Modeling buffer capacity and pH in acid and acidified foods

Area of Science:

• Food Science
• Analytical Chemistry
• Physical Chemistry

Background:

• Standard pH calculations are impractical for complex food matrices with unknown buffering components.
• Accurate pH prediction is crucial for food safety and product development, especially for acid and acidified foods.

Purpose of the Study:

• To develop and validate a pH prediction algorithm using buffer capacity (BC) models for complex food matrices.
• To enable practical pH prediction from BC data, even with unknown buffering components.

Main Methods:

• Combined buffer capacity (BC) models with a pH prediction algorithm.
• Developed numerical models in Matlab to estimate pH and buffering components.
• Validated models using laboratory solutions of acetic/citric acids and ammonia with varying salts.

Main Results:

• Linear regressions showed strong correlation (slopes 0.96-1.01) between observed and predicted pH.
• Predicted pH values were within 0.11 pH units of measured values.
• Model accurately estimated acetic acid concentration (within 6% for <400 mM).

Conclusions:

• The developed BC models and pH prediction algorithm are effective for complex food systems.
• These models can estimate pH and buffering capacity of food ingredients with unknown components.
• Applications include regulatory purposes for acid/acidified foods and product development.