Physical analysis and chemical profiling of illicit herbal cannabis using multivariate analysis
An important aspect in forensic analysis is drug profiling. Information regarding the chemical properties of seized drug samples can be accumulated, which provides intelligence information to assist law enforcement agencies. Such information can be used to combat drug trafficking and abuse. In this...
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| Main Authors: | , , |
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| Format: | Article |
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School of Health Sciences, Universiti Sains Malaysia
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/59884/ http://forensics.org.my/mjofs/volume5no1.php |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | An important aspect in forensic analysis is drug profiling. Information regarding the chemical properties of seized drug samples can be accumulated, which provides intelligence information to assist law enforcement agencies. Such information can be used to combat drug trafficking and abuse. In this study, twenty three illicit cannabis samples seized from Selangor and suburbs of Kuala Lumpur which were submitted to the Department of Chemistry Malaysia were analyzed. All cannabis samples were extracted using methanol-chloroform mixture in a ratio of 9:1. High performance liquid chromatographic (HPLC) technique was used to separate cannabinoids in illicit herbal cannabis samples using Onyx Monolithic column. Mobile phase consisting of methanolwater (75:25) was used as the eluent at a flow rate of 0.8 mL/min and analytes detected at 220 nm. Analysis of reproducibility of retention time and peak area has validated the robustness of silica based monolithic column for HPLC analysis of cannabis. Peak areas of the cannabis extracts were used to profile illicit cannabis samples. Profiling of cannabis samples were established using cluster analysis and principal component analysis (PCA). Results from cluster analysis suggest that the illicit cannabis samples could have originated from five different geographical origins. Although PCA produced almost similar groupings like cluster analysis, but is not a suitable tool for analysing small set of data. PCA is more suited to decompose large data set with more variables. Classification model from this work suggests that plant material from one geographical origin can be trafficked by different means of route. |
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