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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Open-source feature detection for non-target LC-MS analytics.

Christian Dietrich1, Arne Wick1, Thomas A Ternes1

  • 1Federal Institute of Hydrology, Koblenz, Germany.

Rapid Communications in Mass Spectrometry : RCM
|October 6, 2021
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Summary
This summary is machine-generated.

A new R and C++ algorithm automates chromatographic peak picking for high-resolution liquid chromatography-high-resolution mass spectrometry (LC-HRMS) non-target screening. This method efficiently processes large environmental science datasets with moderate accuracy and speed.

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Area of Science:

  • Environmental sciences
  • Analytical chemistry
  • Mass spectrometry

Background:

  • Non-target screening is crucial for environmental analysis.
  • High-resolution mass spectrometry (HRMS) generates large datasets.
  • Sophisticated software is needed for HRMS data analysis.

Purpose of the Study:

  • To develop an automated algorithm for chromatographic peak picking in LC-HRMS data.
  • To provide a reliable software solution for non-target screening in environmental science.

Main Methods:

  • Data processing using an open, XML-based format.
  • Automated peak assignment with the R programming language.
  • Algorithm developed in R and C++ for m/z-centroided data.

Main Results:

  • A straightforward algorithm for chromatographic peak extraction.
  • Successful processing of raw data from three different LC-HRMS systems.
  • Demonstrated reasonable False Positives/Negatives and moderate computation times.

Conclusions:

  • The algorithm enables automated peak picking for non-target LC-MS data.
  • Validated on diverse HRMS data, showing effective performance.
  • Offers a valuable tool for environmental data analysis.