This study investigated heat-induced protein aggregation in skim camel milk by monitoring changes in the volume-weighted mean particle size (d4,3) during isothermal heating (60–90 °C, up to 60 min, four temperature levels and 25 time–temperature conditions). Pronounced increases in d4,3 with both time and temperature confirmed significant thermal aggregation. The reaction kinetics were described using a generalized exponential growth model, which fitted well at intermediate temperatures (e.g., coefficient of determination (R2) = 0.901 at 70 °C and 0.959 at 80 °C) but deviated at the lower (60 °C) and upper (90 °C) extremes, reflecting more complex behavior. Arrhenius analysis of the rate constant yielded an activation energy of 50.61 kJ mol−1, lower than values typically reported for bovine milk systems, indicating that camel milk proteins require less thermal input to aggregate. In parallel, a machine learning model implemented as an artificial neural network (ANN) predicted d4,3 from time-temperature inputs with high accuracy (R2 > 0.97 across training, validation, and testing), capturing nonlinear patterns without mechanistic assumptions. Together, the kinetic and ANN approaches provide complementary insights into the heat sensitivity of camel milk proteins and offer predictive tools to support the optimization of thermal processing, formulation, and quality control in dairy applications.
Nazir et al. (Wed,) studied this question.