TITLE:
Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles
AUTHORS:
Mansour Ghaffari Moghaddam, Mostafa Khajeh
KEYWORDS:
Artificial Neural Network; Response Surface Methodology; Box-Behnken Design; Microwave-Assisted Extraction; Predictive Capability
JOURNAL NAME:
Food and Nutrition Sciences,
Vol.2 No.8,
October
17,
2011
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.