Dept. of Biological and Agricultural Engineering, 1308 Bainer Hall University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA.
Food Funct. 2019 Sep 1;10(9):6074-6087. doi: 10.1039/c9fo01160a. Epub 2019 Sep 6.
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.
缓冲能力是食物抵抗 pH 值变化的特性，这在胃消化中很重要，因为它会影响食物的理化分解。需要一种标准化的方法来测量和量化消化过程中的缓冲能力，以改善体外消化研究，更好地估计胃酸分泌和随后的蛋白质消化率。本研究的目的是开发一种测量消化过程中缓冲能力的方法，并开发一种使用基于蛋白质的模型食品预测缓冲能力的回归模型。通过滴定 0.16 M HCl 到不同蛋白质含量和粒径的卵清蛋白和乳清蛋白分散体和凝胶中来分析缓冲能力，记录每次添加后的 pH 值。缓冲能力实验中计算的参数包括添加的总酸量、曲线下面积、总缓冲能力、相对 [H] 增加量和滞后期。开发了一个回归模型，根据蛋白质浓度、比表面积、天冬氨酸和谷氨酸含量预测每个缓冲能力参数。结果表明，较高的蛋白质浓度和较小的表面积导致较高的缓冲能力。使用验证数据集评估模型对不同蛋白质浓度、表面积或蛋白质来源数据的拟合优度。结果表明，总缓冲能力和滞后期参数可用于量化消化过程中蛋白质凝胶的缓冲能力，因为它们与观测数据集和验证数据集有很好的拟合度。
