Innovative Implementation of Computational Fluid Dynamics in Proteins Denaturation Process Prediction in Goose Breast Meat and Heat Treatment Processes Optimization

Abstract

This study aimed to calculate the optimal thermal processing parameters for goose meat using CFD simulation. CFD provides a precise determination of heat treatment conditions by predicting protein denaturation and mass loss, leading to higher quality and improved sensory experience and, thus, acceptance of products. Accurate calculation of these conditions reduces energy losses and enhances process efficiency in the food industry. This study focused on the prediction of protein denaturation and cooking loss in goose breast meat during roasting. Specific CFD techniques, including conjugate heat transfer and phase change models, were utilized to ensure accuracy in protein denaturation prediction. These models accounted for variations in meat composition, such as fat and water content across different samples, which improved the accuracy of the predictions. Optimal conditions were determined using a mathematical model. These conditions were 164.65 °C, 63.58% humidity, and a fan rotation of 16.59 rpm for 2000 s. The myosin, collagen, and actin denaturation levels, as well as cooking loss, closely matched predicted values. The findings show that CFD is a valuable method for evaluating protein denaturation and cooking loss in goose breast meat, potentially improving product quality and consistency in gastronomy and the meat industry. This innovative optimization method enhances food production efficiency and elevates sensory characteristics, physicochemical properties, and nutritional value, contributing to consumer satisfaction and market competitiveness. The model proposed in this paper can be adapted to predict denaturation in other types of meat or food products with necessary modifications, offering broad applicability. Potential limitations of using CFD in protein denaturation prediction in complex food matrices include the need for detailed compositional data and computational resources, which can be addressed in future research.