The experiment was designed to aid the design expert software (design expert® version 6.02, Minneapolis, USA) using box–behnken experimental design of response surface methodology with two numerical factors (soaking time [4–6 h] and soaking temperature [60–70°C]) and a categorical factor (rice variety NERICA‐4 and NERICA‐6) were used and generated 17 experimental points (Table 1 ). The minimum and maximum soaking time and temperature ranges were adjusted based on the recommendation of Heinemann et al. (2005 (link)) and Kale et al. (2015 ). Finally, physical properties, proximate composition, minerals content, and optimum soaking temperature and soaking time with better physicochemical properties were studied.
Design expert version 6
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Design-Expert version 6 is a software package for the design and analysis of experiments. It provides tools for creating and evaluating experimental designs, as well as for analyzing the data collected from those experiments.
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Lab products found in correlation
12 protocols using design expert version 6
1
Optimizing Rice Soaking Conditions
2
Optimized Response Surface Modeling
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For the first and second phases of the study, the data were analyzed and modeled using Design Expert® version 6.0.2, Minneapolis, USA, to generate second‐degree polynomial models with response surface effects. Contour plots were generated to visualize the combined effects of two factors on the response while keeping the third factor at its median value. For the third and fourth phases of the study, the results were analyzed using analysis of variance (ANOVA) with the Minitab statistical computer software program version 19. Differences were determined by the Tukey's test when p‐values were significant at a 5% probability level.
3
Optimizing Nanoemulsion Formulation Parameters
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RSM was employed to study the process parameters of the independent variables: homogenization rate (X1); sonication amplitude (X2); and sonication time (X3), on the particle size (Y1), ζ-potential (Y2), and viscosity (Y3) of the nanoemulsions. The experiments were approached using two different designs as a comparison between the CCRD and BBD. The coded independent variables in both of the designs are shown in Table 1 . The total number of runs was generated by Design Expert version 6.0.6 by Stat-Ease Inc. (Minneapolis, MN, USA). The design matrix was created, and the results were statistically analyzed, which were then converted into a response surface.
The designs were evaluated separately based on the influence of process variables in the modeling of the emulsion particle size, ζ-potential, and viscosity. Each design was expressed by second-order polynomial regression equation to generate the model shown below,
Design Expert software was used to obtain the combination of values that illustrate the response surface model. Experiments were run in a randomized order to avoid questionable variability that affects the outcome of the response due to extraneous factors. The center of the experimental field was performed six and five times for CCRD and BBD, respectively.
The designs were evaluated separately based on the influence of process variables in the modeling of the emulsion particle size, ζ-potential, and viscosity. Each design was expressed by second-order polynomial regression equation to generate the model shown below,
Design Expert software was used to obtain the combination of values that illustrate the response surface model. Experiments were run in a randomized order to avoid questionable variability that affects the outcome of the response due to extraneous factors. The center of the experimental field was performed six and five times for CCRD and BBD, respectively.
4
Optimizing RBD Palm Oil Quality
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The effects of the phosphoric acid percentage (X 1 ) and different types of bleaching earth (X 2 ) on the 3-MCPDE and GE levels of RBD palm oil was evaluated and modelled by using D-optimal design. Both degumming and bleaching processes on Standard Quality I CPO were studied concurrently using three phosphoric acid dosages (0 -2.5％ w/w) and three different bleaching earths at levels of 1％ w/w, namely natural bleaching earth (NBE) , acid-activated bleaching earth (acidic pH) ( AAA) and acid-activated bleaching earth (neutral pH) ( AAN) . D-optimal design was made up of 16 experimental runs with three factorial and four center points (Refer to Table 3) . Design Expert version 6.0.6 (Stat-Ease, Inc., Minneapolis, MN USA) were used to analyze data for all experimental runs with the level of significance set at a 95％ confidence level. F-test, ANOVA, and lack of fit test were used to eval-uate the adequateness of the models and the overall predictive capability of the model was elucidated by the coefficient of determination R 2 .
The experimental results in this study were analyzed using the MINITAB statistical software (Version 14, Minitab Inc., PA, USA) . All data were expressed as the means± standard deviations of duplicate analyses. A one-way analysis of variance (ANOVA) at the 5％ significance level was used to determine significant differences (p＜0.05) between the means.
The experimental results in this study were analyzed using the MINITAB statistical software (Version 14, Minitab Inc., PA, USA) . All data were expressed as the means± standard deviations of duplicate analyses. A one-way analysis of variance (ANOVA) at the 5％ significance level was used to determine significant differences (p＜0.05) between the means.
