Journal
KOREAN JOURNAL OF CHEMICAL ENGINEERING
Volume 31, Issue 9, Pages 1661-1667Publisher
KOREAN INSTITUTE CHEMICAL ENGINEERS
DOI: 10.1007/s11814-014-0106-3
Keywords
Olea europaea; Polyphenols; Ultrasound-assisted Extraction; Optimization; RSM; Box-Behnken; ANN
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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.
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