Investigation of dyeing properties of mohair fiber dyed with natural dyes obtained from candelariella reflexa

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dc.contributor.authorEyupoglu, Can
dc.contributor.authorEyupoglu, Seyda
dc.contributor.authorMerdan, Nigar
dc.date.accessioned2023-02-16T10:51:33Z
dc.date.available2023-02-16T10:51:33Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mimarlık ve Tasarım Fakültesi, Moda ve Tekstil Tasarımı Bölümüen_US
dc.description.abstractThe current study reports on using ascorbic acid as a possible substitute for improving fastness properties of natural dyes. In the mordanting process, microwave energy, which is a part of the sustainable and ecological production approach, was used. Renewable natural dye source Candelariella reflexa, which is a genus of lichen, was obtained from the trunk of Pinus nigra. Mohair fiber was dyed with natural dye extracted from Candelariella reflexa by using a conventional method. Before dyeing, mohair fiber was subjected to the premordanted process with iron (III) chloride (FeCl3) using microwave energy. In order to determine the effect of mordanting process parameters on dyeing properties, the mordanting process was performed with different concentrations and durations. In the dyeing process, ascorbic acid was added at different concentrations in the dyeing bath to improve the light fastness of samples. After the dyeing process, spectrophotometric features, light, and rubbing and washing fastness of samples were investigated. The color strength, washing, light, and rubbing fastness of dyed mohair fiber improve slightly with the premordanting process and by adding ascorbic acid. The spectrophotometric measurement results show that color coordinates vary from the mordanting time and amount of ascorbic acid. Furthermore, the use of microwave energy in the mordanting process leads to saving of energy and time. Besides, in this study, a machine learning-based model exploiting the artificial neural network (ANN) was developed for prediction of dyeing properties of mohair fiber dyed with natural dyes obtained from Candelariella reflexa. Experimental data obtained through various tests were first used to feed the proposed ANN, and then the trained ANN was validated and tested for the aim of prediction. The study results show that the proposed model can successfully predict most of the dyeing properties of mohair fiber. Therefore, this model can be used as an effective tool to estimate dyeing characteristics of mohair fiber.en_US
dc.identifier.doi10.1080/15440478.2022.2076273en_US
dc.identifier.endpage12848en_US
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85131161281en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage12829en_US
dc.identifier.urihttps://hdl.handle.net/11467/6245
dc.identifier.urihttps://doi.org/10.1080/15440478.2022.2076273
dc.identifier.volume19en_US
dc.identifier.wosWOS:000802022000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofJournal of Natural Fibersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectartificial neural network; ascorbic acid; dyeing properties; machine learning; mohair fiber; Natural dyesen_US
dc.titleInvestigation of dyeing properties of mohair fiber dyed with natural dyes obtained from candelariella reflexaen_US
dc.typeArticleen_US

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