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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Related Experiment Video

Updated: Jan 24, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data.

Dominik Reinhold1, Harrison Pielke-Lombardo2, Sean Jacobson3

  • 1PPD, Wilmington, NC, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 24, 2019
PubMed
Summary

This study presents a general pipeline for processing untargeted mass spectrometry data in metabolomics, focusing on addressing missing values and batch effects. It offers guidance on data processing steps and diagnostic tools for robust analysis.

Keywords:
FilteringImputationMass spectrometryMetabolomicsNormalizationPre-analyticProcessingTechnical replicatesUntargeted

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Area of Science:

  • Metabolomics
  • Biochemistry
  • Systems Biology

Background:

  • Metabolomics characterizes small molecule metabolites, linking genotype, environment, and phenotype.
  • Untargeted mass spectrometry generates complex data with challenges like missing values and batch effects.
  • Robust data processing is crucial for accurate biological interpretation in metabolomics.

Purpose of the Study:

  • To present a generalizable pipeline for processing untargeted mass spectrometry metabolomics data.
  • To address common data challenges, specifically missing values and batch effects.
  • To review diagnostic tools and criteria for data processing decisions and normalization effectiveness.

Main Methods:

  • A step-by-step pipeline for metabolite abundance matrices (metabolites x samples).
  • Includes summarizing replicates, filtering, imputation, transformation, and normalization.
  • Emphasizes method and parameter selection based on data characteristics and research questions.

Main Results:

  • A structured approach to mitigate missing values and batch effects in metabolomics data.
  • Guidance on selecting appropriate methods and parameters for each processing step.
  • Review of diagnostic tools for assessing data quality and normalization efficacy.

Conclusions:

  • The proposed pipeline provides a framework for reproducible metabolomics data processing.
  • Diagnostic tools aid in informed decision-making throughout the analysis.
  • The study highlights the importance of careful data handling for reliable biological insights.