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dc.creatorUysal, Sengul
dc.creatorCvetanović, Aleksandra
dc.creatorZengin, Gokhan
dc.creatorĐurović, Saša
dc.creatorZeković, Zoran
dc.creatorAktumsek, Abdurrahman
dc.date.accessioned2023-06-01T10:09:14Z
dc.date.available2023-06-01T10:09:14Z
dc.date.issued2018
dc.identifier.issn0003-2719
dc.identifier.urihttps://riofh.iofh.bg.ac.rs/handle/123456789/597
dc.description.abstractIn the last few years, bioactive components or their extraction techniques are gaining special interest in scientific areas. In this framework, orange leaves were used for preparation of extracts with high content of biologically active compounds. To optimize the extraction process, three levels and three variables of Box-Behnken design with response surface methodology were applied. Investigated responses were the total phenolic content (TPC), 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical, cupric ion reducing antioxidant capacity (CUPRAC), and ferric reducing antioxidant power (FRAP). Independent variables were methanol concentration (10-90%), temperature (20-60 degrees C), and extraction time (60-180min). Experimentally obtained results were fit into a second-order polynomial model with multiple regression. Analysis of variance was used to estimate model fitness and determine optimal conditions for processing. Estimated optimal conditions were 90% methanolic solution, 60 degrees C and 180min using these parameters; the predicted values of investigated responses were 43.19mg GAE/g (GAE: gallic acid equivalents), 43.04mg TE/g (TE: trolox equivalents), 139.34 and 93.76mg TE/g for TPC, DPPH, CUPRAC, and FRAP, respectively. The obtained optimal conditions could be considered as an alternative strategy for developing novel functional products.en
dc.rightsrestrictedAccess
dc.sourceAnalytical Letters
dc.subjectresponse surface methodologyen
dc.subjectorange leavesen
dc.subjectoptimizationen
dc.subjectextraction processen
dc.subjectBox-Behnken designen
dc.titleEffects of Orange Leaves Extraction Conditions on Antioxidant and Phenolic Content: Optimization Using Response Surface Methodologyen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage1519
dc.citation.issue10
dc.citation.other51(10): 1505-1519
dc.citation.rankM23
dc.citation.spage1505
dc.citation.volume51
dc.identifier.doi10.1080/00032719.2017.1382501
dc.identifier.rcubconv_715
dc.identifier.scopus2-s2.0-85044028269
dc.identifier.wos000429348300005
dc.type.versionpublishedVersion


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