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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Automated metabolite formula ranking using formula subset analysis for LC-MS/MS-based metabolomics.

Ke-Shiuan Lynn1

  • 1Department of Mathematics, Fu Jen Catholic University, Room MA322, New Taipei City, 24205, Taiwan ROC. 128171@mail.fju.edu.tw.

Metabolomics : Official Journal of the Metabolomic Society
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

Formula subset analysis (FSA) efficiently ranks chemical formula candidates for MS/MS spectra in untargeted metabolomics. This method significantly improves metabolite identification by prescreening candidates, even without database reliance.

Keywords:
Formula rankingLC-MS/MSMetabolomicsMother-daughter relationship

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Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
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Area of Science:

  • Metabolomics
  • Mass Spectrometry
  • Computational Chemistry

Background:

  • Metabolite identification is a critical challenge in untargeted liquid chromatography-tandem mass spectrometry (LC-MS) metabolomics.
  • Existing methods struggle when metabolites are not present in tandem mass spectrometry (MS/MS) databases.

Purpose of the Study:

  • To develop and evaluate Formula Subset Analysis (FSA), a novel approach for prescreening and ranking chemical formula candidates from MS/MS spectra.
  • To address the bottleneck in metabolite identification for untargeted metabolomics.

Main Methods:

  • FSA computes mother-daughter relationships (MDRs) between precursor and fragment ions.
  • Characteristic fragments (CFs) with unique MDRs are identified.
  • Precursor formula candidates are ranked based on the number of MDRs.

Main Results:

  • FSA correctly identified the top chemical formula candidate in 85.28% of cases and top-5 in 97.35% across 30,690 spectra.
  • The average processing time per spectrum was only 0.024 seconds.
  • FSA demonstrated high accuracy without requiring training data or MS/MS databases.

Conclusions:

  • FSA offers significant advantages in speed, expandability, and applicability for untargeted LC-MS metabolomics.
  • While not using structural information, its performance makes it ideal for prescreening metabolite candidates.
  • The method is adaptable to various chemical elements and species identification.