Prediction of the extraction yield using artificial neural network and response surface methodology: ultrasound-assisted extraction from Achillea berbresteinii L.

Main Article Content

D. Salarbashi
F. Khanzadeh
S.M. Hosseini
M. Mohamadi
A. Rajaei
A. Daraei Garmakhany

Keywords

Achillea L, artificial neural network-genetic algorithm, phenolic compounds, response surface methodology

Abstract

This study investigates the extraction efficiency of phenolic compounds from Achillea berbresteinii by ultrasound-assisted extraction (UAE) method. Meanwhile, to predict the phenolic compound extraction yield, artificial neural network-genetic algorithm (ANN-GA) and response surface methodology (RSM) were compared. The results indicated that UAE method could significantly improve the extraction yield in comparison to conventional method. Optimised processing conditions were 35 °C, 6.3, 20% and 35 min as temperature, pH, solvent to sample ratio and extraction time, respectively. On the other hand, hybrid ANN-GA was employed to estimate the phenolic compound extraction yield. The results revealed the better capability of this method in comparison to RSM. Optimised network contained 8 and 3 neurons in first and second hidden layers, respectively. This configuration could estimate phenolic compound extraction yield with high correlation coefficient (0.94). Finally, A. berbresteinii can be considered as an excellent potential source of phenolic compounds and ANN-GA as a successful applied method for the prediction of the phenolic compound extraction yield.

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