Optimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variables

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dc.authorid0000-0003-4577-028Xen_US
dc.contributor.authorCigeroglu, Zeynep
dc.contributor.authorAras, Ömür
dc.contributor.authorPinto, Carlos A.
dc.contributor.authorBayramoglu, Mahmut
dc.contributor.authorKirbaslar, Sismail
dc.contributor.authorLorenzo, Jose M.
dc.date.accessioned2021-03-20T20:13:03Z
dc.date.available2021-03-20T20:13:03Z
dc.date.issued2018
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Kimya Mühendisliği Bölümüen_US
dc.description.abstractBACKGROUND: The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 degrees C), solid:liquid ratio (50 - 100 gL(-1)) and calorimetric energy density of ultrasound (0.25-0.50 kW L-1), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor. RESULTS: The optimized experimental conditions yielded by RSM were: 10.80% for ethanol concentration; 58.52 min for extraction time; 30.37 degrees C for extraction temperature; 52.33 g L-1 for solid:liquid ratio; 0.457 kW L-1 for ultrasonic power density, with thick probe type. Under these conditions total phenolics content was found to be 19.04 mg gallic acid equivalents g dried leaf. CONCLUSION: The same dataset was used to train multilayer feed-forward networks using different approaches via MATLAB, with ANN exhibiting superior performance to RSM (differences included categorical factor in one model and higher regression coefficients), while close values were obtained for the extraction variables under study, except for ethanol concentration and extraction time. (C) 2018 Society of Chemical Industryen_US
dc.description.sponsorshipIstanbul University Research Fund (BAP)Istanbul University [3426]; BAP; FCT/MEC [FCT UID/QUI/00062/2013]; FEDEREuropean Commissionen_US
dc.description.sponsorshipThis study was supported by Istanbul University Research Fund (BAP, Project No. 3426). All authors thank BAP for support. Thanks are due to FCT/MEC for financial support to the QOPNA Research Unit (FCT UID/QUI/00062/2013) through national funds and where applicable co-financed by the FEDER, within the PT2020 Partnership Agreement.en_US
dc.identifier.doi10.1002/jsfa.8987en_US
dc.identifier.endpage4596en_US
dc.identifier.issn0022-5142
dc.identifier.issn1097-0010
dc.identifier.issue12en_US
dc.identifier.pmid29508393en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4584en_US
dc.identifier.urihttp://doi.org/10.1002/jsfa.8987
dc.identifier.urihttps://hdl.handle.net/20.500.12885/777
dc.identifier.volume98en_US
dc.identifier.wosWOS:000440302900025en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorAras, Ömür
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal Of The Science Of Food And Agricultureen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectultrasound-assisted extractionen_US
dc.subjectpolyphenolsen_US
dc.subjectgrapefruit leavesen_US
dc.subjectD-optimal designen_US
dc.subjectartificial neural networken_US
dc.subjectoptimizationen_US
dc.titleOptimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variablesen_US
dc.typeArticleen_US

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