Experimental Design and Statistical Analysis
Experimental Design and Statistical Analysis
Response surface methodology (RSM) was applied to determine the best combination of extrusion process variables for the production of snack with a twin-screw extruder and for the application in foods industry. In this study, a four-factor experimental set-up was used with brewers spent cassava levels-CBS (X1), barrel temperature-Temp (X2) (Barrel temperature of zone 3), water levels-W (X3) and main screw speed-MSS (X4) as the independent factors at three levels each. The data obtained was analyzed by response surface methodology (RSM) using Box–Behnken design (Table 1) to optimize process variables. Twenty-nine combinations including five replicates of center point was performed in random order according to the Box–Behnken design. A second-order polynomial model for the dependent variables as shown in Eq. (1) was established to fit the experimental data. An analysis of variance (ANOVA) test was carried out using Design-Expert Version 7 (Stat-Ease) to determine level of significance at 5% level. Y= β_o+ ∑_(i=1)^4▒〖β_i X_i 〗+ ∑_(i=1)^4▒〖β_ii X_i^2 〗+ ∑▒∑_(i Where Y is the response; β_o is a constant; while β_i, β_ii, and β_ij are linear, quadratic and interaction coefficients, respectively; and ε is error.
Response surface methodology (RSM) was applied to determine the best combination of extrusion process variables for the production of snack with a twin-screw extruder and for the application in foods industry. In this study, a four-factor experimental set-up was used with brewers spent cassava levels-CBS (X1), barrel temperature-Temp (X2) (Barrel temperature of zone 3), water levels-W (X3) and main screw speed-MSS (X4) as the independent factors at three levels each. The data obtained was analyzed by response surface methodology (RSM) using Box–Behnken design (Table 1) to optimize process variables. Twenty-nine combinations including five replicates of center point was performed in random order according to the Box–Behnken design. A second-order polynomial model for the dependent variables as shown in Eq. (1) was established to fit the experimental data. An analysis of variance (ANOVA) test was carried out using Design-Expert Version 7 (Stat-Ease) to determine level of significance at 5% level. Y= β_o+ ∑_(i=1)^4▒〖β_i X_i 〗+ ∑_(i=1)^4▒〖β_ii X_i^2 〗+ ∑▒∑_(i Where Y is the response; β_o is a constant; while β_i, β_ii, and β_ij are linear, quadratic and interaction coefficients, respectively; and ε is error.
Nhận xét
Đăng nhận xét