Evaluation of a predictive kinetic approach to the quantitation of creatinine in serum
Abstract
The evaluation of a predictive kinetic method for the determination of creatinine in serum using alkaline picrate and enzymatic reaction systems is described. The predictive method, which is based on a first-order model for both systems, is applied to absorbance data collected as a function of time. For the alkaline picrate reaction system, effects of reaction variables (alkalinity, picreate concentration, and temperature) on sensitivity and rate constants were quantified for a range of experimental conditions. A predictive method was evaluated for various reagent compositions. For the enzymatic system, reagent composition was modified so that the observed response was pseudo-first-order with respect to creatinine. A predictive method was evaluated under several sets of experimental conditions. As part of these evaluations, results were compared with results from a number of other data-processing methods applied to the same data sets. Other kinetic methods included some which were and some which were not expected to compensate for variations in the rate constant between samples or between runs, with results from equilibrium-based methods, and with results from liquid chromatography. The abilities of each data-processing method to compensate for errors caused by changing the temperature for alkaline picrate reactions and by changing the enzyme activity in the rate-limiting step of the enzymatic reaction were quantified. For both systems, a predictive method applied over three half-lives produced error coefficients similar to those for an equilibrium-based method.
Degree
Ph.D.
Advisors
Pardue, Purdue University.
Subject Area
Analytical chemistry
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