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Related Concept Videos

Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

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The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
One type of fragmentation pattern is the cleavage of a single bond in the molecular ion. The cleavage leads to a radical and a cation. The cleavage can...
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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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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 low-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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Mass Spectrometers01:16

Mass Spectrometers

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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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Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation

1.0K
The fragmentation patterns observed for compounds such as carboxylic acids, esters, and amides in the mass spectra include ⍺-cleavage and McLafferty rearrangement. Fragmentation by ⍺-cleavage preferentially occurs at the carbon-carbon bond at the ⍺-position next to the carboxylic group to generate a neutral radical and a cation. Long chain compounds with hydrogen at their γ-carbon undergo McLafferty rearrangement to give a radical cation and a neutral alkene.
For example,...
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Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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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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Mass Spectrum01:23

Mass Spectrum

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A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x axis represents the ratio of the mass of the charged fragment to the elementary charge it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal...
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Related Experiment Video

Updated: May 15, 2025

Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis
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Comprehensive and Explainable Fragmentation: A Machine Learning Approach for Fast and Accurate Mass Spectrum

Xian-Yang Zhang1, Xue-Qing Gong1,2

  • 1Centre for Computational Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.

The Journal of Physical Chemistry. A
|April 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dual-model machine learning approach for enhanced in-silico mass spectrometry (MS) analysis. The method improves spectral prediction accuracy and efficiency for identifying unknown molecular structures.

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Mass spectrometry (MS) is crucial for chemical identification but current in-silico prediction tools have limitations.
  • Existing methods struggle with broad instrument conditions, large molecular libraries, and detailed fragment structure analysis.

Purpose of the Study:

  • To develop an advanced in-silico prediction strategy for mass spectrometry.
  • To overcome limitations in predicting spectral properties and identifying molecular fragments.
  • To enhance the understanding of molecular fragmentation behavior.

Main Methods:

  • Proposed a dual-model machine learning strategy combining classification and regression.
  • Utilized a classification model for fragment identification and noise filtering.
  • Employed a regression model for accurate spectral prediction, incorporating an attention mechanism.

Main Results:

  • The dual-model approach demonstrated superior accuracy and efficiency compared to existing algorithms.
  • The attention mechanism significantly improved the prediction of molecular fragmentation patterns.
  • Successfully facilitated large-scale in-silico spectra calculations.

Conclusions:

  • The developed machine learning strategy offers a robust solution for in-silico mass spectrometry.
  • It enables deeper insights into molecular fragmentation and aids in analyzing unknown structures.
  • Promotes broader applications of MS in chemical identification and analysis.