A novel approach for olive leaf extraction through ultrasound technology : Response surface methodology versus artificial neural networks


Ilbay Z. , Sahin S. , Buyukkabasakal K.

KOREAN JOURNAL OF CHEMICAL ENGINEERING, vol.31, no.9, pp.1661-1667, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 9
  • Publication Date: 2014
  • Doi Number: 10.1007/s11814-014-0106-3
  • Title of Journal : KOREAN JOURNAL OF CHEMICAL ENGINEERING
  • Page Numbers: pp.1661-1667

Abstract

Response surface methodology (RSM) and artificial neural network (ANN) were used to evaluate the ultrasound-assisted extraction (UAE) of polyphenols from olive leaves. To investigate the effects of independent parameters on total phenolic content (TPC) in olive leaves, pH (3-11), extraction time (20-60 min), temperature (30-60 A degrees C) and solid/solvent ratio (500 mg/10-20 mL) were selected. RSM and ANN approaches were applied to determine the best possible combinations of these parameters. Box-Behnken design model was chosen for designing the experimental conditions through RSM. The second-order polynomial models gave a satisfactory description of the experimental data. Experimental parameters and responses were used to train the multilayer feed-forward networks with MATLAB. ANN proved to have higher prediction accuracy than that of RSM.