Metabolite identification is one of the major challenges of non-targeted metabolomics involving liquid chromatography coupled with mass spectrometry (LC-MS). Compound databases contain enormous numbers of records, which makes compound identification difficult in practice because each search will return a large number of candidates. We therefore developed a practical compound identification system using LC-MS with high mass accuracy and MSn capability, combined with a compound database. A large number of candidates were evaluated by score calculation based on a combination of formulae and spectral assignments. Here, we demonstrate this method using green tea extract as a model sample. We applied our approach to predict the structures of compounds of interest, and the correct identification of several candidates was confirmed by comparisons to analysis of chemical standards.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry
- Organic Chemistry