Chemometrics Applied toNMRAnalysis

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.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

“…After the NMR acquisition, a set of FIDs is obtained. The spectral processing for the conversion of FIDs to spectra includes the standard procedures that are used in any other type of NMR experiments, such as Fourier transform, phase correction, baseline correction (Xi & Rocke, ), and signal referencing (Rusilowicz, O'Keefe, Wilson, & Charlton, ). In addition, the NMR spectral acquisition and processing must be identical for all samples.…”

A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples

“…The use of liquid chromatography-mass spectrometry (LC–MS) for this purpose has found a wide range of applications, including drug discovery (Korfmacher 2005 ), disease biomarker discovery (Lu et al 2008 ), pesticide (Zhang et al 2011 ) and herbicide (Shalaby et al 1992 ) analysis in agriculture, wastewater analysis (Kostich et al 2014 ) and the discovery of novel metabolites (Nakabayashi and Saito 2013 ). LC–MS however suffers from lower reproducibility in comparison to other analytical techniques such as NMR spectroscopy (Gürdeniz et al 2013 ; Rusilowicz 2014 ). Many non-targeted approaches focus on qualitative results, such as biomarker discovery, and the need for reproducible and comparable results is imperative, especially when differences between experimental groups are small.…”

“…Data reduction is achieved by considering just the first few components accounting for most of the variance, and therefore most information, in the data. As an unsupervised method, PCA is commonly exploited in metabolomics studies to highlight experimental differences (Katajamaa et al 2007 ; Rusilowicz 2014 ).…”