Multi-class differentiation of cannabis seedlings in a forensic context

Abstract

This article presents an experimental study about the classification ability of several classifiers for multi-class classification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland law enforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabis plant and then to conclude if the plantation is legal or not. This classification is mainly performed when the plant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a time consuming and costly procedure. A previous study made by the authors has investigated this problematic [1] and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at an early stage of growth using gas chromatography interfaced with mass spectrometry (GC–MS) based on the relative proportions of eight major leaf compounds. The aims of the present work are on one hand to continue former work and to optimize the methodology for the discrimination of drug- and fibre type cannabis developed in the previous study and on the other hand to investigate the possibility to predict illegal cannabis varieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namely Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest Neighbour Classification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines (RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method was assessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabis with drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiers are not able to manage the distribution of classes in which some overlap areas exist for both classification problems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and 12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBF SVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabis plants coming from police seizures. In forensic case work this study shows that the discrimination between cannabis samples at an early stage of growth is possible with fairly high classification performance for discriminating between cannabis chemotypes or between drug type cannabis varieties.