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                            Library & Multimedia
 Rapid Species-level Identification of Salvias by Chemometric Processing of Ambient Ionisation Mass Spectrometry-derived Chemical Profiles
NCJ Number
              252180
Journal

*Phytochemical Analysis* Volume: 28 Issue: 1 Dated: 2017 Pages: 16-26
Author(s)
          Justine E. Giffen;
                        Ashton D. Lesiak;
                        A. J. Dane;
                        Robert B. Cody;
                        Rabi A. Musah
Date Published

2017
Length
              11 pages
Annotation
              This article reports on the development of a simple high-throughput method of analyzing fresh and dried Salvia leaves that would permit rapid species-level identification and detection of diagnostic biomarkers.
Abstract
              Plant leaves were analyzed in their native form by DART-MS without the need for any sample preparation steps. This furnished chemical fingerprints characteristics 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 modeling of class analogy (SIMCA) to classify the fingerprints according to species. 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, a-pinene, B-theorem, B-caryophyllene, camphoe, and boneol 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. The study concludes that 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. (Publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2015-DN-BX-K057


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Development of Ambient Ionization Mass Spectrometric and Multivariate Statistical Analysis Methods for Rapid High Throughput Analysis and Identification of Psychotropic Plant Species
    
  
  
Date Published: January 1, 2017
    
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