Simple correction improving long-term reproducibility of HPLC-MS

Area of Science:

• Analytical Chemistry
• Chromatography
• Mass Spectrometry

Background:

• Chromatographic peak areas in high-performance liquid chromatography-mass spectrometry (HPLC-MS) experiments exhibit variability, impacting reproducibility and potentially introducing bias.
• Sensitivity changes in electrospray ionization (ESI) are a likely cause of this variability, ranging from 20-40% for various components.
• Existing peak area correction methods often fail to account for these ESI-driven sensitivity fluctuations.

Purpose of the Study:

• To develop and validate a novel correction algorithm for chromatographic peak areas.
• To address the limitations of current methods in handling ESI sensitivity variations.
• To improve the reproducibility and reduce bias in HPLC-MS data.

Main Methods:

• Characterization of peak area variability in extensive HPLC-MS experimental series.
• Modeling peak area changes using polynomial functions, identifying a fourth-order polynomial as most suitable.
• Development of a simple correction algorithm based on polynomial fitting.

Main Results:

• The proposed polynomial fitting correction significantly enhanced reproducibility, reducing average variability from 12% to 3% in well-reproducible experiments.
• In experiments with larger random errors, such as nano-ESI, the correction still provided essential bias reduction, improving reproducibility from 15% to 12%.
• The method effectively models and corrects for ESI-induced sensitivity drifts.

Conclusions:

• Polynomial fitting offers a robust and simple method for correcting chromatographic peak area variability in HPLC-MS.
• This approach is crucial for mitigating bias and enhancing data reliability, especially in long-term or high-throughput studies.
• The developed algorithm provides a valuable tool for improving the accuracy and reproducibility of quantitative LC-MS analyses.