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Related Concept Videos

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...
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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

High-Resolution Mass Spectrometry (HRMS)

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 example, the mass of helium...
Mass Spectrometers01:16

Mass Spectrometers

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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Related Experiment Video

Updated: Jun 25, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Spectral relative standard deviation: a practical benchmark in metabolomics.

Helen M Parsons1, Drew R Ekman, Timothy W Collette

  • 1Centre for Systems Biology, The University of Birmingham, Edgbaston, Birmingham, UKB15 2TT.

The Analyst
|February 25, 2009
PubMed
Summary

Spectrum-wide relative standard deviations (RSDs) provide a robust method for assessing reproducibility in metabolomics datasets. These measurements help characterize technical and biological variations across diverse sample types.

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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Published on: January 5, 2021

Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Metabolomics datasets involve numerous metabolite measurements, susceptible to technical and biological variations.
  • Assessing the reproducibility of these datasets across all detected metabolites is crucial.
  • Existing methods may not adequately capture variations inconsistent across all metabolites.

Purpose of the Study:

  • To introduce and validate spectrum-wide relative standard deviations (RSDs) as a metric for metabolomics reproducibility.
  • To demonstrate the utility of spectral RSDs in characterizing technical and biological variations.
  • To propose spectral RSDs as practical benchmarks for metabolomics studies.

Main Methods:

  • Calculation of spectrum-wide relative standard deviations (RSDs), also known as coefficient of variation (CV), for ten diverse metabolomics datasets.
  • Analysis of sample types including mammals, fish, invertebrates, and cell lines.
  • Visualization of RSDs using boxplots.

Main Results:

  • Technical variation in metabolomics datasets ranged from 1.6% to 20.6% (median spectral RSD) across NMR and mass spectrometry techniques.
  • Inter-individual biological variation was generally larger, ranging from 7.2% in rat tissue extracts to 58.4% in fish plasma.
  • Spectral RSD values showed invariance across different spectral processing methods like baseline correction, normalization, and binning resolution.

Conclusions:

  • Spectrum-wide RSDs are effective for characterizing both technical and biological variations in metabolomics.
  • These RSDs can be applied to optimize metabolite extractions, compare analytical techniques, investigate matrix effects, and refine experimental designs.
  • The proposed spectral RSDs and their median values serve as practical benchmarks for ensuring the quality and reproducibility of metabolomics studies.