Machine learning-guided green ultrasound processing enhances polyphenols, antioxidant capacity, and in vitro bioaccessibility of black fig vinegar

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dc.authorid0000-0002-0737-7009
dc.authorid0000-0003-1512-8552
dc.authorid0009-0009-7023-2142
dc.authorid0000-0002-4899-2219
dc.authorid0000-0001-8694-0658
dc.authorid0000-0003-3365-7327
dc.authorid0000-0001-7293-883X
dc.authorid0000-0002-6127-2195
dc.authorid0000-0003-1936-9337
dc.authorid0000-0001-5089-9200
dc.authorid0000-0001-9840-4211
dc.contributor.authorÇakmak Sancar, Burcu
dc.contributor.authorAktaran Bala, Deniz
dc.contributor.authorDemirel, Selinay
dc.contributor.authorGürfidan, Remzi
dc.contributor.authorYıkmış, Seydi
dc.contributor.authorKilim, Oğuzhan
dc.contributor.authorAçıkgözoğlu, Enes
dc.contributor.authorŞimşek, Mehmet Ali
dc.contributor.authorTokatlı Demirok, Nazan
dc.contributor.authorIbrahim, Marwa Ezz El-Din
dc.contributor.authorTokatlı, Nazlı
dc.date.accessioned2026-08-21T12:41:30Z
dc.date.available2026-08-21T12:41:30Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractBlack fig vinegar is a functional fermented product rich in phenolic compounds and antioxidants; however, conventional thermal pasteurization may reduce its bioactive composition and functional quality. Although ultrasound processing has emerged as a promising non-thermal alternative, its optimization, predictive modeling, and influence on the in vitro bioaccessibility of black fig vinegar have not yet been comprehensively investigated. Therefore, this study aimed to optimize ultrasound processing conditions and evaluate their effects on phenolic composition, antioxidant capacity, and in vitro bioaccessibility of traditionally produced black fig vinegar using response surface methodology (RSM) integrated with ensemble machine learning algorithms. Untreated, pasteurized, and ultrasound-treated vinegars were compared, while ultrasound processing variables (processing time and amplitude) were optimized through RSM. The experimental dataset was subsequently expanded using data augmentation, and Extra Trees, CatBoost, Gradient Boosting, and AdaBoost models were developed to predict total phenolic content (TPC) and ferric reducing antioxidant power (FRAP). Among the tested algorithms, the Extra Trees model achieved the best predictive performance, with coefficients of determination (R2) exceeding 0.998 and prediction accuracies above 99%. Under the optimized ultrasound conditions (8 min and 58% amplitude), TPC (79.77 ± 3.69 mg GAE/100 mL), FRAP (8.87 ± 0.24 mmol TE/L), and total flavonoid content were significantly higher than those of untreated and pasteurized samples (p < 0.05). Ultrasound processing also significantly increased caffeic acid, catechin hydrate, chrysin, vanillin, p-coumaric acid, o-coumaric acid, and trans-ferulic acid concentrations. During simulated gastrointestinal digestion, all samples exhibited reductions in bioactive compounds; however, ultrasound-treated vinegar consistently retained higher TPC, total flavonoid content, and FRAP values, with bioaccessibility remaining approximately 30%–32%. Pearson correlation and principal component analyses further identified caffeic acid, trans-ferulic acid, rutin, and o-coumaric acid as the principal contributors to antioxidant capacity. This study integrates ultrasound optimization, RSM, data enhancement, community machine learning, phenolic profiling, and in vitro bioavailability assessment for black fig vinegar, presenting a robust and sustainable strategy for process optimization and the development of high-quality functional vinegar products.
dc.description.sponsorshipThe author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. KFU264316). Yazar(lar) bu çalışma ve/veya yayınlanması için mali destek aldıklarını beyan etmiştir. Bu çalışma, Suudi Arabistan, Kral Faysal Üniversitesi, Bilimsel Araştırmalar Dekanlığı, Lisansüstü Çalışmalar ve Bilimsel Araştırmalar Başkan Yardımcılığı tarafından desteklenmiştir (Burs No. KFU264316).
dc.identifier.citationÇakmak Sancar, B., Aktaran Bala, D., Demirel, S., Gürfidan, R., Yıkmış, S., Kilim, O., Açıkgözoğlu, E., Şimşek, M. A., & Tokatlı Demirok, N., Ibrahim, M. E. E. D., & Tokatlı, N. (2026). Machine learning-guided green ultrasound processing enhances polyphenols, antioxidant capacity, and in vitro bioaccessibility of black fig vinegar. Frontiers in Nutrition, 13, pp. 1-16. https://doi.org/10.3389/fnut.2026.1934149
dc.identifier.doi10.3389/fnut.2026.1934149
dc.identifier.endpage16
dc.identifier.issn2296-861X
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.3389/fnut.2026.1934149
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1589
dc.identifier.volume13
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expanded
dc.institutionauthorTokatlı, Nazlı
dc.institutionauthorid0000-0001-9840-4211
dc.language.isoen
dc.publisherFrontiers Media S. A.
dc.relation.ispartofFrontiers in Nutrition
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBioaccessibility
dc.subjectBlack Fig Vinegar
dc.subjectFunctional Foods
dc.subjectGreen Processing
dc.subjectMachine Learning
dc.subjectPolyphenols
dc.subjectUltrasound Processing
dc.titleMachine learning-guided green ultrasound processing enhances polyphenols, antioxidant capacity, and in vitro bioaccessibility of black fig vinegar
dc.typeArticle
dspace.entity.typePublication

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