Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species

• Published in: Analytical and bioanalytical chemistry Vol. 402; no. 2; pp. 833 - 842
• Main Authors: Bodnar Willard, Melissa A., McGuffin, Victoria L., Waddell Smith, Ruth
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 2012; Springer
• Subjects: Analytical Chemistry; Assessments; Biochemistry; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Chlorinated solvents; Chromatography; Discrimination; Diterpenes, Clerodane - analysis; Extraction; Food Science; Forensic engineering; Forensic Sciences - methods; Hallucinogenic drugs; Hallucinogens - analysis; Herbal medicine; Laboratory Medicine; Leaves; Monitoring/Environmental Analysis; Multivariate Analysis; Original Paper; Plant Leaves - chemistry; Principal Component Analysis; Salvia - chemistry; Salvia - classification; Species Specificity; Variance; Visual; Principal components analysis; Multivariate statistical procedures; Controlled substance identification; species; Salvinorin A
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-011-5479-0

Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum , was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species ( Salvia officinalis, Salvia guaranitica , Salvia splendens , and Salvia nemorosa ) were extracted using this procedure, and all extracts were analyzed by gas chromatography–mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student’s t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.