dc.contributor.author | Buyukkabasakal, Kemal | |
dc.contributor.author | Ilbay, Zeynep | |
dc.contributor.author | Sahin, Selin | |
dc.date.accessioned | 2021-03-04T19:14:37Z | |
dc.date.available | 2021-03-04T19:14:37Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Ilbay Z., Sahin S., Buyukkabasakal K., "A novel approach for olive leaf extraction through ultrasound technology : Response surface methodology versus artificial neural networks", KOREAN JOURNAL OF CHEMICAL ENGINEERING, cilt.31, ss.1661-1667, 2014 | |
dc.identifier.issn | 0256-1115 | |
dc.identifier.other | av_8ec4b328-b044-4a02-8c2e-db6f28cd90c9 | |
dc.identifier.other | vv_1032021 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12627/96461 | |
dc.identifier.uri | https://doi.org/10.1007/s11814-014-0106-3 | |
dc.description.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. | |
dc.language.iso | eng | |
dc.subject | Alkoloidler | |
dc.subject | Temel Bilimler | |
dc.subject | Mühendislik ve Teknoloji | |
dc.subject | Mühendislik, Bilişim ve Teknoloji (ENG) | |
dc.subject | Biyokimya | |
dc.subject | Kimya Mühendisliği ve Teknolojisi | |
dc.subject | Mühendislik | |
dc.subject | MÜHENDİSLİK, KİMYASAL | |
dc.subject | Temel Bilimler (SCI) | |
dc.subject | Kimya | |
dc.subject | KİMYA, MULTİDİSİPLİNER | |
dc.title | A novel approach for olive leaf extraction through ultrasound technology : Response surface methodology versus artificial neural networks | |
dc.type | Makale | |
dc.relation.journal | KOREAN JOURNAL OF CHEMICAL ENGINEERING | |
dc.contributor.department | Ege Üniversitesi , , | |
dc.identifier.volume | 31 | |
dc.identifier.issue | 9 | |
dc.identifier.startpage | 1661 | |
dc.identifier.endpage | 1667 | |
dc.contributor.firstauthorID | 81508 | |