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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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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Related Experiment Video

Updated: Jun 26, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Practical Workflow for Building Local Mass Spectral Libraries for Untargeted Metabolomics.

Torbjørn Norberg Myhre1, Terkel Hansen1,2, Tetiana Lutchyn3

  • 1Department of Pharmacy, UiT-The Arctic University of Norway, 9037 Tromsoe, Norway.

Metabolites
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a practical workflow for building high-quality mass spectral libraries using authentic metabolite standards. The method improves metabolite identification in untargeted metabolomics by ensuring reliable data and efficient analysis.

Keywords:
LC-MS/MSMSMLScompound discovererfragmentation spectramass spectral librarymzVault

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

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Last Updated: Jun 26, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
08:23

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Published on: August 6, 2018

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Metabolomics

Background:

  • Metabolite identification is challenging in untargeted metabolomics due to mass spectral feature ambiguity.
  • High-confidence metabolite annotation requires experimental validation with authentic standards.
  • Current methods often rely on in silico predictions or database matching, which lack sufficient reliability.

Purpose of the Study:

  • To develop a practical and scalable workflow for creating a high-quality mass spectral library.
  • To utilize a commercially available analytical standards kit for library construction.
  • To enhance the reliability and specificity of metabolite identification in metabolomics studies.

Main Methods:

  • Organized 603 metabolites into 42 mixtures based on molecular mass and logD values for optimal chromatographic separation.
  • Minimized injections by strategic mixture design while maintaining spectral quality.
  • Validated spectral data against online resources and in silico fragmentation predictions.

Main Results:

  • Successfully detected and incorporated 471 metabolites (78%) into the spectral library.
  • Demonstrated improved spectral quality and reliability compared to existing resources.
  • Enabled identification of endogenous metabolites in human serum samples using the constructed library.

Conclusions:

  • The developed workflow offers a scalable strategy for mass spectral library construction.
  • It balances spectral quality with analytical throughput for untargeted metabolomics.
  • Utilizes rational mixture design and authentic standards for reliable metabolite identification with minimized experimental effort.