Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry

Royston Goodacre, Emma V. York, Jim K. Heald, Ian M. Scott

Research output: Contribution to journalArticlepeer-review

114 Citations (SciVal)

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. Chemometric methods including discriminant function analysis, artificial neural networks, and genetic programming, could discriminate the metabolic fingerprints obtained from unfractionated Pharbitis nil leaf sap by direct infusion into an electrospray ionization MS.
Original languageEnglish
Pages (from-to)859-863
Number of pages5
JournalPhytochemistry
Volume62
Issue number6
Early online date08 Feb 2003
DOIs
Publication statusPublished - 01 Mar 2003

Keywords

  • Pharbitis nit
  • convolvulaceae
  • japanese morning glory
  • electrospray ionization mass spectrometry
  • neural networks
  • genetic programming
  • metabolic fingerprinting

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