Planta Med 2013; 79 - PK9
DOI: 10.1055/s-0033-1352270

Linking analysis to value chains: Using NMR spectroscopy and HPTLC to analyse the metabolite variability of turmeric products

A Booker 1, D Frommenwiler 2, C Umealajekwu 1, D Johnston 3, E Reich 2, M Heinrich 1
  • 1Centre for Pharmacognosy and Phytotherapy, UCL School of Pharmacy, University of London, UK.
  • 2CAMAG, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland
  • 3School of Oriental and African Studies, University of London, Thornhaugh Street, Russell Square, London UK

Introduction: It is well known that raw materials are vital components of herbal products and while considerable attention has been paid to assessing the pharmaceutical quality of the final product, surprisingly little is known about the variability of the starting material and about the value chains from producer to consumer (Booker et al 2012). Stringent procedures are needed for their cultivation and primary processing if the finished product is to approach high level of quality. Research often focuses on developing improved varieties but not on the variability of the starting material sourced from multiple producers.

Objectives: To determine the variability of metabolite content in a range of turmeric samples collected in India and in commercially available products and assess the strengths and weaknesses of two selected analytical techniques. Leading on from this primary goal our secondary objective was to attempt to link variability in product composition to the methods of production and the value chain.

Methodology: Fifty samples of turmeric products were gathered from sites in India, Europe and the USA. The samples were analysed using proton-NMR linked to multi-variate analysis software to identify differences in the metabolite content. The samples were subsequently analysed using HPTLC to discover if this technique gave similar information to NMR and if there were any benefits gained by employing a dual analysis strategy.

Results: It was found that samples varied in metabolite composition and the NMR-Multivariate analysis platform was able to group samples according to these differences. HPTLC generally gave similar results but with some notable exceptions which gave rise to further investigation. (Fig 1)