Development and evaluation of an error-compensating predictive data-processing method for liquid chromatography

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Abstract
This paper describes an alternative data-processing approach for liquid chromatographic responses. Transient data from the leading edges of chromatographic peaks for nonsaturating amounts of sample are used with a suitable mathematical model and curve-fitting program to predict the steady-state response that would be measured if sufficient sample were used to saturate the system. Results obtained by this approach are compared with peak-height and peak-area options by using aspirin as an analyte. For aspirin concentrations from 0.6 to 5.0mmol/L, each data-processing option yields linear calibration plots for each of several sample volumes from 50 to 100 microL and flow rates from 1.0 to 3.0 mL/min. As expected, the predictive option yielded lower dependency on sample volume (20-30-fold improvement) and flow rate (10-fold improvement) than the peak-height and peak-area options. However, the peak-height option provided slightly better (approximately 2-fold) calibration statistics.

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