Recent (2000-2015) developments in the analysis of minor unknown natural products based on characteristic fragment information using LC-MS.
Mass Spectrom Rev
; 37(2): 202-216, 2018 03.
Article
em En
| MEDLINE
| ID: mdl-27341181
ABSTRACT
Liquid chromatography-Mass Spectrometry (LC-MS) has been widely used in natural product analysis. Global detection and identification of nontargeted components are desirable in natural product research, for example, in quality control of Chinese herbal medicine. Nontargeted components analysis continues to expand to exciting life science application domains such as metabonomics. With this background, the present review summarizes recent developments in the analysis of minor unknown natural products using LC-MS and mainly focuses on the determination of the molecular formulae, selection of precursor ions, and characteristic fragmentation patterns of the known compounds. This review consists of three parts. Firstly, the methods used to determine unique molecular formula of unknown compounds such as accurate mass measurements, MSn spectra, or relative isotopic abundance information, are introduced. Secondly, the methods improving signal-to-noise ratio of MS/MS spectra by manual-MS/MS or workflow targeting-only signals were elucidated; pure precursor ions can be selected by changing the precursor ion isolated window. Lastly, characteristic fragmentation patterns such as Retro-Diels-Alder (RDA), McLafferty rearrangements, "internal residue loss," and so on, occurring in the molecular ions of natural products are summarized. Classical application of characteristic fragmentation patterns in identifying unknown compounds in extracts and relevant fragmentation mechanisms are presented (RDA reactions occurring readily in the molecular ions of flavanones or isoflavanones, McLafferty-type fragmentation reactions of some natural products such as epipolythiodioxopiperazines; fragmentation by "internal residue loss" possibly involving ion-neutral complex intermediates). © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37202-216, 2018.
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MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article