5
Optimizing TOC Removal via Central Composite Design
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The central composite face-centered design was employed to capture the effects of three essential adsorption process variables: (I) contact time, (J) solution temperature, and (K) adsorbent dosage on the responses (removal of TOC). The numerical factors were varied over 3-level plus and minus one axial point, plus and minus one (1) factorial point and one center point. The variables, the coded and actual values are as presented in Table 1 . The experimental results of the response surface methodology were fitted using second order polynomial Eq. (5) Where Y is the predicted responses (TOC removal), is the constant, is the quadratic coefficients. , is the interaction coefficients, and are the coded values of the chosen variables. Analysis of variance (ANOVA) was carried out to establish the significance of the quadratic model at a 95 % confidence interval and Design Expert Version 6.0.8 (Stat Ease, Inc., Minneapolis, MN 55413, USA) was used for the quadratic model fitting.Table 1
Variation of variables at the center point.
| Variables | Unit | Symbols | Coded values | Actual values | |
|---|---|---|---|---|---|
| -1 | 0 | ||||
| Contact time | (min) | I | 10 | 30 | 50 |
| Solution temperature | (°C) | J | 30 | 40 | 50 |
| Adsorbent dosage | (g) | K | 0.02 | 0.06 | 0.1 |
6
Optimizing Waste Cooking Oil Degradation
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A wide range of parameters and their interaction effects can play a role in the optimisation process using methodologies such as RSM [38 ]. To help simplify this, the initial use of a classical method such as OFAT can help in narrowing down the optimum range for each factor tested before proceeding to RSM. RSM was carried out based on surface placement with the key objectives being to study the topography of the response surface including the local maximum, local minimum and ridgelines and to identify the region (combination of conditions) where the optimum response occurs [39 ]. The Plackett-Burman design (PBD) was used to screen and remove factors with no significant influence on the degradation of WCO or PCO before optimising the response using central composite design (CCD). Statistical optimisation was carried out using Design Expert version 6.0.8 software (Stat-Ease Inc., Minneapolis, MI, USA).
7
Second-Order Polynomial Regression Model for Experimental Data Analysis
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A second‐order polynomial model for the dependent variables as shown in equation (5) was established to fit the experimental data. An analysis of variance (ANOVA) test was carried out using Design‐Expert Version 6 (Stat‐Ease, Inc., Minneapolis, MN) to determine level of significance at 5% level. The generalized regression model fitted was
where Y is the response; βo is a constant; while βi, βii and βiii are linear, quadratic, and interaction coefficients, respectively; and ε is error.
where Y is the response; βo is a constant; while βi, βii and βiii are linear, quadratic, and interaction coefficients, respectively; and ε is error.
8
Optimizing Experimental Extraction Conditions
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Design-Expert version 6.0.2 (Design 6.0.2, Stat-Ease, Inc., Minneapolis, MN, USA) was utilized to apply response surface methodology and optimize the experimental data. Regression analysis and response surface plots were employed to determine the optimal extraction conditions by examining the relationship between independent and dependent variables. The data were subjected to one-way analysis of variance (ANOVA), and mean separation was conducted using Duncan’s multiple range tests with a significance level of p < 0.05. Significant differences were indicated by using the same letters in a row. Statistical analyses were performed using SPSS 17.0 (SPSS, Inc., IBM Corp., Chicago, IL, USA), and t-tests (p < 0.05) were used to describe significant differences.
9
Validating Phenol Degradation Model
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Based on the results from CCD, the predicted value of response was generated using Design-Expert version 6 (Stat-Ease Inc., Minneapolis, MN, USA) to permit the validation of experiments with the values of significant factors given. Independent statistically designed experiments were carried out in triplicate to validate the predicted model. Subsequently, the actual value (percentage of phenol degradation) obtained from the experiment was compared with the predicted value of response generated by CCD [74 (link)].
10
Screening and Optimizing Phenol Degradation
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The Plackett–Burman design was employed to screen for significant factors prior to statistical optimization with RSM. The statistically planned experiments were designed and analysed by using statistical software Design-Expert version 6 (Stat-Ease Inc., Minneapolis, MN, USA). The experimental ranges of each parameter were selected based on the results from OFAT. Four important factors as previously identified in OFAT were optimized and screened at two levels (−1 and 1) using the Plackett–Burman design. Each statistically planned experiment was conducted in triplicate, and the significance of the effect of each factor on phenol degradation was determined. The experimental ranges and levels of the four independent variables tested in the Plackett–Burman design for strain AQ5-07 are shown in Table 7 . The analysis shows a total number of 12 experimental designs, with each row of the table consisting of four independent variables for the selected strain, where A is the temperature, B is pH, C is the concentration of NaCl (g/L), and D is the concentration of the nitrogen source (g/L).
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