Optimization of Ethanol Extraction Conditions From Sun-Dried Apricot and Prediction of Antioxidant, Phenolic, and Flavonoid Contents and Their Effects on HCT116 Cells Using Artificial Neural Networks

Basit Öğe Kaydı
dc.authorid0000-0003-2877-7105
dc.authorid0000-0001-8762-9693
dc.authorid0000-0001-9832-553X
dc.authorid0000-0002-3715-8739
dc.contributor.authorTopcu, Kubra Cinar
dc.contributor.authorGueven, Sara Altun
dc.contributor.authorCakir, Ozlem
dc.contributor.authorAnlar, Pinar
dc.contributor.authorDurul, Melekber Sulusoglu
dc.contributor.authorAy, Mizgin
dc.contributor.authorErcisli, Sezai
dc.date.accessioned2026-02-28T12:17:40Z
dc.date.available2026-02-28T12:17:40Z
dc.date.issued2025
dc.departmentBayburt Üniversitesi
dc.description.abstractThis study aimed to optimize the ethanol extraction conditions of sun-dried apricot (Prunus armeniaca L.) and evaluate the antioxidant capacity, phenolic, and flavonoid contents of the obtained extracts. Response surface methodology (RSM) was applied to determine the optimal extraction conditions, which were identified as 60 degrees C temperature, 34% ultrasonic power, 46 min sonication time, and a 4 g/mL solid-liquid ratio. Under these conditions, the extract exhibited a total phenolic content (TPC) of 4.20 mg GAE/g, a total flavonoid content (TFC) of 7.09 mg QE/g, a DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging capacity of 1.37 mg TE/g, a ferric reducing antioxidant power (FRAP) value of 9.12 mg TE/g, and a thiobarbituric acid reactive substances (TBARS) level of 1.69 mg MDA/g. The artificial neural network (ANN) model provided highly accurate predictions for these parameters. Additionally, cell culture experiments demonstrated that the extract exerted a dose-dependent cytotoxic effect on HCT116 colon cancer cells, significantly reducing their viability. These findings highlight the potential of sun-dried apricot extracts as natural antioxidants with possible applications in the functional food and pharmaceutical industries.
dc.identifier.doi10.1002/fsn3.70610
dc.identifier.issn2048-7177
dc.identifier.issue7
dc.identifier.pmid40678329
dc.identifier.scopus2-s2.0-105010894690
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/fsn3.70610
dc.identifier.urihttps://hdl.handle.net/20.500.12403/5907
dc.identifier.volume13
dc.identifier.wosWOS:001537443200014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofFood Science & Nutrition
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260218
dc.subjectANN
dc.subjectantioxidant activity
dc.subjectHCT116
dc.subjectphenolic compounds
dc.subjectRSM
dc.subjectsun-dried apricot
dc.titleOptimization of Ethanol Extraction Conditions From Sun-Dried Apricot and Prediction of Antioxidant, Phenolic, and Flavonoid Contents and Their Effects on HCT116 Cells Using Artificial Neural Networks
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu