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J. Bourquin, H. Schmidli., P. V. Hoogevest and A. Leuenberger, “Advantages of Artificial Neural Networks (ANNs) as Alternative Modeling Technique for Data Sets Showing Non-Linear Relationships Using Data from a Galenical Study on a Solid Dosage Form,” European Journal of Pharmaceutical Sciences, Vol. 7, No. 1, 1998, pp. 5-16. doi:10.1016/S0928-0987(97)10028-8

has been cited by the following article:

• 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.