## 3 Results and Discussion
### Thermal Treatment Parameters Optimization
Additionally, it was possible to predict the quantitative breakdown of DHA acid, the consumption of which has a significant impact on human health [23]. Additionally, [PERSON] et al. (2022) investigated the optimization of heat treatment of beef using CFD simulations, with an emphasis on modeling the level of protein denaturation depending on the basic composition of the sample. Our study simulated the roasting of 3 different pieces of beef with different basic compositions (i.e., the content of water, fat, protein, CTP, and ash). It has been proven that CFD simulations successfully predict the course of denaturation of beef proteins, and the basic composition significantly affects the course of this process [24]. These findings underscore the impact of genotype and muscle type on the extent of cooking and roasting losses during thermal processing, highlighting the need to consider these factors in optimizing heat treatment processes for goose breast meat. To adapt or extend the CFD model for optimizing thermal processing parameters for various meat or food products beyond its current scope, adjustments must be made to account for specific heat transfer properties, moisture content, and structural characteristics of the new materials. This may involve recalibrating simulation parameters, incorporating additional variables such as moisture evaporation rates, and validating the model against experimental data for each type of product. Additionally, the model might require refinement to accurately simulate complex interactions between different food matrices and heating environments [49]. The composition of goose breast meat, particularly its fat and water content, can significantly impact the accuracy of CFD simulations in predicting protein denaturation and optimizing heat treatment processes. Variations in meat composition alter the thermal properties of the meat, such as thermal conductivity, specific heat capacity, and density, which are critical parameters in CFD models. The meat's thermal conductivity is influenced by its fat and water content. Water has a higher thermal conductivity compared to fat, meaning that higher water content can lead to faster heat transfer within the meat. Conversely, increased fat content, which has lower thermal conductivity, can slow down the heat transfer process. The specific heat capacity, which is the amount of energy required to raise the temperature of a unit mass of meat by one degree Celsius, also varies with the composition. Water has a higher specific heat capacity compared to fat, so meat with higher water content requires more energy for heating, affecting the rate and uniformity of temperature rise during heat treatment. The density of meat affects the overall mass and, thus, the heat distribution within the meat during cooking. Variations in fat and water content change the density of the meat, influencing the heat absorption and distribution patterns [50; 51; 52]. To enhance the reliability of the CFD model in predicting protein denaturation and cooking loss within intricate food matrices, several additional validation steps could be implemented. Firstly, conducting experimental validation studies by comparing the model predictions with actual measurements in real-world scenarios would provide crucial validation. This involves testing the model's accuracy across a range of food matrices and cooking conditions to ensure its applicability and reliability in diverse contexts. Moreover, sensitivity analysis could be performed to assess the impact of various input parameters on the model's outputs, helping to identify critical factors influencing prediction accuracy. Furthermore, cross-validation techniques, such as splitting the dataset into training and testing subsets, could be employed to assess the model's robustness in terms of performance. Additionally, incorporating feedback from food industry experts and conducting peer reviews of the model's methodology and results would offer valuable insights and validation perspectives [53; 54; 55; 56].