The interpretation of NMR spectroscopic
information for structure
elucidation involves decoding of complex resonance patterns that contain
valuable molecular information (δ and <i>J</i>), which
is not readily accessible otherwise. We introduce a new concept of
2D-NMR barcoding that uses clusters of fingerprint signals and their
spatial relationships in the δ−δ coordinate space
to facilitate the chemical identification of complex mixtures. Similar
to widely used general barcoding technology, the structural information
of individual compounds is encoded as a specifics pattern of their
C,H correlation signals. Software-based recognition of these patterns
enables the structural identification of the compounds and their discrimination
in mixtures. Using the triterpenes from various <i>Actaea</i> (syn. <i>Cimicifuga</i>) species as a test case, heteronuclear
multiple-bond correlation (HMBC) barcodes were generated on the basis
of their structural subtypes from a statistical investigation of their
δ<sub>H</sub> and δ<sub>C</sub> data in the literature.
These reference barcodes allowed in silico identification of known
triterpenes in enriched fractions obtained from an extract of <i>A. racemosa</i> (black cohosh). After dereplication, a differential
analysis of heteronuclear single-quantum correlation (HSQC) spectra
even allowed for the discovery of a new triterpene. The 2D barcoding
concept has potential application in a natural product discovery project,
allowing for the rapid dereplication of known compounds and as a tool
in the search for structural novelty within compound classes with
established barcodes
