The R2 coefficient presents the proportion of the total variation
in the response predicted by the model, indicating the ratio of the
sum of squares due to regression to the total sum of squares
(Ghafari et al., 2009). In addition, an R2 value close to 1 with
desirable and reasonable agreement with the adjusted R2 was
necessary; high R2 coefficient ensured a satisfactory adjustment of
the quadratic model to the experimental data. Therefore, the values
of R2 0.9280 (COD) and 0.9670 (color) (Table 6) showed a satisfactory
prediction of the experimental data.