title: Rapid Species-level Identification of Salvias by Chemometric Processing of Ambient Ionisation Mass Spectrometry-derived Chemical Profiles.
abstract: BACKGROUND
  The Salvia genus contains numerous economically important plants that have horticultural, culinary and nutraceutical uses. They are often similar in appearance, making species determination difficult. Species identification of dried Salvia products is also challenging since distinguishing plant morphological features are no longer present.
  
  
  OBJECTIVE
  The development of a simple high-throughput method of analysis of fresh and dried Salvia leaves that would permit rapid species-level identification and detection of diagnostic biomarkers.
  
  
  METHODS
  Plant leaves were analysed in their native form by DART-MS without the need for any sample preparation steps. This furnished chemical fingerprints characteristic of each species. In the same experiment, in-source collision-induced dissociation was used to identify biomarkers. Biomarker presence was also independently confirmed by GC-MS. Chemometric processing of DART-MS profiles was performed by kernel discriminant analysis (KDA) and soft independent modelling of class analogy (SIMCA) to classify the fingerprints according to species.
  
  
  RESULTS
  The approach was successful despite the occurrence of diurnal cycle and plant-age related chemical profile variations within species. In a single rapid experiment, the presence of essential oil biomarkers such as 3-carene, α-pinene, β-pinene, β-thujone, β-caryophyllene, camphor and borneol could be confirmed. The method was applied to rapid identification and differentiation of Salvia apiana, S. dominica, S. elegans, S. officinalis, S. farinacea and S. patens.
  
  
  CONCLUSIONS
  Species-level identification of Salvia plant material could be accomplished by chemometric processing of DART-HRMS-derived chemical profiles of both fresh and dried Salvia material. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.
authors.forename: Justine E
authors.initials: JE
authors.lastname: Giffen
authors.affiliation: Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.
authors.forename: Ashton D
authors.initials: AD
authors.lastname: Lesiak
authors.affiliation: JEOL USA Inc., 11 Dearborn Road, Peabody, MA, 01960, USA.
authors.forename: A John
authors.initials: AJ
authors.lastname: Dane
authors.affiliation: JEOL USA Inc., 11 Dearborn Road, Peabody, MA, 01960, USA.
authors.forename: Robert B
authors.identifier: http://orcid.org/0000-0002-6624-8530
authors.initials: RB
authors.lastname: Cody
authors.affiliation: Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.
authors.forename: Rabi A
authors.initials: RA
authors.lastname: Musah
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D015999:Multivariate Analysis; D027544:Salvia / Q000737:chemistry* / Q000145:classification; D013045:Species Specificity
chemical_list: D015415:Biomarkers
keywords: DART-MS; KDA; SIMCA; Salvia; ambient ionisation; essential oils; multivariate statistical analysis; sage; triterpenoids