Abstract

In this study, the support vector machine and the artificial neural network are adopted in the microwave-assisted extraction method to determine the amount of zinc in fish muscle samples. In the experiment, the irradiation power, irradiation time, nitric acid concentration and temperature are set as independent variables while the amount of zinc was considered as a function of the four factors. By comparing the RMS error and the training time of the support vector machine and the artificial neural network, the most suitable predicting model can be determined. The results show that the MLFN model with 7 nodes performed the best with the lowest RMS error of 1.21 and 100% prediction accuracy.

Keywords:

Artificial neural networks, microwave-assisted extraction, predicting accuracy, support vector machine,

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