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

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, r², between the observed and theoretical distributions of the area measurements from 0.373 to 0.966. The statistical confidence levels of the theoretically predicted relative SDs for cefaclor were verified by comparing them with those obtained by repeated experiments (r² = 0.989). The limits of detection (= 3.3 × SD = 18.0 µg/L) and quantitation (= 10 × SD = 54.7 µg/L) for cefaclor have signal-to-noise ratios close to the commonly adopted values, 3 and 10, respectively.