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

Salvinorin A, the active compound in the hallucinogenic herb Salvia divinorum, was extracted from its leaves in five minutes using dichloromethane. Four other Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted the same way, and all extracts were analyzed by gas chromatography-mass spectrometry. Visual inspection of the chromatograms distinguished S. divinorum from the others. For a more objective comparison, the total ion chromatograms were processed with principal components analysis after data pretreatment to reduce non-chemical variance. The resulting scores plot also allowed visual discrimination. Numerical assessments using Euclidean distance, hierarchical cluster analysis, t tests, Wilcoxon rank-sum tests, and Pearson correlation were applied to the PCA scores, and these statistical procedures were compared for their forensic usefulness.

Study at a glance

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.