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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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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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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 electrospray 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...
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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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Updated: Jun 21, 2025

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.

Yunjia Lai1, Jeremy P Koelmel2, Douglas I Walker3

  • 1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States.

Environmental Science & Technology
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

High-resolution mass spectrometry (HRMS) advances human exposome research by enabling broad chemical profiling. Further development of analytical and computational tools is crucial for integrating exposomic data with health outcomes.

Keywords:
chemical spacechromatographyenvironmental exposuresexposomehigh-resolution mass spectrometrymetabolomicsnon-targeted analysistoxicants

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Area of Science:

  • Environmental Health Sciences
  • Analytical Chemistry
  • Toxicology

Background:

  • The human exposome, encompassing all environmental exposures, is key to understanding disease etiology.
  • High-resolution mass spectrometry (HRMS) is a powerful tool for profiling chemicals and biomolecules.
  • Current HRMS exposomics methods face challenges in data analysis and integration.

Purpose of the Study:

  • To review state-of-the-art HRMS technologies for exposome research.
  • To discuss analytical workflows, informatic pipelines, and challenges in HRMS-based exposomics.
  • To provide a reference for advancing exposomic approaches in health sciences.

Main Methods:

  • Literature review of HRMS technologies and exposomic approaches.
  • Survey of current analytical workflows and computational pipelines.
  • Discussion of gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS) applications.

Main Results:

  • HRMS enables non-targeted, data-driven exposome-wide profiling.
  • New analytical and computational infrastructures are needed for scalable, harmonized workflows.
  • Benchmarking platforms are proposed for expanding chemical space coverage.

Conclusions:

  • HRMS is pivotal for advancing exposome research and understanding health impacts.
  • Streamlined, scalable, and harmonized workflows are essential for longitudinal exposome tracking.
  • Integration of exposomic data with multi-omics is critical for health-oriented inferences.