文章基本信息

标题：Efficient extraction of antioxidants from Vernonia cinerea leaves: Comparing response surface methodology and artificial neural network
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作者：Oluwaseun Ruth Alara ; Oluwaseun Ruth Alara ; Nour Hamid Abdurahman 等
期刊名称：Beni-Suef University Journal of Basic and Applied Sciences
印刷版ISSN：2314-8535
电子版ISSN：2314-8543
出版年度：2018
卷号：7
期号：3
页码：276-285
DOI：10.1016/j.bjbas.2018.03.007
语种：English
出版社：Elsevier
摘要：AbstractDespite 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 fromVernonia cinerealeaves 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 fromVernonia cinerealeaves.
关键词：Extraction yield;Vernonia cinerea;Artificial neural network;Antioxidants;Response surface methodology