Three-dimensional (3D) response surface plots) and two-dimensional (2D) contour plots) are the graphical representations of the quadratic polynomial regression equation and usually illustrate the relationships between the experimental levels of each variable and corresponding response.
This kind of graphical visualization allows the relationships between the experimental levels of each factor and the response to be investigated, and the type of interactions between test variables to be determined, which is necessary to establish the optimal medium components and culture conditions.
The response surface plots provide a method to visualize the relationship between responses and experimental levels of each variables and the type of interactions between two test variables [[22]].
The graphical representation provides a method to visualize the relationship between the response and experimental levels of each variable and the type of interactions between test variables in order to deduce the optimum conditions.
Response surface contour plots and three dimensional graphs provide clear information on the relationship between the response and experimental levels of each variable.
In order to visualize the relationship between the response and experimental levels of each factor, and to deduce the optimum conditions, the regression coefficients were used to generate 3-D surface plots and contour plots from the fitted polynomial equation.
The fitted polynomial equation, aiming at visualizing the relationship between the response and experimental levels of each factor, developed the final response surfaces and deduced the optimum conditions [13, 14].
The fitted polynomial equation was expressed as three-dimensional (3D) surface plots to visualize the relation between responses and the experimental levels of each factor used in the design.
In order to depict the relationship between the responses and the experimental levels of each of the variables under study, the fitted polynomial equation was expressed in the form of contour and response surface plots.
The quality of fit the second-order polynomial model equation was expressed via the coefficient of determination (R) and the adjusted R. The fitted polynomial equation was then expressed in the form of three-dimensional surface plots, in order to illustrate the relationship between the responses and the experimental levels of each of the variables utilized in this study.
