Location: Food Science Research
Title: Semi-mechanistic partial buffer approach to modeling pH, the buffer properties, and the distribution of ionic species in complex solutions Authors
|Dougherty, Daniel - MICHIGAN STATE UNIV|
|Da Conceicao Neta, Edith - NC STATE UNIVERSITY|
|Lubkin, Sharon - NC STATE UNIVERSITY|
Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 2, 2006
Publication Date: August 9, 2006
Repository URL: http://hdl.handle.net/10113/663
Citation: Dougherty, D.P., Da Conceicao Neta, E.R., McFeeters, R.F., Lubkin, S.R., Breidt, F. 2006. Semi-mechanistic partial buffer approach to modeling pH, the buffer properties, and the distribution of ionic species in complex solutions. Journal of Agricultural and Food Chemistry. 54:6021-6029. Interpretive Summary: Adjustment of pH and acidity is important in biological and food sciences. It has not been possible to predict how much of an acid or base to add to adjust to a target pH in foods or complex biological systems. This paper describes a mathematical approach to make such predictions, a software program to do the complex calculations required to make predictions, and validates the utility of the software by making comparisons of predictions made by the software to experimental pH adjustments made in cucumber slurries. The ability to make these predictions can be useful in a wide range of biological research and in the development of food products.
Technical Abstract: In many biological science and food processing applications, it is very important to control or modify pH. However, the complex, unknown composition of biological media and foods often limits the utility of purely theoretical approaches to modeling pH. This paper is to provides general formulae and efficient algorithms for predicting the pH, titration, species concentrations, buffer capacity, and ionic strength of buffer solutions containing both defined and undefined components. A flexible, semi-mechanistic, partial buffering (SMPB) approach is presented which uses local polynomial regression to model the buffering influence of complex or undefined components in a solution, while identified components of known concentration are modeled using expressions based on extensions of the standard acid-base theory. The SMPB method is implemented in a freeware package, pHTools, for use with Matlab. We validated the predictive accuracy of these methods by using strong acid titrations of cucumber slurries to predict the amount of a weak acid required to adjust pH to selected target values.