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Local Asymmetric Gaussian Fitting Algorithm for Enhanced Peak Detection of Liquid Chromatography-High Resolution Mass

Shengsi Zou1, Qingxiao Cui1, Jinyue Liu1

  • 1School of Chemistry and Molecular Engineering & Research Center of Analysis and Test, East China University of Science and Technology, Shanghai 200237, China.

Analytical Chemistry
|May 6, 2025
PubMed
Summary

This study introduces a novel Local Asymmetric Gaussian Fitting (LAGF) algorithm for enhanced feature detection in liquid chromatography-mass spectrometry (LC-MS). LAGF improves accuracy and efficiency in compound identification for metabolomics analysis.

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Biotechnology

Background:

  • Feature detection is critical for liquid chromatography-mass spectrometry (LC-MS) data preprocessing.
  • Existing methods often suffer from complex parameter tuning and high false positive rates, complicating compound identification.

Purpose of the Study:

  • To introduce a novel algorithm, Local Asymmetric Gaussian Fitting (LAGF), for improved peak detection in LC-MS.
  • To enhance feature detection efficiency and accuracy in metabolomics workflows.

Main Methods:

  • Developed the Local Asymmetric Gaussian Fitting (LAGF) algorithm for peak detection.
  • Integrated LAGF with a "data points bins" approach for extracted ion chromatogram (EIC) extraction using 1 Da bins.
  • Automated determination of peak parameters (center, height, standard deviations) for adaptive peak modeling.
  • Filtered features using a goodness-of-fit threshold of 0.5.

Main Results:

  • LAGF demonstrated superior performance compared to conventional tools in terms of determination coefficient (R²) and relative standard deviation for peak areas.
  • The algorithm showed high efficiency and accuracy in analyzing standard mixtures and serum samples across different LC modes.
  • Reduced computational time through 1 Da data points binning, enabling efficient batch metabolomics analysis.

Conclusions:

  • LAGF offers a robust and efficient solution for feature detection in LC-MS, minimizing parameter complexity and false positives.
  • The open-source availability of LAGF with an interactive interface facilitates its adoption in both targeted and nontargeted LC-MS analyses.
  • LAGF enhances compound identification accuracy and overall data processing efficiency in metabolomics.