All animals with data on FPG and weight measured at baseline, days 7 and 14, were included in the analysis. Data were analysed using the Statistical Package for Social Sciences (IBM SPSS®) version 20.0 (Armonk, New York, USA). The mean and standard deviation of triplicates were calculated. Then, data were summarized into bar graphs. A one-way analysis of variance (ANOVA) was applied for hypothesis testing. Since homogeneity of variance was not assumed (Levene statistic was statistically significant, p-value < 0.001), we reported Welch’s p-value as a robust test of equality of means and later used Gomes-Howell multiple comparison tests for multiple comparisons mean between treatment groups. The p-value of less than 0.05 was considered statistically significant. The effective concentration (EC50) value representing the concentration of extract that exerts 50% of its maximal response was determined by regression analysis, using a Quest Graph™ EC50 Calculator [31].

Further analysis of experimental data was done using Design-Expert software version 12 (StatEase, Minneapolis, USA). Then data were fitted in a higher-order quadratic polynomial model as given in Eq 1.

Y represents the response variable (TPC), and Xi and Xj (ij) are independent factors. The β0, βj, βjj and βij are regression coefficients for the model intercept, linear, quadratic, and interaction terms. The regression parameters obtained from the multiple regression models quantify the impact of the three independent factors on the extraction efficiency, taking into account their interaction. The model’s adequacy was determined by assessing the p-value of the lack of fit, coefficient of determination (R2), and ANOVA F-value. The three-dimensional response surface graph was plotted for optimal TPC level against two independent factors.

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