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Related Concept Videos

2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

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Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
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Mass Spectrometry: Overview01:19

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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 electron 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 behind a...
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2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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2D NMR: Overview of Homonuclear Correlation Techniques01:16

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Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
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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: Complex Analysis01:21

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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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Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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CorrelandSW: Correlation Networks from LC-MS Data.

Andrea Kosinova1, Jiri Gruz1

  • 1Department of Experimental Biology, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic.

Journal of the American Society for Mass Spectrometry
|April 22, 2026
PubMed
Summary

Correland software simplifies complex mass spectrometry data by clustering metabolite correlations. This tool aids in identifying biosynthetically related metabolites and visualizing complex biological networks.

Keywords:
LC-MS datacorrelationmetabolomicsnetwork

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry (MS) data is growing in size and complexity.
  • Advanced computational tools are needed to analyze large MS datasets.
  • Understanding metabolite correlations is crucial for biological insights.

Purpose of the Study:

  • To present Correland, a MATLAB-based software for analyzing metabolite correlations.
  • To enable clustering and visualization of metabolite associations using weighted correlation networks.
  • To demonstrate the software's utility in processing complex biological data.

Main Methods:

  • Development of Correland software in MATLAB.
  • Application of weighted correlation networks for metabolite association analysis.
  • Utilizing an ion grouping algorithm to reduce network complexity.
  • Employing rescaled Pearson correlation coefficients for network generation.

Main Results:

  • Correland effectively clusters biosynthetically related metabolites.
  • The ion grouping algorithm reduced network scale from ~900 features to 83 nodes.
  • Pseudomolecular ion identification achieved an 86-90% success rate in Arabidopsis extract.
  • Generated interpretable and visually coherent networks by reducing network density.

Conclusions:

  • Correland is an effective tool for analyzing and visualizing complex MS data.
  • The software aids in identifying biologically relevant metabolite relationships.
  • Correland facilitates the interpretation of large-scale metabolomic datasets.