<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In 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.</p></abstract>

In 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.

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm

Faculty of Forest, Forest Industry Engineering

School of Applied Sciences, Gastronomy and Culinary Arts

Faculty of Science, Department of Chemistry

Journal of Apicultural Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this study, the optimum values of propolis ethanol extracts parameters were determined with the use of single and multi-objective optimization procedures....

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm

Article Category: Original Article

Publicado en línea: 26 oct 2021

Páginas: 229 - 241

Recibido: 16 jun 2020

Aceptado: 08 jul 2021

DOI: https://doi.org/10.2478/jas-2021-0018

Palabras clave
artificial neural network, extraction parameters, modelling, optimization, propolis

© 2021 Ayşenur Gurgen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm

Article Category: Original Article

Publicado en línea: 26 oct 2021

Páginas: 229 - 241

Recibido: 16 jun 2020

Aceptado: 08 jul 2021

DOI: https://doi.org/10.2478/jas-2021-0018

Palabras claveartificial neural network, extraction parameters, modelling, optimization, propolis

© 2021 Ayşenur Gurgen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
artificial neural network, extraction parameters, modelling, optimization, propolis