Planta Med 2016; 82 - OA13
DOI: 10.1055/s-0036-1578583

Metabolomics Based UHPLC-QToF-MS Approach for the Authentication of Various Botanicals and Dietary Supplements

B Avula 1, YH Wang 1, G Isaac 2, J Yuk 2, M Wrona 2, K Yu 2, IA Khan 1
  • 1National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, University of Mississippi, Oxford, MS 38677, USA
  • 2Waters Corporation, 34 Maple Street, Milford, MA. USA

Metabolomics provides an unbiased, comprehensive qualitative and quantitative overview of the metabolites present in botanicals and dietary supplements. Compared to conventional analyses which are focused on a limited set of compounds of primary interest, metabolomics approaches, together with novel data processing tools, enable a more holistic comparison of samples. With the constant evolution in analytical technologies, the application of high resolution LC-MS instrumentation such as UPLC-QTof MS has been gaining popularity in metabolomics analyses. Application of these technologies can help to shorten analysis time, increase separation efficiency, and obtain results with high confidence. However, these technologies generate large and complex datasets which hinder data analysis and interpretation. Here, we present various botanical studies (Hoodia, Terminalia and Chamomile) using UHPLC-QToF-MS metabolomics for the authentication of botanicals and dietary supplements sold in the market. The metabolite profiles of the botanicals (Hoodia, Terminalia and Chamomile) and their commercial products were investigated using novel informatics software called Progenesis QI to determine the pattern and identification of different metabolites. Progenesis QI adopts an intuitive workflow to performing comparative metabolomics data analysis. The workflow starts with raw data file loading, retention time alignment, normalization and deconvolution, followed by analysis that creates a list of features. The key features are explored using multivariate statistical methods and identified by searching various databases. Differential analysis of results across several botanical species can quickly be performed, thereby facilitating identification and quantitation of potential metabolite markers. Finally, significantly changing metabolite markers that differentiate between various botanical species were identified that can be used as a target markers for botanical authentication.