{Reference Type}: Journal Article
{Title}: Exploring correlations between MS and NMR for compound identification using essential oils: A pilot study.
{Author}: Borges RM;Resende JVM;Pinto AP;Garrido BC;Borges RM;Resende JVM;Pinto AP;Garrido BC;
{Journal}: Phytochem Anal
{Volume}: 33
{Issue}: 4
{Year}: Jun 2022
{Factor}: 3.024
{DOI}: 10.1002/pca.3107
{Abstract}: <B>BACKGROUND: </B>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.<BR><B>OBJECTIVE: </B>Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.