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

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

A geographical analysis of trafficking on a popular darknet market

© 2017 Elsevier B.V. Cryptomarkets are online marketplaces, located on the darknet, that facilitate the trading of a variety of illegal goods, mostly drugs. While the literature essentially focus on drugs, various other goods and products related to financial or identity fraud, firearms, counterfeit goods, as well as doping products are also offered on these marketplaces. Through the analysis of relevant data collected on a popular marketplace in 2014–2015, Evolution, this research provides an analysis of the structure of trafficking (types and proportions of products, number of vendors and shipping countries). It also aims at highlighting geographical patterns in the trafficking of these products (e.g. trafficking flows, specialisation of vendors and assessment of their role in the distribution chain). The analysis of the flow of goods between countries emphasises the role of specific countries in the international and domestic trafficking, potentially informing law enforcement agencies to target domestic mails or international posts from specific countries. The research also highlights the large proportion of licit and illicit drug listings and vendors on Evolution, followed by various fraud issues (in particular, financial fraud), the sharing of knowledge (tutorials) and finally goods, currencies and precious metals (principally luxury goods). Looking at the shipping country, there seems to be a clear division between digital and physical products, with more specific information for physical goods. This reveals that the spatial analysis of trafficking is particularly meaningful in the case of physical products (such as illicit drugs) and to a lesser extent for digital products. Finally, the geographical analysis reveals that spatial patterns on Evolution tend to reflect the structure of the traditional illicit market. However, regarding illicit drugs, country-specificity has been observed and are presented in this article

Buying drugs on a Darknet market: A better deal? Studying the online illicit drug market through the analysis of digital, physical and chemical data.

Darknet markets, also known as cryptomarkets, are websites located on the Darknet and designed to allow the trafficking of illicit products, mainly drugs. This study aims at presenting the added value of combining digital, chemical and physical information to reconstruct sellers' activities. In particular, this research focuses on Evolution, one of the most popular cryptomarkets active from January 2014 to March 2015. Evolution source code files were analysed using Python scripts based on regular expressions to extract information about listings (i.e., sales proposals) and sellers. The results revealed more than 48,000 listings and around 2700 vendors claiming to send illicit drug products from 70 countries. The most frequent categories of illicit drugs offered by vendors were cannabis-related products (around 25%) followed by ecstasy (MDA, MDMA) and stimulants (cocaine, speed). The cryptomarket was then especially studied from a Swiss point of view. Illicit drugs were purchased from three sellers located in Switzerland. The purchases were carried out to confront digital information (e.g., the type of drug, the purity, the shipping country and the concealment methods mentioned on listings) with the physical analysis of the shipment packaging and the chemical analysis of the received product (purity, cutting agents, chemical profile based on minor and major alkaloids, chemical class). The results show that digital information, such as concealment methods and shipping country, seems accurate. But the illicit drugs purity is found to be different from the information indicated on their respective listings. Moreover, chemical profiling highlighted links between cocaine sold online and specimens seized in Western Switzerland. This study highlights that (1) the forensic analysis of the received products allows the evaluation of the accuracy of digital data collected on the website, and (2) the information from digital and physical/chemical traces are complementary to evaluate the practices of the online selling of illicit drugs on cryptomarkets

The study of doping market: how to produce intelligence from Internet forums

Despite the predominant role played by Internet in the distribution of doping substances, little is currently known about the online offer of doping products. Therefore, the study focuses on the detection of doping substances and suppliers discussed in Internet forums. It aims at having a comprehensive understanding of products and sellers to lead an operational monitoring of the online doping market. Thirteen community forums on the Internet were investigated and one million topics were extracted with source code scrappers. Then, a semantic analysis was conducted with a semi-automatic process to classify the relevant words according to doping matters. Additionally, the ranking of doping products, active substances and suppliers in regards to the number of contributors to the forums were established and analyzed over time. Finally, promotion methods of suppliers were evaluated. The results show that anabolic androgenic steroids, used to enhance body image and performance, are the most discussed type of products. A temporal analysis illustrates the stability of the most popular products as well as the emergence of new products such as peptides (e.g. CJC-1295). 327 suppliers were detected, mostly with dedicated websites or direct sales by e-mail as selling methods. Globally, the implemented methodology shows its ability to detect products and suppliers as well as to follow their temporal trends. The intelligence will serve the definition of online monitoring strategies (e.g. the selection of appropriate keywords). Additionally, it also allows the adjustment of customs inspection strategies and anti-doping analysis by monitoring the popular and emerging substances

Innovative methodology to transfer conventional GC-MS heroin profiling to UHPLC-MS/MS

