Modeling the formulation pH of elderberry syrup with multiple weak acids.

Area of Science:

• Food Chemistry
• Food Science
• Chemical Engineering

Background:

• Acidified foods like elderberry syrup require precise pH control for safety and quality.
• Understanding ingredient buffering capacity is crucial for predicting final product pH.
• Existing methods for pH prediction in complex food matrices can be limited.

Purpose of the Study:

• To develop and validate buffer models for predicting the pH of acidified elderberry syrup.
• To quantify the buffering capacity (tBeta) of individual food ingredients.
• To assess the influence of ingredient composition on the final product pH.

Main Methods:

• Defined total ingredient buffering (tBeta) as the area under the buffer capacity curve (pH 2-12).
• Measured tBeta for key elderberry syrup ingredients like citric acid, elderberry juice, malic acid, ascorbic acid, and lemon juice.
• Developed and applied combined buffer models using Matlab software to predict syrup pH based on ingredient concentrations and tBeta values.

Main Results:

• Citric acid, elderberry juice, and malic acid exhibited the highest buffering capacities (tBeta values).
• Predicted syrup pH (2.78) closely matched observed pH (2.67), with a difference of 0.11 pH units.
• Regression analysis of 16 model formulations showed an excellent fit between observed and predicted pH, with a root mean square error of 0.076 pH units.

Conclusions:

• Buffer models utilizing ingredient tBeta values provide accurate in silico estimates of pH for acidified food formulations.
• The tBeta metric is valuable for identifying ingredients with the most significant impact on pH.
• These models can enhance product development and safety assessments for acidified foods.