No 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. 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, process control, monitoring genetic modification, and predicting the side effects of pharmaceuticals and their environmental effects.