Article

The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools.

Institut de Police Scientifique, School of Criminal Sciences, Batochime, University of Lausanne, 1015 Lausanne-Dorigny, Switzerland.
Forensic science international (Impact Factor: 2.1). 04/2010; 200(1-3):87-92. DOI: 10.1016/j.forsciint.2010.03.034
Source: PubMed

ABSTRACT Cannabis cultivation in order to produce drugs is forbidden in Switzerland. Thus, law enforcement authorities regularly ask forensic laboratories to determinate cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. As required by the EU official analysis protocol the THC rate of cannabis is measured from the flowers at maturity. When laboratories are confronted to seedlings, they have to lead the plant to maturity, meaning a time consuming and costly procedure. This study investigated the discrimination of fibre type from drug type Cannabis seedlings by analysing the compounds found in their leaves and using chemometrics tools. 11 legal varieties allowed by the Swiss Federal Office for Agriculture and 13 illegal ones were greenhouse grown and analysed using a gas chromatograph interfaced with a mass spectrometer. Compounds that show high discrimination capabilities in the seedlings have been identified and a support vector machines (SVMs) analysis was used to classify the cannabis samples. The overall set of samples shows a classification rate above 99% with false positive rates less than 2%. This model allows then discrimination between fibre and drug type Cannabis at an early stage of growth. Therefore it is not necessary to wait plants' maturity to quantify their amount of THC in order to determine their chemotype. This procedure could be used for the control of legal (fibre type) and illegal (drug type) Cannabis production.

0 Bookmarks
 · 
63 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, we compare the performance of several methods belonging to two different classes of multivariate techniques: supervised and Unsupervised Techniques. The Supervised Techniques (ST) are the k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Probabilistic Neural Networks (PNN) and Linear Discriminant Analysis (LDA); the Unsupervised Techniques are the k-Means CA and the Fuzzy C-Means (FCM). The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR) from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA) and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.
    Egyptian Journal of Forensic Sciences. 09/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Accessions of wild and domesticated hemp (Cannabis sativa L.) originating from Colombia, Mexico, California, Bolivia, Thailand, Afghanistan, Serbia, Hungary, south Africa and different regions of China, were studied by means of DNA polymorphisms in order to discriminate between drug and fiber types. Analysis of molecular variance (AMOVA) was used to partition the total genetic variance within and among populations. The significance of the variance components was tested by calculating their probabilities based on 999 random permutations. AMOVA revealed 74 % variation among accessions and 26 % within accessions, all AMOVA variation was highly significant (P < 0.001). The cluster analysis of molecular data, grouped accessions into eight clusters and gave a matrix correlation value of r = 0.943, indicating a very good fit between the similarity values implied by the phenogram and those of the original similarity matrix. In this study, DNA polymorphisms could discriminate the fiber and drug types, and accessions were grouped in accordance to their classification and uses. In addition, seed size variation and micromorphological characters of seeds were studied by means of a scanning electron microscope (SEM). Seeds varied significantly in size, and were bigger in the fiber types. SEM analysis exhibited variation of micromorphological characters of seeds that could be important for discriminating the fiber or drug types.
    Genetic Resources and Crop Evolution 01/2013; · 1.59 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Lipids available in fingermark residue represent important targets for enhancement and dating techniques. While it is well known that lipid composition varies among fingermarks of the same donor (intra-variability) and between fingermarks of different donors (inter-variability), the extent of this variability remains uncharacterized. Thus, this work aimed at studying qualitatively and quantitatively the initial lipid composition of fingermark residue of 25 different donors. Among the 104 detected lipids, 43 were reported for the first time in the literature. Furthermore, palmitic acid, squalene, cholesterol, myristyl myristate and myristyl myristoleate were quantified and their correlation within fingermark residue was highlighted. Ten compounds were then selected and further studied as potential targets for dating or enhancement techniques. It was shown that their relative standard deviation was significantly lower for the intra-variability than for the inter-variability. Moreover, the use of data pre-treatments could significantly reduce this variability. Based on these observations, an objective donor classification model was proposed. Hierarchical cluster analysis was conducted on the pre-treated data and the fingermarks of the 25 donors were classified into two main groups, corresponding to "poor" and "rich" lipid donors. The robustness of this classification was tested using fingermark replicates of selected donors. 86% of these replicates were correctly classified, showing the potential of such a donor classification model for research purposes in order to select representative donors based on compounds of interest.
    Forensic science international 02/2014; 238C:68-82. · 2.10 Impact Factor