Design‐Expert software 8.0.6 (Stat‐Ease Inc.) and SPSS 13.0 (SPSS Inc.) were used to analyze the experimental data. Experiments were repeated at least in triplicate. The result values are presented as the mean ± standard deviation, p < .05 indicates a statistically significant difference, and p < .01 indicates a highly statistically significant difference.
Design expert software 8
Manufactured by Stat-Ease
Sourced in United States
Design-Expert software 8.0.6.1 is a statistical software package for the design and analysis of experiments. It provides tools for experimental design, data analysis, and optimization.
Automatically generated - may contain errors
Lab products found in correlation
12 protocols using design expert software 8
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of Experimental Data
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All data were analyzed in Excel for mean and variance analysis. The experimental values were expressed by MEAN ± SD. One-way analysis of variance (ANOVA) was performed using SPSS Statistics 22 software (IBM, New York, NY, USA), and Origin 2018C (OriginLab Corporation, Northampton, MA, USA) was used for data mapping. Response surface analyses were carried out using Design-Expert® software 8.0.6 (Stat-Ease inc., Minneapolis, MN, USA).
3
Optimizing Phospholipid Extraction Conditions
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Based on the experimental results of single factor, the phospholipid extraction conditions of the HAS were further optimized using RSM to obtain the highest total (PC＋PE) content. Three independent variables, including solvent ratio of n-hexane to acetone (X 1 ) , freezing time (X 2 ) , freezing temperature (X 3 ) , were investigated. The coding levels and value ranges of the three variables are shown in Table 1. A total of 17 runs (Table 2) of the three variables, including five center points, were designed by Box-Behnken design using Design-Expert software 8.0.6 (Stat-Ease, Inc., USA) 32) . Total (PC＋PE) content was specified as the re-sponse value. According to the experimental design, each experiment was executed and then the observed values of the output response ［i.e., (PC＋PE) total content］ were input in the software for fitting a statistical model. The statistical model for the three independent variables can be written as
, where Y was the total content of (PC＋PE) , X 1 was the solvent ratio of n-hexane to acetone, X 2 was the freezing time, and X 3 was the freezing temperature. The coefficients of the equations were determined by Design-Expert 8.0.6 using analysis of variance (ANOVA) .
, where Y was the total content of (PC＋PE) , X 1 was the solvent ratio of n-hexane to acetone, X 2 was the freezing time, and X 3 was the freezing temperature. The coefficients of the equations were determined by Design-Expert 8.0.6 using analysis of variance (ANOVA) .
4
Triplicate Experiments with Statistical Analysis
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All experiments were conducted in triplicate, and data were expressed in means ± standard deviations. The obtained data were analyzed by the statistical package of the Design Expert software 8.0.5 (Stat-Ease Inc., Minneapolis, MN, USA). Statistical analysis was performed using Origin 9.0 software (OriginLab Corporation, Northampton, MA, USA). Statistical significances were carried out by an independent-sample t-test. Values of p < 0.05 were considered as statistically significant.
5
Triplicate Experiments for Statistical Analysis
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All experiments were conducted in triplicate, and data were expressed in means ± standard deviations. The obtained data were analyzed by the statistical package of the Design Expert software 8.0.5 (Stat-Ease Inc., Minneapolis, MN, USA). The independent sample t-test (p < 0.05) was used to explore significant differences.
6
Optimizing Nanoparticle Formulations by Factorial Design
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A 32 randomized full factorial design was run to optimize the variables. In this design, two independent factors (volume of organic media [X1] and amount of polymer content [X2]) were evaluated, each at three levels to develop nine formulations (F1-F9) and experimental trials were performed using Design Expert Software 8.0.7.1 (Stat-Ease, Inc., Minneapolis, USA). Table 1 represents the design model. The dependent variables that are particle size, % entrapment efficiency (%EE) and %cumulative drug release (%CDR) were evaluated and polynomial equations were generated for the dependent variables that were reduced by removing nonsignificant coefficients by applying one-way analysis of variance (ANOVA) (P < 0.05). To demonstrate graphically, the influence of each factor on responses, the response surface plots and three-dimensional (3D) bar graph were generated.
7
Optimized Enteric-Coated Colon-Targeted Capsules
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A numerical optimization technique using the desirability approach was employed to select optimized formulation with desired responses. Constraints like maximizing EE and %drug release at the end of 8 h as well as minimizing particle size were set as goals to select the optimized formulation using Design Expert Software 8.0.7.1 (Stat-Ease, Inc., Minneapolis, MN, USA). Optimized formulation was plugged in enteric-coated colon-targeted capsules.
8
Optimization of P. ginseng Polysaccharide Extraction
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According to the results of preliminary single-factor tests, the extraction conditions of P. ginseng polysaccharides were then optimized by using RSM. A four-variable and three-level CCD containing 21 runs was applied at the center point using extraction yield as the response value (Table 6 ).
Regression analysis was applied for the experimental data and fitted to the following second-order polynomial equation:
where Y represents the response function; β0 represents the intercept; βi, βii and βij represent the coefficients of linear, quadratic, and interactive terms, respectively; and Xi and Xj represent the coded independent variables. Design-Expert software 8.0.6.1 (Stat-Ease, Minneapolis, MN, USA) was used to estimate the response of the independent variables, and was applied to the calculation of the predicted data. Next, each test was repeated three times, and a value of p < 0.05 was considered to be statistically significant.
Regression analysis was applied for the experimental data and fitted to the following second-order polynomial equation:
where Y represents the response function; β0 represents the intercept; βi, βii and βij represent the coefficients of linear, quadratic, and interactive terms, respectively; and Xi and Xj represent the coded independent variables. Design-Expert software 8.0.6.1 (Stat-Ease, Minneapolis, MN, USA) was used to estimate the response of the independent variables, and was applied to the calculation of the predicted data. Next, each test was repeated three times, and a value of p < 0.05 was considered to be statistically significant.
9
Experimental Design and Statistical Analysis
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Each experiment was operated in triplicate, and the data were displayed as the means ± standard deviations. The experimental design, modeling, and data analysis were performed by Design Expert software 8.0.6.1 (Stat-Ease Inc., Minneapolis, MN, USA). Statistical analysis was conducted using Origin 9.0 software (OriginLab Corporation, Northampton, MA, USA).
10
Optimized Process for Compound Extraction
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Results were represented as the means ± SD (standard deviation). RSM optimization was conducted by using Design-Expert software 8.0.6.1 (Stat-Ease, Minneapolis, MN, USA). The t-test and one-way analysis of variance (ANOVA) were performed for evaluating the significance, respectively.
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