A Statistical analysis was performed using Design-Expert 7.0.10 software (Stat-Ease Inc., United States) and IBM SPSS Statistics version 24. ANOVA was used to compare multiple samples, and the p value < 0.05 was considered significant.
Design expert 7
Manufactured by Stat-Ease
Sourced in United States
Design Expert 7.0 is a statistical software package used for the design and analysis of experiments. It provides a comprehensive set of tools for optimizing product and process designs, including tools for experimental design, analysis, and optimization.
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76 protocols using design expert 7
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of D-optimal Design
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Statistical analysis of d -optimal design was performed using Design-Expert 7.0.10 software (Stat-Ease Inc., U.S.A). Responses were analyzed using the ANOVA test. The p-value of <0.05 was considered as significant.
3
Optimized Amikacin-Loaded Niosomal Formulation
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The effect of independent variables (lipid mmol, the molar ratio of surfactant: cholesterol, and the molar ratio of span 60: twin 60) on physicochemical properties of amikacin-loaded niosome by optimal design using Design-Expert 7.0.10 software (Stat-Ease Inc., USA) was done (Table 1 ). In addition, the effect of the specified variables on size, polydispersity index (PDI), and encapsulation efficiency percent (EE%) is presented in Table 2 . The optimal formulation with the smallest size and PDI range with the highest EE% was selected to continue the studies.
Different levels for variables in the Box–Behnken design optimization
| Level | − 1 | 0 | + 1 |
|---|---|---|---|
| A (Lipid, µmol) | 200 | 250 | 300 |
| B (Surfactant: Cholesterol, molar ratio) | 0.5 | 1 | 2 |
| C (Span60:Tween60, molar ratio) | 75:25 | 50:50 | 25:75 |
Design of experiments using Box–Behnken method to optimize the niosomal formulation of Amikacin
| Run | Levels of independent variables | Dependent variables | ||||
|---|---|---|---|---|---|---|
| Lipid, µmol | Surfactant: cholesterol, molar ratio | Span60:Tween60, molar ratio | Average size (nm) | PDI | Entrapment efficiency (EE) (%) | |
| 1 | 1 | − 1 | 0 | 284.3 | 0.319 | 57.34 |
| 2 | 0 | 1 | − 1 | 209.5 | 0.287 | 52.25 |
| 3 | − 1 | 1 | 0 | 207.4 | 0.142 | 53.23 |
| 4 | 0 | 0 | 0 | 189.2 | 0.159 | 57.42 |
| 5 | 1 | 0 | − 1 | 197.4 | 0.253 | 58.24 |
| 6 | 0 | 0 | 0 | 183.5 | 0.184 | 56.49 |
| 7 | 0 | 0 | 0 | 175.6 | 0.166 | 54.3 |
| 8 | 0 | − 1 | 1 | 280.4 | 0.379 | 54.85 |
| 9 | − 1 | 0 | − 1 | 175.2 | 0.188 | 53.12 |
| 10 | − 1 | − 1 | 0 | 248.9 | 0.291 | 55.79 |
| 11 | 0 | − 1 | − 1 | 220.6 | 0.334 | 49.41 |
| 12 | 1 | 0 | 1 | 271.4 | 0.369 | 67.23 |
| 13 | 1 | 1 | 0 | 182.3 | 0.157 | 62.75 |
| 14 | 0 | 1 | 1 | 232.4 | 0.283 | 64.49 |
| 15 | − 1 | 0 | 1 | 242.9 | 0.315 | 60.21 |
4
Pressure Effects on Experimental Design
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The effect of pressure level was tested with a one-way analysis of variance (ANOVA) followed by a multiple comparisons test (Tukey's honestly significant difference, HSD) to identify differences between treatments. The level of significance was established at p<0.05.
The experimental design was statistically analyzed using the Design Expert 7.1.1 (Stat-Ease, Inc., MN, USA). The set of experiments followed a central composite design (CCD). This design has three groups of points: a) two-level factorial design points; b) axial points; c) centre points. This design has 5 levels of the independent variables with desirable statistical properties. In this case, to study the range 150-450
MPa, the design gives the following levels: 150, 169.27, 300, 430.73 and 450 MPa. The models used, and the statistical approach are described previously (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2018) (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2017).
