UHPLC-HRMS/MS on untargeted metabolomics: a case study with Copaifera (Fabaceae).
Ananda da Silva AntonioDavi Santos OliveiraGustavo Ramalho Cardoso Dos SantosHenrique Marcelo Gualberto PereiraLarissa Silveira Moreira WiedemannValdir Florêncio da Veiga JúniorPublished in: RSC advances (2021)
Untargeted metabolomics is a powerful tool in chemical fingerprinting. It can be applied in phytochemistry to aid species identification, systematic studies and quality control of bioproducts. This approach aims to produce as much chemical information as possible, without focusing on any specific chemical class, thus, requiring extensive chemometric effort. This study aimed to evaluate the feasibly of an untargeted metabolomics method in phytochemistry by a study case of the Copaifera genus (Fabaceae). This genus contains significant medicinal species used worldwidely. Copaifera exploitation issues include a lack of chemical data, ambiguous species identification methods and absence of quality control for its bioproducts. Different organs of five Copaifera species were analysed by UHPLC-HRMS/MS, GNPS platform and chemometric tools. Untargeted metabolomics enabled the identification of 19 chemical markers and 29 metabolites, distinguishing each sample by species, plant organs, and biome type. Chemical markers were classified as flavonoids, terpenoids and condensed tannins. The applied method provided reliable information about species chemodiversity using fast workflow with little sampling size. The untargeted approach by UHPLC-HRMS/MS proved to be a promising tool for species identification, pharmacological prospecting and in the future for the quality control of extracts used in the manufacture of bioproducts.
Keyphrases
- mass spectrometry
- quality control
- high resolution mass spectrometry
- ms ms
- liquid chromatography
- multiple sclerosis
- ultra high performance liquid chromatography
- simultaneous determination
- gas chromatography mass spectrometry
- healthcare
- electronic health record
- high throughput
- solid phase extraction
- case control
- big data