Development and evaluation of an error compensating predictive data-processing method for chromatography based on the leading and trailing edges of chromatographic peaks with emphasis of liquid chromatography
Abstract
The development and evaluation of a more rugged error-compensating predictive data-processing method for chromatographic data is described. This predictive approach utilizes the chromatographic steady-state (or equilibrium) signals corresponding to sample concentrations to quantify usual chromatographic peaks. The values of steady-state signals are extrapolated by fitting suitable mathematical models to the time-dependent data from the leading and/or trailing edges of chromatographic peaks obtained in the usual way with the usual sample volumes. Suitable mathematical models and corresponding algorithms are developed by a combination of chromatographic theory and experimentation. The performance characteristics of the new curve-fitting method is evaluated by comparing with the conventional peak-height and peak-area approaches for the same sets of liquid chromatograms including well resolved and partially resolved peaks. A physical quantity, called relative error coefficient, is used to quantitatively compare the different options in terms of their dependencies on experimental variables. Chromatographic variables studied include sample volume, flow rate, capacity factor, and concentration. The more rugged predictive method can offer up to 350-fold improvement in error coefficient for injection volume over peak-height and peak-area methods, 110-fold improvement for flow rate over peak-height and peak-area methods and 12-fold improvement for capacity factor for peak-height method.
Degree
Ph.D.
Advisors
Pardue, Purdue University.
Subject Area
Analytical chemistry
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