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Related Concept Videos

Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

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Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
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Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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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Gas Chromatography: Introduction01:13

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Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
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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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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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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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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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SIMAT: GC-SIM-MS data analysis tool.

Mohammad R Nezami Ranjbar1,2, Cristina Di Poto3, Yue Wang4

  • 1Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USA. nranjbar@vt.edu.

BMC Bioinformatics
|August 19, 2015
PubMed
Summary

A new R package, SIMAT, aids in analyzing gas chromatography-selected ion monitoring-mass spectrometry (GC-SIM-MS) data. It optimizes fragment selection and peak detection for accurate quantification of targeted analytes.

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Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Metabolomics

Background:

  • Gas chromatography-mass spectrometry (GC-MS) is crucial for small molecule analysis.
  • Selected ion monitoring (SIM) in GC-MS enables targeted quantitative analysis.
  • Existing software lacks specific tools for GC-SIM-MS data analysis.

Purpose of the Study:

  • Introduce SIMAT, a novel R/Bioconductor package for quantitative GC-SIM-MS data analysis.
  • Provide tools for optimal fragment selection and retention time window determination.
  • Facilitate accurate quantification of targeted analytes in complex samples.

Main Methods:

  • Developed an R package, SIMAT, incorporating an optimization algorithm for fragment selection.
  • Implemented functions for importing GC-SIM-MS data (netCDF, MSL formats).
  • Included capabilities for retention index calibration, total ion chromatogram (TIC), and extracted ion chromatogram (EIC) visualization.

Main Results:

  • Evaluated SIMAT on metabolomic and method development datasets (plasma samples, internal standards).
  • Demonstrated SIMAT's effectiveness in accurate target detection and relative intensity estimation.
  • Showcased SIMAT as a viable alternative to existing tools like AMDIS and MetaboliteDetector.

Conclusions:

  • SIMAT is a comprehensive R package for GC-SIM-MS data analysis.
  • The package facilitates optimal fragment and retention time window selection.
  • SIMAT supports data import, preprocessing, and visualization for targeted analysis.