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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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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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This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
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High-Resolution Mass Spectrometry (HRMS)01:15

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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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Updated: Sep 3, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.

Ethan King1, Richard Overstreet2, Julia Nguyen1

  • 1Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Journal of Chemical Information and Modeling
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a method to enhance mass spectrometry-mass spectrometry (MS/MS) spectral libraries by statistically interpolating missing collision energy data. This improves the accuracy of identifying small molecules and metabolites, even with limited experimental spectra.

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Spectroscopy

Background:

  • Tandem mass spectrometry (MS/MS) is crucial for identifying small molecules and metabolites.
  • MS/MS spectrum variability poses challenges for building standardized reference libraries.
  • Existing spectral libraries often lack comprehensive data across various acquisition parameters.

Purpose of the Study:

  • To develop a method for augmenting existing MS/MS spectral libraries.
  • To improve the identification accuracy of small molecules and metabolites by addressing data gaps.
  • To enhance the utility of MS/MS reference libraries through statistical interpolation.

Main Methods:

  • Statistically interpolating spectra at unreported collision energies.
  • Augmenting experimental spectra data sets with interpolated data.
  • Validating interpolated spectra against experimental data from the same instrument.

Main Results:

  • Highly accurate spectral approximations can be interpolated from as few as three experimental spectra.
  • Interpolated spectra are consistent with true spectra acquired on the same instrument.
  • Supplementing spectral databases with interpolated spectra consistently improves identification accuracy across various instruments and precursor types.

Conclusions:

  • The interpolation method significantly improves spectral matching, identifying ~10% more spectra correctly in large datasets.
  • This approach enhances spectral matching across different instrument types and collision energies.
  • The method provides a quick and adept tool for improving spectral matching when reference libraries are insufficient.