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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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Electrospray Ionization (ESI) Mass Spectrometry01:12

Electrospray Ionization (ESI) Mass Spectrometry

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Higher molecular weight biomolecules are nonvolatile compounds that may decompose before ionizing or vaporizing during mass analysis with conventional electron impact ionization methods. Accordingly, electrospray ionization (ESI) is the favored method for vaporizing and ionizing biomolecules as it circumvents rapid fragmentation and enables the recording of mass signals for the entire biomolecule.
ESI utilizes electrical energy to transfer ions from the liquid phase of the sample into the...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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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...
4.7K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

615
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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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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Related Experiment Video

Updated: Jun 12, 2025

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
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Self-Organizing Maps for Secondary Ion Mass Spectrometry.

Sarah E Bamford1, Wil Gardner1, David A Winkler2,3,4

  • 1Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.

Journal of the American Society for Mass Spectrometry
|September 23, 2024
PubMed
Summary
This summary is machine-generated.

Self-organizing maps (SOMs) help analyze complex secondary ion mass spectrometry (SIMS) data. This machine learning approach aids in interpreting large spectral, imaging, and depth profiling datasets for surface characterization.

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Data Science

Background:

  • Secondary Ion Mass Spectrometry (SIMS) is crucial for surface composition analysis.
  • SIMS generates large, complex datasets with spatial and molecular information.
  • Existing analysis methods struggle with the inherent complexity of SIMS data.

Purpose of the Study:

  • Introduce Self-Organizing Maps (SOMs) for SIMS data analysis.
  • Demonstrate SOMs' utility in interpreting complex mass spectrometry datasets.
  • Provide a guide for SIMS specialists exploring SOMs.

Main Methods:

  • Utilized Self-Organizing Maps (SOMs), a machine learning algorithm.
  • Applied SOMs for dimensionality reduction and clustering of hyperspectral data.
  • Reviewed examples of SOM application in SIMS and related techniques.

Main Results:

  • SOMs effectively cluster similar samples and reduce data dimensionality.
  • Demonstrated SOMs' capability to interpret high-volume SIMS data (spectra, imaging, depth profiling).
  • Highlighted the potential of SOMs for uncovering subtle spatial and molecular relationships.

Conclusions:

  • SOMs are powerful tools for analyzing complex SIMS data.
  • This machine learning technique enhances data interpretation and visualization.
  • SOMs offer a valuable approach for specialists in mass spectrometry.