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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

3.6K
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...
3.6K
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

7.2K
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...
7.2K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

1.9K
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...
1.9K
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

2.8K
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...
2.8K
Mass Spectrum01:23

Mass Spectrum

5.1K
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...
5.1K
Mass Spectrometers01:16

Mass Spectrometers

9.6K
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:
9.6K

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Related Experiment Video

Updated: Mar 3, 2026

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

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Binary Markov Random Fields and interpretable mass spectra discrimination.

Ao Kong, Robert Azencott

    Statistical Applications in Genetics and Molecular Biology
    |May 6, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new algorithm using parameterized Markov Random Fields for interpretable cancer biomarker discovery from mass spectrometry data. The method achieves high accuracy in discriminating cancer types and stages, aiding further research.

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    Area of Science:

    • Computational Biology
    • Biomarker Discovery
    • Machine Learning in Oncology

    Background:

    • Automated discrimination of cancer types/stages from mass spectra often uses machine learning but lacks biomarker interpretability.
    • Existing methods struggle to provide clear biological insights from complex mass spectrometry data.

    Purpose of the Study:

    • To develop a novel, interpretable algorithm for mass spectra discrimination using parameterized Markov Random Fields.
    • To identify small groups of scored biomarkers for accurate cancer classification.

    Main Methods:

    • Developed a parameterized Markov Random Fields algorithm for interpretable mass spectra classification.
    • Validated the approach on MALDI colorectal and SELDI ovarian cancer mass spectra datasets.
    • Evaluated classifier performance and visualization capabilities.

    Main Results:

    • Achieved high accuracies (81%-100%) in discriminating colorectal and ovarian cancer stages.
    • Performed comparably or superiorly to existing methods on similar datasets.
    • Enabled efficient planar visualization of mass spectra discrimination.

    Conclusions:

    • The proposed algorithm generates interpretable classifiers, facilitating biological interpretation of cancer signatures.
    • The method demonstrates good performance and scalability for large datasets.
    • Classifiers aid in planning experiments for biomarker validation and clinical application.