Open AccessOptimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling
Author(s) -
Romaan Nazir,
Devendra Kumar Pandey,
Babita Pandey,
Vijay Kumar,
Padmanabh Dwivedi,
Aditya Khampariya,
Abhijit Dey,
Tabarak Malik
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0253617
Subject(s) - diosgenin , response surface methodology , extraction (chemistry) , particle size , yield (engineering) , box–behnken design , chromatography , materials science , chemistry , organic chemistry , composite material
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. Materials and methods 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. Results 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. Conclusions Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model.