ArticleAnalytical chemistry2023
Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.
Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.Analytical chemistry · 2026Article
- Ionization efficiency prediction of electrospray ionization mass spectrometry analytes based on molecular fingerprints and cumulative neutral losses.Journal of cheminformatics · 2025Article
- Machine Learning for Enhanced Identification Probability in RPLC/HRMS Nontargeted Workflows.Analytical chemistry · 2025Article
- Automated annotation of complex natural products using a modular fragmentation-based structure assembly (MFSA) strategy.Science advances · 2025Article
- Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories.Environmental science & technology · 2025Article
- High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.Environmental science & technology · 2024Review
- Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis.Environmental science & technology · 2023Review
- Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.Analytical chemistry · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a score
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.