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

İlbay Z., ŞAHİN SEVGİLİ S., BÜYÜKKABASAKAL K.

Korean Journal of Chemical Engineering, cilt.31, sa.9, ss.1661-1667, 2014 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 9
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s11814-014-0106-3
  • Dergi Adı: Korean Journal of Chemical Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1661-1667
  • Anahtar Kelimeler: ANN, Box-Behnken, Olea europaea, Optimization, Polyphenols, RSM, Ultrasound-assisted Extraction
  • Uşak Üniversitesi Adresli: Evet

Özet

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 °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.