Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.

<h4>Introduction</h4>Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural networ...

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Autores principales:	Romaan Nazir, Devendra Kumar Pandey, Babita Pandey, Vijay Kumar, Padmanabh Dwivedi, Aditya Khampariya, Abhijit Dey, Tabarak Malik
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e39e475ce3a84850b4e0c96ecc64695f
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spelling	oai:doaj.org-article:e39e475ce3a84850b4e0c96ecc64695f2021-12-02T20:06:46ZOptimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.1932-620310.1371/journal.pone.0253617https://doaj.org/article/e39e475ce3a84850b4e0c96ecc64695f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253617https://doaj.org/toc/1932-6203<h4>Introduction</h4>Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural network (ANN) modelling have been implemented to evaluate the diosgenin content from D. deltoidea. In addition, different extraction parameters have been also optimized and developed.<h4>Materials and methods</h4>Firstly, Plackett-Burman design (PBD) was applied for screening the significant variables among the selected extraction parameters i.e. solvent composition, solid: solvent ratio, particle size, time, temperature, pH and extraction cycles on diosgenin yield. Among seven tested parameters only four parameters (particle size, solid: solvent ratio, time and temperature) were found to exert significant effect on the diosgenin extraction. Moreover, Box-Behnken design (BBD) was employed to optimize the significant extraction parameters for maximum diosgenin yield.<h4>Results</h4>The most suitable condition for diosgenin extraction was found to be solid: solvent ratio (1:45), particle size (1.25 mm), time (45 min) and temperature (45°C). The maximum experimental yield of diosgenin (1.204% dry weight) was observed close to the predicted value (1.202% dry weight) on the basis of the chosen optimal extraction factors. The developed mathematical model fitted well with experimental data for diosgenin extraction.<h4>Conclusions</h4>Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model.Romaan NazirDevendra Kumar PandeyBabita PandeyVijay KumarPadmanabh DwivediAditya KhampariyaAbhijit DeyTabarak MalikPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253617 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Romaan Nazir Devendra Kumar Pandey Babita Pandey Vijay Kumar Padmanabh Dwivedi Aditya Khampariya Abhijit Dey Tabarak Malik Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
description	<h4>Introduction</h4>Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural network (ANN) modelling have been implemented to evaluate the diosgenin content from D. deltoidea. In addition, different extraction parameters have been also optimized and developed.<h4>Materials and methods</h4>Firstly, Plackett-Burman design (PBD) was applied for screening the significant variables among the selected extraction parameters i.e. solvent composition, solid: solvent ratio, particle size, time, temperature, pH and extraction cycles on diosgenin yield. Among seven tested parameters only four parameters (particle size, solid: solvent ratio, time and temperature) were found to exert significant effect on the diosgenin extraction. Moreover, Box-Behnken design (BBD) was employed to optimize the significant extraction parameters for maximum diosgenin yield.<h4>Results</h4>The most suitable condition for diosgenin extraction was found to be solid: solvent ratio (1:45), particle size (1.25 mm), time (45 min) and temperature (45°C). The maximum experimental yield of diosgenin (1.204% dry weight) was observed close to the predicted value (1.202% dry weight) on the basis of the chosen optimal extraction factors. The developed mathematical model fitted well with experimental data for diosgenin extraction.<h4>Conclusions</h4>Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model.
format	article
author	Romaan Nazir Devendra Kumar Pandey Babita Pandey Vijay Kumar Padmanabh Dwivedi Aditya Khampariya Abhijit Dey Tabarak Malik
author_facet	Romaan Nazir Devendra Kumar Pandey Babita Pandey Vijay Kumar Padmanabh Dwivedi Aditya Khampariya Abhijit Dey Tabarak Malik
author_sort	Romaan Nazir
title	Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
title_short	Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
title_full	Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
title_fullStr	Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
title_full_unstemmed	Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
title_sort	optimization of diosgenin extraction from dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e39e475ce3a84850b4e0c96ecc64695f
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Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.

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