An automated assessment system of limits of detection and quantitation in gradient high-performance liquid chromatography with ultraviolet detection.

A previous paper of this series of study put forward a basic model of an automated system for predicting detection limits and showed its application to a simple example of isocratic high-performance liquid chromatography (HPLC). This paper describes an expansion of the basic system into gradient HPLC. The most serious problem with the expansion is a long-term variation in backgrounds, called gradient baseline drifts, which in theory cannot be covered by a noise model (stationary random process) of the original system. This paper demonstrates that the above problem can be solved with modifying a parametrization procedure of the noise model. The essential role of the system is to predict the standard deviation (SD) of measurements at low concentrations from a chromatogram without repeated measurements of real samples. Laboratory-made software enables the automated assessment of the limits of detection and quantitation for each of chromatographically separated signals in a single run. Simulated background noise which consists of the stationary noise model with linear slopes is used to confirm the accuracy and reproducibility of the automated prediction. A gradient HPLC determination for cefaclor is taken as an example. The parametrization modification improves the correlation coefficient, r

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.