Design expert software version

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Design-Expert software, Version is a statistical software package for experimental design, optimization, and analysis. It provides tools for creating and analyzing factorial designs, response surface designs, mixture designs, and other experimental designs. The software helps users plan, analyze, and interpret the results of their experiments to improve product quality and process performance.

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Design-Expert software, Version 11 Design Expert® software version 13 Design-Expert® Software Version 12 Design Expert software version 9 Design-Expert software version 10 Design Expert® 7.0 software version 7 Design expert software, version 6.0 Design-Expert® software version 7 Design-Expert software version 8.0.6 Design-Expert software

5 protocols using design expert software version

Curcumin Proniosome Optimization

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Nine Curcumin proniosomes were optimized according to a 3² factorial design using Design-Expert software, Version 7.0.0 (Stat-Ease, Inc., Minneapolis, MN, USA) to demonstrate the impact of the independent variables on different responses [30 (link)]. The ratio of surfactant to CHO and type of surfactant were considered as the independent variables X1 and X2, respectively. The entrapment efficiency (EE%, Y1) and the percentage of Curcumin released after 12 h (Q_12h, Y2) were selected as the dependent variables. Each factor was screened at three levels (−1, 0, and +1) that labeled the lower, the middle, and the upper levels, respectively.
The coefficient of determination (R²), predicted R² and adjusted R² were estimated in addition to plotting diagnostic plots for both EE% and Q_12h in order to demonstrate the goodness of fit of the present model to the experimental results. The statistical analysis of the data was performed by the analysis of variance (ANOVA) for estimation of the significance level of each term according to the p-value and F-statistics [31 (link)].

Badria F.A., Abdelaziz A.E., Hassan A.H., Elgazar A.A, & Mazyed E.A. (2020). Development of Provesicular Nanodelivery System of Curcumin as a Safe and Effective Antiviral Agent: Statistical Optimization, In Vitro Characterization, and Antiviral Effectiveness. Molecules, 25(23), 5668.

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Optimizing Nanoparticle Size via Design-Expert

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The experimental design results were statistically analysed using Design-Expert software version 10.0.5.0 (Stat-ease- Inc., Minneapolis). Response and interaction plots were generated to examine the effect of parameter levels on the mean response (particle size). Half-normal plots and Pareto charts were generated to guide the selection of parameters for the final optimal model. The data were assessed by ANOVA combined with Fisher’s statistical test (F- test) to determine whether a chosen parameter had a significant effect on the desired value (p < 0.05). The S/N ratio formula below (i.e. smaller-the-better) was used to evaluate the response values. All data presented were expressed as mean and standard deviation (SD).

\frac{S}{N} r a t i o = - 10 * log \frac{\sum (Y^{2})}{n}

where Y is the mean and n is the number of experiments.

Mensah R.A., Kirton S.B., Cook M.T., Styliari I.D., Hutter V, & Chau D.Y. (2019). Optimising poly(lactic-co-glycolic acid) microparticle fabrication using a Taguchi orthogonal array design-of-experiment approach. PLoS ONE, 14(9), e0222858.

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Optimizing Hydrochar Properties via CCD

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In this study,
the influences of the reaction temperature and hold
time on the MY and CV of the hydrochars produced were studied using
a typical RSM design known as the central composite design (CCD).
The CCD is suitable for fitting a quadratic surface and optimizing
variables with a small number of experiments while also analyzing
the interaction between the variables.^{44 (link)} The CCD for this study has two variables consisting of four factorial
points, four axial points, and five center points that must be replicated
five times to achieve a reliable approximation of the experimental
error and data reproducibility. The target parameters (responses)
for optimization were MY and CV. A total of 13 experimental runs were
obtained, as seen in Table 5, each representing a different combination of the preparation
variables and the results obtained. The design and response of each
experimental run were evaluated using Stat-Ease Inc.’s design
expert software version 11.1.2.0.
The experimental outcomes were evaluated
using ANOVA. The regression
model was calculated for all responses using the probability (p-value) and the Fischer test value (F-value).
Subsequently, the best-fitting model for MY and CV was selected based
on the statistical parameters. In addition, the preparation variables
(temperature and hold time) were optimized to achieve the desired
target responses, such as maximum MY and CV.

Abdulsalam J., Setsepu R.L., Lawal A.I., Onifade M, & Bada S.O. (2023). Recycling of Trees Planted for Phytostabilization to Solid Fuel: Parametric Optimization Using the Response Surface Methodology and Genetic Algorithm. ACS Omega, 8(8), 7448-7458.

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Optimization of Nanosuspension Formulation

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Nanosuspensions were prepared by high speed (IKA T25, Ultra Turrex) 45 min at 15000 rpm) and high pressure homogenization technique (Gea Niro Soavi, 750 barr pressure with 20 cycles). To establish the process of nanosuspension trial batches were prepared. Combination of HPMC and poloxamer 407 (stearic stabilizer) and SLS (surfactant) were used for the preparation of nanosuspension. Response surface methodology (RSM) with central composite design (quadratic model) was used for the optimization of nanosuspension (Design-Expert® software, version 12, Stat-Ease, Inc., Minneapolis, MN, USA) with 2 independent factors as mention in Table 1. According to the design, 13 runs including factorial points (4), central points (5) and axial points (4) were analysed. Dependant variables were screened for all the batches, these are particle size, PDI and zeta potential. The polynomial fitting quality was employed to quantify the effect of independent formulation variable A and B on the response variables Y with applied constraints. Quadratic model generated following Eq. (1): Where Y is the measured response and A, B are the independent formulation variables.Table 1

Independent variable factors of design with coded value.

Table 1

Factor	Name	Minimum (-α)	Maximum (+α)	Coded Low(-1)	Coded High(+1)
A	Poloxamer 407	31.72	88.28	-1 ↔ 40.00	+1 ↔ 80.00
B	SLS	1.89	23.11	-1 ↔ 5.00	+1 ↔ 20.00

Kakad S, & Kshirsagar S. (2021). Nose to brain delivery of Efavirenz nanosuspension for effective neuro AIDS therapy: in-vitro, in-vivo and pharmacokinetic assessment. Heliyon, 7(11), e08368.

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Lycopene Extraction Optimization via ASCD

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An augmented simplex centroid design (ASCD) was used to investigate the effects of the composition of solvent mixtures on lycopene recovery. The ASCD consisted of a {3,3} simplex lattice, with nine equispaced points on the perimeter of the triangle and three points in the internal region (Figure 5). Experiments at the vertices and at the center point were replicated for the estimation of pure error, leading to a total of 17 runs. They were carried out in random order.
The lycopene extraction yield, expressed as the percentage amount of extracted lycopene relative to the total amount of lycopene in the peels, was taken as the response variable.
The Design-Expert® software (version 7.0, Stat-Ease Inc., Minneapolis, MN, USA) was used to design the experiments and analyse the results.

, & Zuorro A. (2020). Enhanced Lycopene Extraction from Tomato Peels by Optimized Mixed-Polarity Solvent Mixtures. Molecules, 25(9), 2038.

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