Multi-targeted interference-free determination of ten β-blockers in human urine and plasma samples by alternating trilinear decomposition algorithm-assisted liquid chromatography–mass spectrometry in full scan mode: Comparison with multiple reaction monitoring

Abstract

β-blockers are the first-line therapeutic agents for treating cardiovascular diseases and also a class of prohibited substances in athletic competitions. In this work, a smart strategy that combines three-way liquid chromatography–mass spectrometry (LC–MS) data with second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm was developed for simultaneous determination of ten β-blockers in human urine and plasma samples. This flexible strategy proved to be a useful tool to solve the problems of overlapped peaks and uncalibrated interferences encountered in quantitative LC–MS, and made the multi-targeted interference-free qualitative and quantitative analysis of β-blockers in complex matrices possible. The limits of detection were in the range of 2.0×10−5–6.2×10−3μgmL−1, and the average recoveries were between 90 and 110% with standard deviations and average relative prediction errors less than 10%, indicating that the strategy could provide satisfactory prediction results for ten β-blockers in human urine and plasma samples only using liquid chromatography hyphenated single–quadrupole mass spectrometer in full scan mode. To further confirm the feasibility and reliability of the proposed method, the same batch samples were analyzed by multiple reaction monitoring (MRM) method. T-test demonstrated that there are no significant differences between the prediction results of the two methods. Considering the advantages of fast, low-cost, high sensitivity, and no need of complicated chromatographic and tandem mass spectrometric conditions optimization, the proposed strategy is expected to be extended as an attractive alternative method to quantify analyte(s) of interest in complex systems such as cells, biological fluids, food, environment, pharmaceuticals and other complex samples.