2026-10-29T22:39:13Zhttps://bayburt-rdlopen.rawdatalibrary.net/oai/request

oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/133762026-10-29T19:52:14Zopenairedrivercom_bayburt_10000col_bayburt_10001com_bayburt_10007col_bayburt_10227

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization AlgorithmGurgen, AyşenurSerencam, HuseyinKara, YakupCan, ZehraYlldlz, Sibelartificial neural networkextraction parametersmodellingoptimizationpropolisIn this study, the optimum values of propolis ethanol extracts parameters were determined with the use of single and multi-objective optimization procedures. The euclidean distance approach was used in the multi-objective optimization process. Firstly, propolis was extracted using water with ethanol contents 40, 50, 60, 70 and 80% for 8, 10, 12, 16, 20 and 24 h. Then, total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) activities of all extracts were determined. With the obtained data a prediction model was produced with the use of artificial neural networks (ANN), and optimization was performed using a moth-flame (MFO) algorithm. The best prediction models for the TPC and FRAP were observed in 2-5-1 and 2-5-1 network architecture with the mean absolute percentage error (MAPE) values, 5.126 and 2.451%, respectively. For maximum TPC, the extraction parameters were determined as ethanol content 57.5% and extraction time 13.56 h. To maximize FRAP, the optimized extraction parameters were ethanol content 72.03% and extraction time 18.04 h. The optimum extraction conditions for both maximum values of the studied assays were ethanol content 70.03% and extraction time 16.93 h. The study concluded that the integrated ANN and MFO algorithm system can be used in single and multi-objective optimization of extraction parameters. The established optimization model can save time, money, labor and energy.Sciendo2021Articlehttps://rdlopen.rawdatalibrary.net/r/bayburt/13376https://doi.org/10.2478/jas-2021-00181643-4439enJournal of Apicultural Scienceinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://bayburt-rdlopen.rawdatalibrary.net/tr/items/bayburt/13376