}). Extraction temperature (X~1~), extraction time (X~2~), and methanol concentration (X~3~) were chosen as the independent variables. This design was applied to investigate the optimal working conditions for the extraction of GA from licorice.Table 1Independent variables and codified values employed for optimization of the extraction procedure.Table 1Independent variablesCode unitsCoded levels−1.68−1011.68Temperature (°C)X~1~4350607077Time (min)X~2~2835455562Methanol concentration (%)X~3~5360708087Table 2Central composite design of three variables with their observed responses.Table 2No.X~1~ (°C)X~2~ (min)X~3~ (%)Glycyrrhizic acid (%)150 (−1)35 (−1)60 (−1)3.307270 (1)35 (−1)60 (−1)3.414350 (−1)55 (1)60 (−1)3.183470 (1)55 (1)60 (−1)3.272550 (−1)35 (−1)80 (1)3.160670 (1)35 (−1)80 (1)3.266750 (−1)55 (1)80 (1)3.199870 (1)55 (1)80 (1)3.332943 (−1.68)45 (0)70 (0)3.2081077 (1.68)45 (0)70 (0)3.3371160 (0)28 (−1.68)70 (0)3.2751260 (0)62 (1.68)70 (0)3.2601360 (0)45 (0)53 (−1.68)3.3321460 (0)45 (0)87 (1.68)3.2081560 (0)45 (0)70 (0)3.3711660 (0)45 (0)70 (0)3.3561760 (0)45 (0)70 (0)3.3681860 (0)45 (0)70 (0)3.3431960 (0)45 (0)70 (0)3.3322060 (0)45 (0)70 (0)3.360

2.5. Statistical analysis {#sec0035}
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All the analyses were carried out in triplicate and the experimental results were expressed as mean values. Statistical analysis was performed using the Minitab 16 (Minitab Inc., State College, PA, USA) software. A response surface analysis and analysis of variance (ANOVA) were employed to determine the regression coefficients and statistical significance of the model terms and to fit the mathematical models of the experimental data that aimed to optimize the overall region for response variable.[@bib0125] A second order polynomial model was applied to predict the response variables as shown in Eq. [(1)](#eq0005){ref-type="disp-formula"}where Y is the predicted response; β~0~ is the constant (intercept); β~1~, β~2~, and β~3~ are the regression coefficients for the 