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qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass

Max Reuschenbach1,2, Felix Drees1,2, Torsten C Schmidt1,2,3

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Analytical Chemistry
|September 1, 2023
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Summary
This summary is machine-generated.

We developed qBinning, a new algorithm for non-target screening (NTS) data processing. It automatically generates extracted ion chromatograms (EICs) using statistical methods, improving transparency and reliability in mass spectrometry analysis.

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Area of Science:

  • Analytical Chemistry
  • Mass Spectrometry
  • Data Science

Background:

  • Non-target screening (NTS) generates vast, complex datasets.
  • Current NTS data processing relies on user-dependent, labor-intensive optimization.
  • The impact of raw data quality on NTS results remains poorly understood.

Purpose of the Study:

  • To introduce qBinning, a novel algorithm for automated extracted ion chromatogram (EIC) construction.
  • To provide a user-independent method for processing NTS data.
  • To enhance the transparency and reliability of NTS data analysis.

Main Methods:

  • Development of the qBinning algorithm based on statistical principles.
  • Implementation of a data quality scoring system (DQSbin) for EICs.
  • Evaluation of qBinning's performance in constructing EICs without user parameters.

Main Results:

  • qBinning successfully constructs EICs using statistical principles, eliminating the need for user-defined parameters.
  • The DQSbin scoring system provides reliable feedback on EIC quality and mass classification probability.
  • The algorithm demonstrates improved transparency in NTS data processing.

Conclusions:

  • qBinning represents a significant advancement in automating NTS data analysis.
  • The statistical approach and DQSbin score enhance the understanding and reliability of NTS results.
  • This method contributes to more transparent and efficient high-resolution mass spectrometry data interpretation.