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Updated: Mar 11, 2026

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation
Published on: April 24, 2020
Performance evaluation of automated magnetic beads extraction method for the measurement of 23 plasma steroids using
Han Chen1, Gang Xu1, Mei-Juan Zhang1
1Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Objectives:
To evaluate the performance of magnetic beads extraction method (MGE) for quantifying plasma steroids using liquid chromatography tandem mass spectrometry (LC-MS/MS).
Methods:
The evaluation encompassed calibration linearity, limit of detection (LOD), lower limit of the measuring interval (LLMI), specificity and matrix effects, trueness and recovery, intra- and inter-day precisions, repeatability, dilution consistency and stability of 23 plasma steroids extracted by MGE method were evaluated. The 23 plasma steroids were isolated and analyzed through a single sample preparation and two injections by LC-MS/MS.
Results:
All 23 steroids were successfully resolved chromatographically within 12.1 min. The automated MGE method exhibited excellent linearity (R2 > 0.995 for all analytes) with coefficients of variation (CVs) ranging from 3.36% to 9.28% at LLMI and from 1.58% to 16.53% at LOD. Additionally, the CVs and average deviation for repeatability were between 0.55% and 7.11%, and - 6.57% to 10.81%, respectively. Both intra-day precision (0.88% to 8.43%) and inter-day precision (0.98% to 6.51%) satisfied the acceptance criteria. The average deviation for trueness and recovery ranged from -13.93% to 9.06%. Moreover, specificity, matrix effect, dilution consistency, and stability were distinctly identified and conformed to guideline requirements. Notably, levels of AD, T, E1, and 17-OHP were significantly elevated in patients with polycystic ovary syndrome (PCOS) (p < 0.05).
Conclusion:
The automated MGE method demonstrates high efficiency and reliability for the simultaneous quantification of 23 plasma steroids, offering a promising solution for high-throughput analysis of steroid panels in clinical in the future.
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