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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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...
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

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...
Mass Spectrum01:23

Mass Spectrum

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 number of charges 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 (the...
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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 soft-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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Related Experiment Video

Updated: Jun 2, 2026

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) Mass Spectrometry
06:56

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) Mass Spectrometry

Published on: June 10, 2018

Mutual information optimization for mass spectra data alignment.

Italo Zoppis1, Erica Gianazza, Massimiliano Borsani

  • 1UniversitĂ  degli Studi di Milano-Bicocca, Milan.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|April 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data alignment method for mass spectrometry, improving proteomic analysis and disease classification. The new approach shows significant performance advantages over existing methods for Alzheimer's patient data.

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Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) Mass Spectrometry
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Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) Mass Spectrometry

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Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
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Published on: January 20, 2022

Area of Science:

  • Proteomics
  • Bioinformatics
  • Clinical Mass Spectrometry

Background:

  • Signal alignments are crucial in clinical settings, particularly for mass spectrometry data in proteomic analysis.
  • Integrating data from diverse sources (e.g., different labs or equipment) presents challenges for accurate classification tasks like disease diagnosis.
  • High-performance data alignment methods are essential for overcoming these integration issues.

Purpose of the Study:

  • To develop and evaluate a novel data alignment method for integrating mass spectrometry data from multiple sources.
  • To improve the accuracy of disease classification by enhancing data fusion techniques.
  • To address the need for advanced alignment methods in clinical proteomic analysis.

Main Methods:

  • The proposed method combines an information theory perspective for feature construction with the weighted bipartite matching problem from mathematical programming.
  • A competitive analysis was conducted comparing the new method against existing approaches.
  • The study utilized plasma/ethylenediaminetetraacetic acid (EDTA) data from control and Alzheimer's disease patients across three hospitals.

Main Results:

  • The novel data alignment method demonstrated a significant performance advantage compared to the competing methods tested.
  • The approach effectively integrates data from different sources, reducing source-specific variations.
  • Improved classification capabilities for inferring disease classes were observed.

Conclusions:

  • The proposed information theory and mathematical programming-based alignment method offers superior performance for mass spectrometry data integration.
  • This advancement is particularly valuable for clinical applications requiring accurate disease classification from multi-source proteomic data.
  • The method shows promise for enhancing the reliability and accuracy of clinical diagnostic tools.