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- W3186996406 abstract "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.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.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.Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model." @default.
- W3186996406 created "2021-08-02" @default.
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- W3186996406 date "2021-07-21" @default.
- W3186996406 modified "2023-10-17" @default.
- W3186996406 title "Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling" @default.
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- W3186996406 doi "https://doi.org/10.1371/journal.pone.0253617" @default.
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