U.S. Department of Agriculture, Agricultural Research Service, SEA, Food Science Research Unit, NC State University, 322 Schaub Hall, Box 7624, Raleigh, NC, 27695, U.S.A.
Department of Food, Bioprocessing and Nutrition Sciences, NC State University, 400 Dan Allen Drive, Raleigh, NC, 27695, U.S.A.
J Food Sci. 2020 Apr;85(4):918-925. doi: 10.1111/1750-3841.15091. Epub 2020 Mar 21.
Standard ionic equilibria equations may be used for calculating pH of weak acid and base solutions. These calculations are difficult or impossible to solve analytically for foods that include many unknown buffering components, making pH prediction in these systems impractical. We combined buffer capacity (BC) models with a pH prediction algorithm to allow pH prediction in complex food matrices from BC data. Numerical models were developed using Matlab software to estimate the pH and buffering components for mixtures of weak acid and base solutions. The pH model was validated with laboratory solutions of acetic or citric acids with ammonia, in combinations with varying salts using Latin hypercube designs. Linear regressions of observed versus predicted pH values based on the concentration and pK values of the solution components resulted in estimated slopes between 0.96 and 1.01 with and without added salts. BC models were generated from titration curves for 0.6 M acetic acid or 12.4 mM citric acid resulting in acid concentration and pK estimates. Predicted pH values from these estimates were within 0.11 pH units of the measured pH. Acetic acid concentration measurements based on the model were within 6% accuracy compared to high-performance liquid chromatography measurements for concentrations less than 400 mM, although they were underestimated above that. The models may have application for use in determining the BC of food ingredients with unknown buffering components. Predicting pH changes for food ingredients using these models may be useful for regulatory purposes with acid or acidified foods and for product development. PRACTICAL APPLICATION: Buffer capacity models may benefit regulatory agencies and manufacturers of acid and acidified foods to determine pH stability (below pH 4.6) and how low-acid food ingredients may affect the safety of these foods. Predicting pH for solutions with known or unknown buffering components was based on titration data and models that use only monoprotic weak acids and bases. These models may be useful for product development and food safety by estimating pH and buffering capacity.
标准离子平衡方程可用于计算弱酸和弱碱溶液的 pH 值。对于包含许多未知缓冲成分的食物，这些计算在分析上是困难的或不可能的，因此在这些系统中进行 pH 值预测是不切实际的。我们将缓冲能力 (BC) 模型与 pH 值预测算法相结合，以便能够根据 BC 数据预测复杂食品基质中的 pH 值。使用 Matlab 软件开发了数值模型，以估算弱酸和弱碱溶液混合物的 pH 值和缓冲成分。使用拉丁超立方设计，使用实验室配制的乙酸或柠檬酸与氨的混合物以及不同盐的组合对 pH 模型进行了验证。基于溶液成分的浓度和 pK 值，对观察到的 pH 值与预测 pH 值进行线性回归，得到的斜率在有盐和无盐的情况下分别为 0.96 和 1.01。根据 0.6 M 乙酸或 12.4 mM 柠檬酸的滴定曲线生成 BC 模型，得到酸浓度和 pK 值的估算值。根据这些估算值预测的 pH 值与测量的 pH 值相差 0.11 pH 单位。与高效液相色谱测量相比，模型中乙酸浓度的测量值在 400 mM 以下的准确度在 6%以内，尽管在该浓度以上则低估了浓度。这些模型可能适用于确定具有未知缓冲成分的食品成分的 BC。使用这些模型预测食品成分的 pH 值变化可能对监管机构和酸或酸化食品的制造商有用，也有助于产品开发。实际应用：缓冲能力模型可能使酸和酸化食品的监管机构和制造商受益，以确定 pH 值稳定性（低于 pH 4.6）以及低酸食品成分如何影响这些食品的安全性。基于滴定数据和仅使用一元弱酸和弱碱的模型，可以预测具有已知或未知缓冲成分的溶液的 pH 值。这些模型可用于产品开发和食品安全，以估算 pH 值和缓冲能力。
