Abstract
Nuclear magnetic resonance (NMR) spectroscopy
provides information about the physical and chemical
properties of atoms within a sample and has become an
essential tool in metabolomics. Although less sensitive
than mass spectrometry, NMR spectroscopy is highly
reproducible and gives wider coverage in comparison
to other techniques used for the analysis of complex
mixtures, providing information about all metabolites
with concentrations above the limit of detection. Recent
advances in technology, advances in methodology, and
increased computer power have increased the need to
develop mathematical and statistical methods to analyze
and interpret the complex datasets acquired. This has
resulted in developments in the field of chemical informatics
known as chemometrics. In contrast to targeted
analyses, chemometric approaches do not initially attempt
to identify particular metabolites. Instead, statistical
techniques and pattern recognition methods are used to
identify spectral features that show consistent trends or
provide discrimination between classes. These features can
provide a fingerprint for a metabolic state to be used, for
example, for disease diagnostics, and individual features
of interest can then be related to specific compounds and
metabolic pathways using database searches.
NMR-based chemometric methods are now used in a wide
range of applications including clinical diagnostics, food
science and traceability, monitoring genetic modification,
predicting the side effects of pharmaceuticals and their
environmental effects, and process control.
provides information about the physical and chemical
properties of atoms within a sample and has become an
essential tool in metabolomics. Although less sensitive
than mass spectrometry, NMR spectroscopy is highly
reproducible and gives wider coverage in comparison
to other techniques used for the analysis of complex
mixtures, providing information about all metabolites
with concentrations above the limit of detection. Recent
advances in technology, advances in methodology, and
increased computer power have increased the need to
develop mathematical and statistical methods to analyze
and interpret the complex datasets acquired. This has
resulted in developments in the field of chemical informatics
known as chemometrics. In contrast to targeted
analyses, chemometric approaches do not initially attempt
to identify particular metabolites. Instead, statistical
techniques and pattern recognition methods are used to
identify spectral features that show consistent trends or
provide discrimination between classes. These features can
provide a fingerprint for a metabolic state to be used, for
example, for disease diagnostics, and individual features
of interest can then be related to specific compounds and
metabolic pathways using database searches.
NMR-based chemometric methods are now used in a wide
range of applications including clinical diagnostics, food
science and traceability, monitoring genetic modification,
predicting the side effects of pharmaceuticals and their
environmental effects, and process control.
| Original language | English |
|---|---|
| Title of host publication | Encyclopedia of Analytical Chemistry |
| Publisher | Wiley-Blackwell |
| Number of pages | 32 |
| DOIs | |
| Publication status | Published - 2013 |
Publication series
| Name | Encyclopedia of Analytical Chemistry |
|---|