These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased.

For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted.

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena.

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