Buffering capacity is a characteristic of foods to resist changes in pH, which is important to consider in gastric digestion as it will impact physicochemical breakdown of food. A standardized method to measure and quantify buffering capacity in the context of digestion is needed to improve in vitro digestion studies by providing a better estimation of acid secretions and subsequent protein digestibility. The objective of this study was to develop a method to measure buffering capacity in the context of digestion and develop a regression model to predict buffering capacity using protein-based model foods. Buffering capacity was analyzed by titrating 0.16 M HCl to egg and whey-protein based dispersions and gels of varying protein content and particle size and recording the pH after each addition. Calculated parameters from buffering capacity experiments included total acid added, area under the curve, total buffering capacity, relative [H+] increase, and lag phase. A regression model was developed to predict each buffering capacity parameter based on protein concentration, specific surface area, aspartic acid and glutamic acid content. Results showed that higher protein concentration and smaller surface area resulted in higher buffering capacity. A validation dataset was used to evaluate the goodness of fit of the model to the data with different protein concentrations, surface area or protein source. Results indicated that total buffering capacity and lag phase parameters can be used to quantify buffering capacity of protein gels in the context of digestion, since they provided a good fit to the observational and validation data sets.