Optimizing Thermal Processing for Mozzarella Cheese Analogue: A Response Surface Approach to Enhance Techno-Functional Quality

Cheese analogues are widely studied for composition and function, but limited work has focused on optimizing processing factors like cooking temperature and time, which strongly affect the techno-functional performance of mozzarella cheese analogues (MCA). This study optimized MCA processing using response surface methodology (RSM) to assess the effects of cooking temperature and time on physicochemical and techno-functional properties. A factorial experiment design produced thirteen runs at cooking temperatures (80-90 °C) cooking time (8–15 min) using thermomix. Lack of fit was non-significant (p > 0.05), confirming model adequacy with R2 > 80%. Results showed that higher cooking temperature and time (p < 0.01) increased hardness and cohesiveness, while reducing moisture, pH, adhesiveness, meltability, stretchability, and fat leakage. Optimized conditions (85 °C/11.55 min) produced MCA with desired quality: moisture (51.70%), pH (6.24), hardness (24.71 N), and stretchability (21.87 cm). PCA (96.58%) confirmed strong associations between physicochemical and techno-functional traits, highlighting the influence of processing on overall product quality. Electron micrographs confirmed a more uniform microstructure in MCA samples produced under optimized conditions (85 °C/11.55 min). This study shows that processing conditions strongly influence MCA techno-functional properties and provides a validated optimization framework, addressing a key gap in cheese analogue research.