Statistical Analysis
The pH and TTA (response variables) obtained during fermentation were
subjected to regression analysis and analysis of variance (ANOVA) to
determining regression coefficients and statistical significances of
model terms and to fit the mathematical models to the experimental data,
aiming at an overall optimal region for the response variable, multiple
regression coefficients were determined by employing the least-squares
technique to predict linear and quadratic polynomial model for the
response variable studied. The behavior of the response surface was
investigated for the response function (Y, the predicted response) using
the regression polynomial equations. The generalize polynomial model
proposed for the predicting the response variable is given as
Y=βo+β1x1+β2x2+ β3x3 +
β12x1 x2+
β13x1 x3+
β23x2 x3+
β11x21 +
β22x22 +
β33x23
Where βo is the intercept,
β1,β2 and β3 as
coefficient. The significance of the equation parameters for each
response variable was also assessed by F ratio at a probability (P) of
0.05. The adequacy of the models was determined using model analysis,
lack of fit test and coefficient of determination (R2)
analysis as describe by [8, 9], for a good fit of a model
R2 should be at least 0.80 [10, 11]. The
experimental design matrix, data and analysis, and optimization
procedure were performed using the Design-Expert version 7 (state-Ease,
Inc, Minneapolis, MN, USA).
3. RESULTS AND DISCUSSION