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Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow signifi cant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identifi ed an additional concern, very small and random fl uctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.