Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.
Melissa A Bodnar Willard, Victoria L Mcguffin, Ruth Waddell Smith
Analytical and Bioanalytical Chemistry 2012 DOI: 10.1007/s00216-011-5479-0 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Comparative study Peer reviewed |
|---|---|
| Population | Salvia divinorum and four other Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, Salvia nemorosa) |
| Topics | Salvia divinorum |
| Keywords | Plant Hallucinogenic Chemical signature Compounds Chemical profiling Multivariate statistical procedures Discrimination Identify Forensic analysis Law enforcement |
| Citations | 15 |
| Key points | Salvia divinorum can be discriminated from four other Salvia species by visual assessment of chromatograms and PCA scores plots, with several statistical procedures providing numerical support for forensic applications. |
Abstract
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.