## 3 Results and Discussion
### Shore A Hardness Results  
Figure 3 illustrates the impact of CPP and HSO quantities on the Shore A hardness of the polyurethane-based composite. An increase in the quantity of CPP is observed to have a discernible impact on the hardness values. The pronounced increase in the hardness curve and the abrupt color transitions along the CPP axis clearly demonstrate the significant influence of CPP on hardness. Conversely, an increase in the quantity of HSO has been observed to exert a diminishing influence on the Shore A hardness of the composite.  
Figure 2: Effects of CPP and HSO content on bulk density change.  
This observed trend in the graph can be attributed to the interaction of HSO with the polymer matrix, which results in a softer structure. Upon examination of the Shore A hardness values depicted in the graph, it is evident that the highest hardness values are attained with high CPP and low HSO combinations. These findings indicate that precise adjustment of the CPP and HSO ratios is essential for optimizing the hardness values of the composite. Equation (2) presents the quadratic model proposed by the RSM design for Shore A hardness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Value** & _p-Value_ \\ \hline
**Model** & 968.60 & 107.62 & 4324.86 & \textless{}0.0001 \\ \hline
**A—CPP (g)** & 681.33 & 681.33 & 27,379.65 & \textless{}0.0001 \\ \hline
**B–HSO (g)** & 4.72 & 4.72 & 189.73 & \textless{}0.0001 \\ \hline
**C—MDI (g)** & 280.23 & 280.23 & 11,261.06 & \textless{}0.0001 \\ \hline
**AB** & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ \hline
**AC** & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ \hline
**BC** & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ \hline
**A\({}^{2}\)** & 0.0326 & 0.0326 & 1.31 & 0.2899 \\ \hline
**B\({}^{2}\)** & 2.10 & 2.10 & 84.31 & \textless{}0.0001 \\ \hline
**C\({}^{2}\)** & 0.0096 & 0.0096 & 0.3843 & 0.5549 \\ \hline
**Residual** & 0.1742 & 0.0249 & & \\ \hline
**Lack of Fit** & 0.1742 & 0.0348 & & \\ \hline
**Pure Error** & 0.0000 & 0.0000 & & \\ \hline
**Cor Total** & 968.78 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA analysis for bulk density of the biocomposites.  
Figure 3: Effect of CPP and HSO amounts on Shore A hardness of polyurethane based-composite.  
Shore A hardness = \(-\) 370.34663 + 8.81107 \(\times\) CPP + 4.22309 \(\times\) HSO + 8.57187 \(\times\) MDI \(-\) 0.183333 \(\times\) CPP \(\times\) HSO \(-\) 0.195833 \(\times\) CPP \(\times\) MDI + 0.029167 \(\times\) HSO \(\times\) MDI  
The results of the ANOVA are presented in Table 3 and show that the model is statistically significant with a \(p\)-value of less than 0.0001. The model accounts for a substantial amount of the total variance as indicated by a model sum of squares (SS) of 172.22 compared to a residual SS of 1.02. Upon further examination, the individual effects of CPP (A), HSO (B), and MDI (C) were all found to be significant (\(p\) < 0.05). Among these, HSO (B) emerges as the most dominant parameter, contributing the largest proportion of variance with an F-value of 1564.44. In addition, the interactions between factors AB (CPP and HSO) and AC (CPP and MDI) are statistically significant (\(p\) < 0.05), indicating that these combinations influence the model when acting together. However, the BC interaction (HSO and MDI) is not statistically significant (\(p\) = 0.4559) and has a negligible effect on the model. The reliability of the model is further supported by its low error rate (residual SS = 1.02) and the non-significant lack-of-fit test (\(p\) > 0.05), indicating that the model accurately represents the experimental data. In conclusion, the analysis highlights HSO (B) as the most influential factor and underscores the importance of considering specific interactions, such as AB and AC, to achieve accurate predictions.