NMR spectroscopy and mass spectrometry are the two major analytical platforms for metabolomics, and both generate
substantial data with hundreds to thousands of observed peaks for a single sample. Many of these are unknown, and peak assignment
is generally complex and time-consuming. Statistical correlations between data types have proven useful in expediting this process,
for example in prioritizing candidate assignments. However, this approach has not been formally assessed for the comparison of
direct-infusion mass spectrometry (DIMS) and NMR data. Here, we present a systematic analysis of a sample set (tissue extracts),
and the utility of a simple correlation threshold to aid metabolite identification. The correlations were surprisingly successful in
linking structurally related signals, with 15 of 26 NMR-detectable metabolites having their highest correlation to a cognate MS ion.
However, we found that the distribution of the correlations was highly dependent on the nature of the MS ion, such as the adduct
type. This approach should help to alleviate this important bottleneck where both 1D NMR and DIMS datasets have been collected.