## 4 Discussion and Findings  
Color attribute processing parameters were optimized using an analysis of variance (ANOVA). Software was used to determine the experimental design study parameters' influence at dL*, da* and db*. Serial F-tests, either linear or quadratic depending on the circumstances, were run after developing a linear model as the starting point and subsequently adding terms [21; 22]. The F-statistic was determined by selecting the model that has the greatest order and most relevant words and then calculating that model's score. For all of the tri-stimulus values, the same approach was followed and only 0.1 probability values and significant words with 0.05 F statistics were included. Influencing parameters found by ANOVA including dL*, da* and db* were described with a sequential model. When terms are at the greatest attainable level, they are the most relevant terms the quadratic models have. Therefore, R2 values, dL*, da* and db* are described in Table 4.  
With an R-squared value of roughly 90% (in dL*, da* and db*), the features generally account for about 5% of the variability in db. The presence of \"Adjacent R-Squared\" is shown in Table 4 as the \"Predicted R-Squared\" column. The signal-to-noise ratio (SNR) is referred to as \"sufficient precision\", and in most circumstances, it should be more than 4, which means that the model is able to work through the process. A comparison of pure and residual error was performed using the test known as \"lack of fit\". Having a P-value larger than 0.05 indicates that the lack of fit test is unimportant. Multiple linear regression models were created based on the findings of the ANOVA analysis to predict the dL*, da* and db* anticipated responses, which are displayed in Table 5. Process variables and their reactions to those responses are represented in a quantitative manner using polynomial equations. Each ANOVA analysis and multiple linear regression models were created to predict the dL*, da* and db* anticipated responses, which are displayed in Tables 3 and 4. Process variables and their reactions to those responses \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variation Responses** & **Sign. Parameters** & **(R2) Values** & **Predicted Values of (R2)** & **Adjacent Values of (R3)** &
\begin{tabular}{c} **Adeguate Precision** \\ **(Signal-to-Noise Ratio)** \\ \end{tabular} \\ \hline Light-dark (dL*) & (A, B, C); (BC); (B*) & 0.940 & 0.840 & 0.910 & 17.420 \\ \hline Green-red (da*) & (A, B, C); (C*); (B*); (A*); (BC); (AC) & 0.98 & 0.89 & 0.970 & 27.80 \\ \hline Yellow-blue (db*) & (A, B, C); (A*); (B*); (BC) & 0.720 & 0.40 & 0.560 & 5.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ANOVA analysis findings. are represented in a quantitative manner using polynomial equations. Each variable is assigned a coefficient value that is proportional to the degree to which it influences the responses. The discrepancies in Table 3 are minute, indicating a high level of agreement when compared to predicted and actual values. By altering the color values' output based on two process factors while leaving the third process parameter in its original state (constant), the response surface curves, also known as RSMs, may be formed. RSM curves are a useful tool for gaining a deeper understanding of the interplay that occurs between the various process components and for allocating their ideal values, both of which are important goals in the optimization process. Figure 3 depicts the dL* feed rate and speed graph at 274.4 \({}^{\circ}\)C. It demonstrates a nonlinear relationship between speed and feed rate, resulting in significantly different response behavior in each quadrant of the graph. Many feed rates and speed combinations can achieve the goal, as illustrated. For feeding at 24.4 kg/h and 728 rpm, the global dL* value of \(-0.01\) is displayed on the graph.