The experimental design was statistically analyzed using the Design Expert 7.1.1 (Stat-Ease, Inc., MN, USA). The set of experiments followed a central composite design (CCD). This design has three groups of points: a) two-level factorial design points; b) axial points; c) centre points. This design has 5 levels of the independent variables with desirable statistical properties. In this case, to study the range 150-450
MPa, the design gives the following levels: 150, 169.27, 300, 430.73 and 450 MPa. The models used, and the statistical approach are described previously (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2018) (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2017).
5
Pressure Effects on Experimental Design
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The effect of pressure level was tested with a one-way analysis of variance (ANOVA) followed by a multiple comparisons test (Tukey's honestly significant difference, HSD) to identify differences between treatments. The level of significance was established at p<0.05.
The experimental design was statistically analyzed using the Design Expert 7.1.1 (Stat-Ease, Inc., MN, USA). The set of experiments followed a central composite design (CCD). This design has three groups of points: a) two-level factorial design points; b) axial points; c) centre points. This design has 5 levels of the independent variables with desirable statistical properties. In this case, to study the range 150-450
MPa, the design gives the following levels: 150, 169.27, 300, 430.73 and 450 MPa. The models used, and the statistical approach are described previously (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2018) (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2017).
The experimental design was statistically analyzed using the Design Expert 7.1.1 (Stat-Ease, Inc., MN, USA). The set of experiments followed a central composite design (CCD). This design has three groups of points: a) two-level factorial design points; b) axial points; c) centre points. This design has 5 levels of the independent variables with desirable statistical properties. In this case, to study the range 150-450
MPa, the design gives the following levels: 150, 169.27, 300, 430.73 and 450 MPa. The models used, and the statistical approach are described previously (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2018) (Pita-Calvo, Guerra-Rodríguez, Saraiva, Aubourg, & Vázquez, 2017).
6
Optimizing Pressure and Storage Conditions
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The experimental design was statistically analyzed using the Design Expert 7.1.1 software (Stat-Ease, Inc., Minneapolis, MN). The set of experiments followed a central composite design (CCD). A CCD has three groups of design points: (a) twolevel factorial or fractional factorial design points; (b) axial points (sometimes called "star" points); (c) center points. This design has 5 levels of the independent variables with desirable statistical properties. The following second order polynomial model was used as a first approach to analyse the experimental data:
where x i (i = 1-2) are the code variables for pressure level and storage time;
where x i (i = 1-2) are the code variables for pressure level and storage time;
7
Multivariate Analysis of Experimental Data
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All analyses were performed in triplicate. MS Excel 2020 (Redmond, WA, USA) was used to calculate mean values and standard deviations. One-way analysis of variance (ANOVA) was performed on the response surface results using Design-Expert 7.0.0 software (Stat-Ease Inc., Minneapolis, MN, USA). ANOVA followed by Turkey’s HSD test was employed for statistical analysis at a probability level of p < 0.05.
8
Optimizing Fenton's Process for Landfill Leachate
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The optimization of the process variables and the linear, interactive and quadratic effects on the process was studied through CCF.23,24 (link) In this study three independent variables A: pH, B: hydrogen peroxide (H2O2) dose, C: ferrous sulphate (FeSO4) dose and residual COD as response was evaluated by 20 experiments (2n + 2n + x0, 23 = 8 – factorial points; 2 × 3 = 6 – axial points; 6 – centre points). The Table 1 shows the summary of design parameters for degradation of landfill leachate by oxidative Fenton's process with respect to actual and coded factors for CCF design. Design Expert 7.0.0., Stat-ease, USA was used to design the experiments and to analyze the data.
9
Optimizing Bioink Formulation for 3D Bioprinting
10
Seahorse Extraction and Purification
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With the aid of Microsoft Excel, SAS Deployment Wizard 9.2 (SAS Institute Inc., Cary, NC, USA) and Design-Expert 7.0.0 (Stat- Ease, Inc., Minneapolis, MN), the conditions and effects of extraction and purification of the seahorse were analyzed and compared by the one-way ANOVA.
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