ArticleAnalytical chemistry2021
From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Pseudoenhanced Peak Efficiency in Liquid Chromatography-High Resolution Mass Spectrometry Using Multiple Second Derivatives.Journal of the American Society for Mass Spectrometry · 2026Article
- Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun Mass Spectrometry Data Integration.Analytical chemistry · 2025Article
- A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.Analytical and bioanalytical chemistry · 2025Article
- High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.Environmental science & technology · 2024Review
- Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.Analytical chemistry · 2023Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
- Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data.Journal of cheminformatics · 2023Article
- Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra.Analytical and bioanalytical chemistry · 2022Article
- Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features.Analytical science advances · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the
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Registered trials
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