Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network

Despite response surface methodology (RSM) has been the most preferred statistical tool for optimizing extraction processes, artificial neural network (ANN) has been one of the most effective tools used for optimization and empirical modelling since the last two decades, most especially for non-line...

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Main Authors:	Alara, Oluwaseun Ruth, Nour, A. H., Afolabi, Haruna Kolawole, Olalere, Olusegun Abayomi
Format:	Article
Language:	English
Published:	Elsevier 2018
Subjects:	TP Chemical technology
Online Access:	http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/1/DJ8.pdf

id	ump-21927
recordtype	eprints
spelling	ump-219272019-09-10T03:59:59Z http://umpir.ump.edu.my/id/eprint/21927/ Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network Alara, Oluwaseun Ruth Nour, A. H. Afolabi, Haruna Kolawole Olalere, Olusegun Abayomi TP Chemical technology Despite response surface methodology (RSM) has been the most preferred statistical tool for optimizing extraction processes, artificial neural network (ANN) has been one of the most effective tools used for optimization and empirical modelling since the last two decades, most especially for non-linear equations. Thus, this study was carried out to compare the performance of RSM and ANN in optimizing the extraction yield and antioxidant capability of extract from Vernonia cinerea leaves using microwave-assisted extraction (MAE) techniques. The responses (extraction yield and antioxidant capabilities) were modelled and optimized as functions of four independent MAE parameters (irradiation time, microwave power level, ethanol concentration, and feed-to-solvent ratio) using RSM and ANN. The coefficient of determination (R2), root mean square error (RMSE) and absolute average deviation (AAD) were employed to compare the performance of both modelling tools. ANN model has a higher predictive potential compared to RSM model with higher correlation coefficients of 0.9912, 0.9928 and 0.9944 for extraction yield, DPPH and ABTS scavenging activities, respectively. Thus, ANN model could be a better alternative in data fitting for the MAE of antioxidants from Vernonia cinerea leaves Elsevier 2018-03-17 Article PeerReviewed pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/21927/1/DJ8.pdf Alara, Oluwaseun Ruth and Nour, A. H. and Afolabi, Haruna Kolawole and Olalere, Olusegun Abayomi (2018) Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network. Beni-Suef University Journal of Basic and Applied Sciences, 7 (3). pp. 276-285. ISSN 2314-8535 https://doi.org/10.1016/j.bjbas.2018.03.007 https://doi.org/10.1016/j.bjbas.2018.03.007
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Alara, Oluwaseun Ruth Nour, A. H. Afolabi, Haruna Kolawole Olalere, Olusegun Abayomi Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
description	Despite response surface methodology (RSM) has been the most preferred statistical tool for optimizing extraction processes, artificial neural network (ANN) has been one of the most effective tools used for optimization and empirical modelling since the last two decades, most especially for non-linear equations. Thus, this study was carried out to compare the performance of RSM and ANN in optimizing the extraction yield and antioxidant capability of extract from Vernonia cinerea leaves using microwave-assisted extraction (MAE) techniques. The responses (extraction yield and antioxidant capabilities) were modelled and optimized as functions of four independent MAE parameters (irradiation time, microwave power level, ethanol concentration, and feed-to-solvent ratio) using RSM and ANN. The coefficient of determination (R2), root mean square error (RMSE) and absolute average deviation (AAD) were employed to compare the performance of both modelling tools. ANN model has a higher predictive potential compared to RSM model with higher correlation coefficients of 0.9912, 0.9928 and 0.9944 for extraction yield, DPPH and ABTS scavenging activities, respectively. Thus, ANN model could be a better alternative in data fitting for the MAE of antioxidants from Vernonia cinerea leaves
format	Article
author	Alara, Oluwaseun Ruth Nour, A. H. Afolabi, Haruna Kolawole Olalere, Olusegun Abayomi
author_facet	Alara, Oluwaseun Ruth Nour, A. H. Afolabi, Haruna Kolawole Olalere, Olusegun Abayomi
author_sort	Alara, Oluwaseun Ruth
title	Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
title_short	Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
title_full	Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
title_fullStr	Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
title_full_unstemmed	Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
title_sort	efficient extraction of antioxidants from vernonia cinerea leaves: comparing response surface methodology and artificial neural network
publisher	Elsevier
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/ http://umpir.ump.edu.my/id/eprint/21927/1/DJ8.pdf
first_indexed	2023-09-18T22:32:24Z
last_indexed	2023-09-18T22:32:24Z
_version_	1777416372781842432

Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network

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