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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
1Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany.
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.
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.
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