Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions

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dc.contributor.authorDurmuş, Yusuf
dc.contributor.authorAtasoy, Ayşe Dilek
dc.contributor.authorAtasoy, Ahmet Ferit
dc.date.accessioned2024-12-09T07:29:16Z
dc.date.available2024-12-09T07:29:16Z
dc.date.issued2024
dc.departmentAÇÜ, Turizm Fakültesi, Gastronomi ve Mutfak Sanatları Bölümüen_US
dc.description.abstractThe objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values.
dc.identifier.doi10.1038/s41598-024-69149-1
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-024-69149-1
dc.identifier.urihttps://hdl.handle.net/11494/5138
dc.identifier.volume14en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANNen_US
dc.subjectHierarchical Clusteringen_US
dc.subjectISOMAPen_US
dc.subjectMathematical Optimizationen_US
dc.subjectPCAen_US
dc.subjectRegressionen_US
dc.subjectTeaen_US
dc.titleMathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusionsen_US
dc.typeArticle

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