi:10.1016/j.jhazmat.2007.03.072
- D. C. Montgomery, “Design and Analysis of Experiments,” Wiley, Hoboken, 2004.
- P. Sharma, L. Singh and N. Dilbaghi, “Optimization of Process Variables for Decolorization of Disperse Yellow 211 by Bacillus Subtilis Using Box-Behnken Design,” Journal of Hazardous Materials, Vol. 164, No. 2-3, 2008, pp. 1024-1029. doi:10.1016/j.jhazmat.2008.08.104
- D. Bas and I. H. Boyaci, “Modeling and Optimization II: Comparison of Estimation Capabilities of Response Surface Methodology with Artificial Neural Networks in a Biochemical Reaction,” Journal of Food Engineering, Vol. 78, No. 3, 2007, pp. 846-854. doi:10.1016/j.jfoodeng.2005.11.025
- B. Manohar and S. Divakar, “An Artificial Neural Network Analysis of porcine Pancreas Lipase Catalysed Esterification of Anthranilic Acid with Methanol,” Process Biochemistry, Vol. 40, No. 10, 2005, pp. 3372-3376. doi:10.1016/j.procbio.2005.03.045
- V. K. Pareek, M. P. Brungs, A. A. Adesina and R. Sharma, “Artificial Neural Network Modeling of a Multiphase photodegradation System,” Journal of Photochemistry and Photobiology A, Vol. 149, 2002, pp. 139-146.
- K. M. Desai, S. A. Survase, P. S. Saudagar, S. S. Lele and R. S. Singhal, “Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Fermentation Media Optimization: Case Study of Fermentative Production of Scleroglucan,” Biochemical Engineering Journal, Vol. 41, No. 3, 2008, pp. 266-273. doi:10.1016/j.bej.2008.05.009
- M. Basri, R. R. Zaliha, A. Ebrahimpour, A. B. Salleh, E. R. Gunawan and M. B. Abdul-Rahman, “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, 2007, pp. 53-63. doi:10.1186/1472-6750-7-53
- W. Lou and S. Nakai, “Application of Artificial Neural Networks for Predicting the Thermal Inactivation of Bacteria: A Combined Effect of Temperature, pH and Water Activity,” Food Research International, Vol. 34, No. 7, 2001, pp. 573-591. doi:10.1016/S0963-9969(01)00074-6