Introduction: In this era of 'omics' technology in natural products studies, the complementary aspects of mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques must be taken into consideration. The advantages of using both analytical platforms are reflected in a higher confidence of results especially when using replicated samples where correlation approaches can be used to statistically link results from MS to NMR.
Objectives: Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification.
Methodology: Essential oil samples of Melaleuca alternifolia and M. rhaphiophylla (Myrtaceae) were used as test objects. Aliquots of 10 samples were collected for GC-MS and NMR data acquisition [proton (1 H)-NMR, and carbon-13 (13 C)-NMR as well as two-dimensional (2D) heteronuclear single quantum correlation (HSQC), heteronuclear multiple-bond correlation (HMBC), and HSQC-total correlated spectroscopy (TOCSY) NMR]. The processed data was imported to Matlab where STOCSY was applied.
Results: STOCSY calculations led to the confirmation of the four main constituents of the sample-set. The identification of each was accomplished using; MS spectra, retention time comparison, 13 C-NMR data, and scalar correlations of the 2D NMR spectra.
Conclusion: This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.
Keywords: GC-MS; NMR; STOCSY; compound identification; data fusion; dereplication; essential oils; metabolomics; statistical heterospectroscopy.
© 2022 John Wiley & Sons, Ltd.