title: Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry.
abstract: Metabolic fingerprints were obtained from unfractionated Pharbitis nil leaf sap samples by direct infusion into an electrospray ionization mass spectrometer. Analyses took less than 30 s per sample and yielded complex mass spectra. Various chemometric methods, including discriminant function analysis and the machine-learning methods of artificial neural networks and genetic programming, could discriminate the metabolic fingerprints of plants subjected to different photoperiod treatments. This rapid automated analytical procedure could find use in a variety of phytochemical applications requiring high sample throughput.
authors.affiliation: Institute of Biological Sciences, University of Wales Aberystwyth, Ceredigion SY23 3DA, UK.
authors.forename: Royston
authors.initials: R
authors.lastname: Goodacre
authors.forename: Emma V
authors.initials: EV
authors.lastname: York
authors.forename: James K
authors.initials: JK
authors.lastname: Heald
authors.forename: Ian M
authors.initials: IM
authors.lastname: Scott
mesh_terms: D001185:Artificial Intelligence; D016002:Discriminant Analysis; D027741:Ipomoea / Q000737:chemistry; D016571:Neural Networks, Computer; D017440:Photoperiod; D010936:Plant Extracts / Q000737:chemistry*; D018515:Plant Leaves / Q000737:chemistry; D021241:Spectrometry, Mass, Electrospray Ionization
chemical_list: D010936:Plant Extracts