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Updated: Dec 25, 2025

Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry

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Quantitative Evaluation of Ion Chromatogram Extraction Algorithms.

Rob Smith¹, Annika R Tostengard¹

¹Department of Computer Science, University of Montana, 430 Stephens Ave, Missoula, Montana 59801, United States.

Journal of Proteome Research

|March 29, 2020

Summary

This summary is machine-generated.

This study quantitatively evaluates mass spectrometry data analysis algorithms using manually curated ground truth data. Results show algorithm performance varies by parameter, but optimization recovers thousands of true positive extracted ion chromatograms (XICs).

Keywords:

LC−MS algorithm bioinformatics data mining mass spectra mass spectrometry parameter optimization peak detection peak picking proteomics

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Area of Science:

Proteomics
Mass Spectrometry
Computational Biology

Background:

Extracted ion chromatograms (XICs) are crucial for mass spectrometry data analysis.
Numerous algorithms exist for XIC analysis, but their individual performance lacks quantitative evaluation.
A lack of ground truth datasets has hindered objective assessment of XIC algorithm performance.

Purpose of the Study:

To quantitatively evaluate the performance of popular XIC algorithms.
To establish a benchmark for XIC algorithm assessment using a manually curated dataset.
To identify optimal parameter settings for maximizing true positive XIC recovery.

Main Methods:

Utilized a manually curated dataset of 48 human proteins across six abundance orders of magnitude as ground truth.
Applied popular XIC algorithms (MaxQuant, MZMine2, XCMS) to the ground truth dataset.
Performed a grid search of algorithm parameters to optimize performance.

Main Results:

Significant variation in algorithm performance was observed across different parameter settings.
Optimized parameter settings enabled most algorithms to recover over 10,000 true positive XICs.
The study provides the first quantitative performance assessment of XIC algorithms.

Conclusions:

Quantitative evaluation is essential for understanding XIC algorithm strengths and weaknesses.
Manual curation of MS1 data provides a valuable resource for algorithm benchmarking.
Optimizing algorithm parameters is critical for accurate and comprehensive mass spectrometry data analysis.