Publication Topic

2D NMR Barcoding for the Identification of Molecules in Complex Mixtures

See publication here

What is NMR Barcoding?

NMR barcoding is an informatics tool which integrates statistics, database systems, and pattern recognition for the automated interpretation of NMR spectra. Generally, it uses clusters of fingerprinting signals and their spatial relationships in the 1D or 2D-NMR spectra to ‘barcode’ the chemical species.

2D-NMR-Barcoding Concept Figure

The Proof of Concept: NMR Barcoding for Actaea Triterpenes

NMR spectra were used to barcode the major types of Actaea triterpenes which are structurally related and mainly found in Actaea plants. As shown in the figure above, the HMBC barcodes of these triterpenes were generated from mining of their NMR data through an extensive literature survey. In practice, pattern recognition of these reference barcodes in the HMBC spectra enables the structural identification of the individual chemical species.

NMR Barcode Reader for Actaea Triterpenes 

A VBA application named “NMR Barcode Reader” was developed within Microsoft Excel 2010 to facilitate the automated batch identification of the Actaea triterpene mixtures. You can download the latest version using the “Save As” function of your browser (typically, upon right mouse click or in the context menu). A sample file is also provided to demonstrate the required data format and the basic functions of the Reader.

Instructions for NMR Barcode Reader

Step 1: Export HMBC peaks into xlsx format (see sample file) using NMR processing software.

Step 2: Click the “Import” button to import the xlsx file.

Step 3: Click the “Scan” button to find the candidate patterns and match them with the reference barcodes.


Important Note: The authors, in some instance together with other copyright holders, hold a copyright ownership interest in any copyrightable information provided on this website. Files may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any of this information, either in whole or in part, in either machine-readable form or any other form without permission from the authors. (c) 2014, UIC, Chicago (IL)