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Fig 1.

Chemical structure of diosgenin.

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Table 1.

Method validation for diosgenin quantification.

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Table 2.

Different extraction parameters used for diosgenin extraction from D. deltoidei.

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Table 3.

Diosgenin content from D. deltoidea tuber using different levels of extraction variables of Plackett-Burman design criteria.

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Table 4.

Regression analysis for prediction of significant extraction parameters by PBD criterion.

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Table 5.

Actual and coded treatment variables for optimization of diosgenin extraction from D. deltoidea using BBD.

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Table 6.

Experimental and predicted value of BBD design with four different extraction variables: Solid: Solvent ratio(X2), particle size (X3), time (X4) and temperature(X5).

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Fig 2.

A HPTLC fingerprinting of BBD samples (1–27) after derivatizing by anisaldehyde regent. B Chromatogram I of BBD samples (1–14); Chromatogram II of BBD samples (15–27).

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Fig 3.

Calibration plot for diosgenin.

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Table 7.

Regression analysis of BBD criterion data for diosgenin extraction from D. deltoidea tuber.

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Table 8.

Analysis of variance for diosgenin extraction from D. deltoidea by using BBD criterion.

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Fig 4.

A. Three-dimensional surface response plots for diosgenin extraction at varying level of temperature and solid/solvent ratio. B. Contour plot for diosgenin extraction at varying level of temperature and particle size. C. Contour plot for diosgenin extraction at varying level of temperature and time. D. Contour plot for diosgenin extraction at varying level of time and particle size. E. Contour plot for diosgenin extraction at varying level of time and solid/solvent ratio. F. Contour plot for diosgenin extraction at varying level of particle size and solid/solvent ratio.

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Fig 5.

Correlation between experimental versus computed yield of ANN.

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Fig 6.

A-B Performance data obtained (ANN) over entire training data and gradient loss for diosgenin.

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Table 9.

Comparison of predictive capacity of RSM and ANN models.

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Table 10.

Validation data set for extraction variables with corresponding experimental and predicted value (RSM and ANN).

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