## 4 Conclusions  
This study showcases the innovative application of the CFD method in predicting protein denaturation in goose breast meat and optimizing heat treatment procedures. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & \multicolumn{2}{c}{**Optimized Process Parameters**} \\ \hline Temperature [\({}^{\circ}\)C] & 164.65 \\ Humidity [\%] & 63.58 \\ Fan rotation [rpm] & 16.59 \\ \hline Responses & Expected values & Laboratory analyses values \\ \hline Myosin denaturation level [\%] & 99.42 \(\pm\) 0.80 & 99.26 \(\pm\) 0.78 \\ Collagen denaturation level [\%] & 88.69 \(\pm\) 0.74 & 87.98 \(\pm\) 0.82 \\ Actin denaturation level [\%] & 20.64 \(\pm\) 0.31 & 21.01 \(\pm\) 0.47 \\ Weight loss [\%] & 19.12 \(\pm\) 0.10 & 18.98 \(\pm\) 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Verification of prediction model with empirical analyses (X\({}^{-}\)\(\pm\) SD). Through the utilization of mathematical models, the study identified optimal thermal treatment parameters: 164.65 \({}^{\circ}\)C temperature, 63.58% air humidity, and a fan rotation speed of 16.59 rpm. The research assessed the denaturation of various proteins and losses during the roasting process. Upon laboratory result verification, it was observed that the denaturation levels of myosin, collagen, and actin, as well as the losses during cooking, closely aligned with the expectations derived from the developed RSM model based on simulation data. This investigation underscores the efficacy of the CFD method in forecasting protein denaturation and cooking losses in goose breast meat, offering potential enhancements in efficiency and product quality for the gastronomy and food industry. Thanks to the use of CFD for predicting the protein denaturation degree and mass loss in goose meat, it is possible to precisely determine the optimal conditions for heat treatment. This will allow for the production of products with better and more consistent quality and, consequently, higher sensory acceptance among consumers. By accurately calculating the optimal conditions for thermal processing, the heat dose will be precisely adjusted, which will allow for the reduction of energy losses in the food industry. As a result, energy consumption during roasting will be reduced. Additionally, the reduced mass loss during the thermal processes of goose meat will lead to increased process efficiency in gastronomy and the food industry. The findings of this study can potentially be generalized to other types of meat or food products by making appropriate modifications to the CFD model parameters specific to the new food matrix. This includes adjusting the thermal properties, moisture content, and structural characteristics of the target food product. Furthermore, extensive experimental validation would be necessary to ensure the accuracy and reliability of the CFD predictions in the new context. By doing so, the benefits observed in goose breast meat, such as optimized heat treatment conditions, improved product quality, and reduced energy consumption, could also be achieved in other meats or food products, enhancing their overall production efficiency and sensory acceptance. However, there are potential practical challenges in implementing CFD-based optimization in commercial food production settings. These challenges include the need for high computational power and expertise in CFD modeling, which may not be readily available in all food production facilities. To overcome these challenges, it is essential to develop user-friendly software with pre-configured settings and provide training for food industry professionals. Collaboration between CFD experts and food technologists can also facilitate the integration of CFD tools into commercial processes. Additionally, investment in advanced computational infrastructure and ongoing research to refine and simplify CFD models will be crucial to making this technology accessible and effective for broader application in the meat industry.