Design expert version 8

The results of the experimental design were analyzed and interpreted using Design Expert Version 8.0 (Stat-Ease Inc., Minneapolis, Minnesota, USA) statistical software.

Design Expert version 8.0 (Stat-Ease Inc., Minneapolis, MN, USA) was used to perform surface response analysis, including the ANOVA decomposition and significance tests for model coefficients, the optimization of the response as a function of the independent variables, and the plotting of the fitted surface. Triplicates were performed, and the results were given as mean ± SD. The significance was analyzed using SPSS 11.5 (SPSS Inc, Chicago, IL, USA) with a significance level of p < 0.01.

The significant variables affecting PUFA production from Kocuria sp. BRI 35 were identified using the Plackett-Burman design. In all, 8 nutritional factors, namely, sodium chloride (NaCl), yeast extract (YE), magnesium sulfate (MgSO₄), magnesium chloride (MgCl₂), protease peptone (P.Pep), potassium chloride (KCl), glucose, and calcium chloride (CaCl₂), along with 2 physical parameters, namely, temperature and pH, were evaluated. Each factor was studied at two levels. The variables studied and their high (+1) and low levels (−1) are presented in Table 1. A design for 12 experiments was generated using Design-Expert version 8.0 (Stat-Ease, Inc., Minneapolis, USA) software (Table 2). An additional experiment where the variables were maintained at values equal to those in MSM was included in the design along with the standard 12 experiments. The organism was cultivated in 100 mL medium in 250 mL Erlenmeyer flasks for 48 hours. All the experiments were carried out in triplicate and the response was measured in terms of the amount of PUFA produced by the organism. The variables with P value < 0.05 were considered to be significant.

All experiments were carried out in triplicate. Design Expert Version 8.0 (Stat-Ease Inc., Minneapolis, MN, USA) was used for performing data-analyses. The treatment effect was evaluated using analysis of variance and Duncan multiple-range test. Differences were considered to be significant at p < 0.05 throughout the present study.

Design Expert version 8.0.6.1 (Stat-Ease Inc., Minneapolis, MN, USA) software was employed in the process of experiment design, analysis, and modeling.
Screening experiment was initially performed through fractional factorial designs. The concentration of PEG6000, concentration of potassium citrate, concentration of [Emim] [OAC], pH, and amount of sample loading were taken into account in this step. The capsaicin yield (Y) was set as the response variable.
In order to obtain an optimized extraction system and evaluate the interaction between various factors, the response surface methodology (RMS) was introduced. The optimum conditions can be predicted by the second-order model as follows: $\mathrm{Y}={\beta }_0+{\displaystyle \sum }_{i=1}^k{\beta }_i{X}_i+{\displaystyle \sum }_{i=1}^k{\beta }_{ii}{X}_i^2+{\displaystyle \sum }{\displaystyle \sum }_{i<j}{\beta }_{ij}{X}_i{X}_j+\epsilon$
Among them, Y is the response variable (in percentage), β₀ is the fixed response value of the experimental center point, β_i, β_ij, and β_ii were the linear, cross product coefficients, and quadratic, respectively, and X_i and X_j were independent variables of the code.

According to the results of the single factor experiment, three main factors (ethanol concentration, solvent/material ratio, and extracting time) were selected for RSM design. A three-factor, five-level CCRD, with 20 experimental runs was carried out. The independent variables and their five levels are presented in Table 5. The results of CCRD were analyzed using ANOVA, and fitted to a second-order polynomial equation, as follows:

In the equation, Y is the response value, X_i and X_j are the independent variables, β₀ is the intercept, and β_i, β_ij, and β_ii are the regression coefficients for the linear, interaction, and quadratic terms, respectively.
The multiple regression fitting of the experimental data and the ANOVA of the quadratic polynomial regression model were performed using Design Expert version 8.06.1 software (Stat-Ease, Minneapolis, MN, USA).