title: Multivariate data analysis and metabolic profiling of artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar.
abstract: An improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) protocol for rapid analysis of co-metabolites of A. annua in raw extracts was developed and extensively characterized. The new method was used to analyse metabolic profiles of 13 varieties of A. annua from an in-field growth programme in Madagascar. Several multivariate data analysis techniques consistently show the association of artemisinin with dihydroartemisinic acid. These data support the hypothesis of dihydroartemisinic acid being the late stage precursor to artemisinin in its biosynthetic pathway.
authors.affiliation: Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB2 3RA, UK.
authors.forename: John
authors.initials: J
authors.lastname: Suberu
authors.affiliation: WestChem, Department of Pure and Applied Chemistry, and Centre for Process Analytics and Control Technology (CPACT), University of Strathclyde, Glasgow G1 1XL, UK.
authors.forename: Piotr S
authors.initials: PS
authors.lastname: Gromski
authors.affiliation: WestChem, Department of Pure and Applied Chemistry, and Centre for Process Analytics and Control Technology (CPACT), University of Strathclyde, Glasgow G1 1XL, UK.
authors.forename: Alison
authors.initials: A
authors.lastname: Nordon
authors.affiliation: Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB2 3RA, UK. Electronic address: aal35@cam.ac.uk.
authors.forename: Alexei
authors.initials: A
authors.lastname: Lapkin
mesh_terms: D031172:Artemisia annua; D008270:Madagascar; D055432:Metabolomics / Q000379:methods*; D015999:Multivariate Analysis; D010936:Plant Extracts / Q000032:analysis* / Q000378:metabolism*; D018515:Plant Leaves; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D010936:Plant Extracts
keywords: Artemisia annua; Metabolic profiling; Multivariate data analysis