Microencapsulation of Beetroot Anthocyanins: Investigation of Degradation Kinetics and Modeling by Using Artificial Neural Networks

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dc.contributor.authorBilenler Koc, Tugca
dc.contributor.authorFirat, Ilkay
dc.contributor.authorKarabulut, Ihsan
dc.contributor.authorBoztepe, Cihangir
dc.contributor.authorTopalcengiz, Zeynal
dc.date.accessioned2026-04-04T13:34:42Z
dc.date.available2026-04-04T13:34:42Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractAnthocyanins are widely appreciated as natural pigments, but their use in foods and related industries is still quite limited because they are highly sensitive to heat, pH changes, light, and oxygen. Improving their stability has therefore become a key focus in developing more reliable natural color systems. In this study, beetroot anthocyanins were microencapsulated with different wall materials, maltodextrin (MD), gum arabic (GA), a simple MD/GA blend, and a ternary structure combining MD, GA, and sodium caseinate (MD/GA/SC). These systems were evaluated for their encapsulation efficiencies, antioxidant activity preservation, release behaviors, and degradation responses over a wide range of temperatures (40-100 degrees C) and pH levels (2.5-6.5). Remarkable findings demonstrated that the MD/GA/SC formulation provided the highest encapsulation efficiency (93.36%), superior radical-scavenging activity (88.43%), and the most controlled release profile. Moreover, this formulation demonstrated the lowest degradation rate constants at pH 2.5, 4.5, and 6.5 (2.886, 2.083, and 1.30 1/min, respectively) together with the highest activation energies at these pH levels (37.460, 52.517, and 62.045 kJ/mol, respectively), indicating a pronounced improvement in thermal stability compared with the other formulations and the free extract. An artificial neural network (ANN) model was developed to predict anthocyanin degradation. The ANN provided highly accurate predictions (R 2 > 0.98, RMSE < 0.01) across all conditions and outperformed the classical first-order kinetic model. These findings highlight the potential of the MD/GA/SC matrix as a promising encapsulation system for improving anthocyanin stability. The strong performance of the ANN model also suggests that data-driven approaches can contribute meaningfully to designing more reliable microencapsulation strategies for future food and nutraceutical applications.
dc.description.sponsorshipIn?n? ?niversitesi [FYL-2021-2674]
dc.description.sponsorshipThis study was supported by Inonu University, Directorate for Scientific Research, Turkiye under project number of FYL-2021-2674.
dc.identifier.doi10.1021/acsomega.5c12228
dc.identifier.endpage2284
dc.identifier.issn2470-1343
dc.identifier.issue1
dc.identifier.orcid0000-0001-5019-2010
dc.identifier.pmid41552500
dc.identifier.scopus2-s2.0-105027281223
dc.identifier.scopusqualityQ1
dc.identifier.startpage2270
dc.identifier.urihttps://doi.org/10.1021/acsomega.5c12228
dc.identifier.urihttps://hdl.handle.net/11616/109352
dc.identifier.volume11
dc.identifier.wosWOS:001650268600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAmer Chemical Soc
dc.relation.ispartofAcs Omega
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectAntioxidant Activity
dc.subjectArabic Gum
dc.subjectShelf-Life
dc.subjectEncapsulation
dc.subjectRelease
dc.subjectMaltodextrin
dc.subjectQuality
dc.subjectOptimization
dc.subjectBehaviors
dc.subjectPropolis
dc.titleMicroencapsulation of Beetroot Anthocyanins: Investigation of Degradation Kinetics and Modeling by Using Artificial Neural Networks
dc.typeArticle

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