A note on normalization of biofluid 1D H-1-NMR data

One-dimensional H-1 nuclear magnetic resonance (1D H-1-NMR) has been used extensively as a metabolic profiling tool for investigating urine and other biological fluids. Under ideal conditions, H-1-NMR peak intensities are directly proportional to metabolite concentrations and thus are useful for class prediction and biomarker discovery. However, many biological, experimental and instrumental variables can affect absolute NMR peak intensities. Normalizing or scaling data to minimize the influence of these variables is a critical step in producing robust, reproducible analyses. Traditionally, analyses of biological fluids have relied on the total spectral area [constant sum (CS)] to normalize individual intensities. This approach can introduce considerable inter-sample variance as changes in any individual metabolite will affect the scaling of all of the observed intensities. To reduce normalization-related variance, we have developed a histogram matching (HM) approach adapted from the field of image processing. We validate our approach using mixtures of synthetic compounds that mimic a biological extract and apply the method to an analysis of urine from rats treated with ethionine. We show that HM is a robust method for normalizing H-1-NMR data and propose it as an alternative to the traditional CS method.