DEDUCTIVE PREDICTION OF MEASUREMENT PRECISION FROM SIGNAL AND NOISE IN LIQUID-CHROMATOGRAPHY

The aim of this paper is to propose and experimentally prove a probability theory to predict the relative standard deviation of repeated measurements in high-performance liquid chromatography (HPLC). The baseline drift in HPLC, often formulated as 1/f noise, is approximated by a mixed random process comprising white noise and Markov process. The standard deviations (SD), w, of the white noise and the SD, m, and retention parameter, rho, of the Markov process completely specify the stochastic properties of the respective random processes and are determined from the power spectral density of the baseline by least-squares curve fitting. All the required parameters for the prediction are the noise parameters, w, m, and rho, working domain of signal processing (here, integration), area of a target peak in the domain, and constant error (mainly originating from sample injection). Every parameter is uniquely determined from experimental data, and no arbitrary constants are involved in the theoretical prediction. The prediction is shown to be excellent for peaks with various areas, heights, and widths over a wide concentration range in the HPLC analysis for some aromatic compounds.