Nowadays, in forensic laboratories, heroin profiling is frequently carried out by gas chromatography coupled with mass spectrometry (GC-MS). This analytical technique is well established, provides good sensitivity and reproducibility, and allows the use of large databases. Despite those benefits, recently introduced analytical techniques, such as ultra-high-pressure liquid chromatography (UHPLC), could offer better chromatographic performance, which needs to be considered to increase the analysis throughput for heroin profiling. With the latter, chromatographic conditions were optimized through commercial modeling software and two atmospheric pressure ionization sources were evaluated. Data obtained from UHPLC-MS/MS were thus transferred, thanks to mathematical models to mimic GC-MS data. A calibration and a validation set of representative heroin samples were selected among the database to establish a transfer methodology and assess the models' abilities to transfer using principal component analysis and hierarchical classification analysis. These abilities were evaluated by computing the frequency of successful classification of UHPLC-MS/MS data among GC-MS database. Seven mathematical models were tested to adjust UHPLC-MS/MS data to GC-MS data. A simplified mathematical model was finally selected and offered a frequency of successful transfer equal to 95%. Figur

Adversarial Matching of Dark Net Market Vendor Accounts

Many datasets feature seemingly disparate entries that actually refer to the same entity. Reconciling these entries, or matching, is challenging, especially in situations where there are errors in the data. In certain contexts, the situation is even more complicated: an active adversary may have a vested interest in having the matching process fail. By leveraging eight years of data, we investigate one such adversarial context: matching different online anonymous marketplace vendor handles to unique sellers. Using a combination of random forest classifiers and hierarchical clustering on a set of features that would be hard for an adversary to forge or mimic, we manage to obtain reasonable performance (over 75% precision and recall on labels generated using heuristics), despite generally lacking any ground truth for training. Our algorithm performs particularly well for the top 30% of accounts by sales volume, and hints that 22,163 accounts with at least one confirmed sale map to 15,652 distinct sellers---of which 12,155 operate only one account, and the remainder between 2 and 11 different accounts. Case study analysis further confirms that our algorithm manages to identify non-trivial matches, as well as impersonation attempts

Etude de l'approvisionnement d'une banque de données avec les résultats provenant de méthodes analytiques différentes dans le cadre du profilage chimique de produits stupéfiants

Dans les laboratoires forensiques, les analyses journalières réalisées sur les produits stupéfiants concernent identification, quantification et détermination de la signature chimique. Cette approche implique la création de banques de données compilant les résultats analytiques obtenus. Les banques de données de produits stupéfiants sont approvisionnées continuellement et permettent la recherche rétrospective de liens chimiques entre différentes saisies policières, non suspectés a priori lors de l'enquête policière. Ces renseignements soutiennent l'investigation des forces de police et doivent être combinés aux informations policières traditionnelles. A un niveau international, la stratégie prônée pour l'échange en temps réel d'informations liées aux profils chimiques consiste en la création de banques de données harmonisées et partagées par les laboratoires des pays participants. Pour y parvenir, l'utilisation d'une même méthode analytique est recommandée, celle-ci étant définie par ses technologies d'analyses de séparation et de détection, par l'appareillage sélectionné pour réaliser les analyses (marque et modèle) et par les paramètres analytiques décrivant chacune des technologies d'analyse. Cette approche s'avère contraignante et longue à mettre en place en raison du travail intensif en laboratoire requis pour obtenir des résultats comparables entre différents laboratoires. De plus, elle est problématique sur le long terme pour un laboratoire en raison de l'inertie analytique et de la perte d'informations qui en découlent. En effet, selon cette approche, il n'est pas possible d'implémenter une nouvelle méthode analytique tout en approvisionnant la même banque de données en raison de la nature différente des résultats. Il faut alors créer une nouvelle banque de données approvisionnée par la nouvelle méthode analytique et en conséquence mettre à zéro la mémoire de notre connaissance, établie durant plusieurs années. Dans ce travail de recherche, une méthodologie est ainsi proposée permettant la comparaison de résultats provenant de méthodes analytiques différentes dans l'optique de l'approvisionnement d'une banque de données par ces dernières

The cutting of cocaine and heroin: a critical review

The illicit drug cutting represents a complex problem that requires the sharing of knowledge from addiction studies, toxicology, criminology and criminalistics. Therefore, cutting is not well known by the forensic community. Thus, this review aims at deciphering the different aspects of cutting, by gathering information mainly from criminology and criminalistics. It tackles essentially specificities of cocaine and heroin cutting. The article presents the detected cutting agents (adulterants and diluents), their evolution in time and space and the analytical methodology implemented by forensic laboratories. Furthermore, it discusses when, in the history of the illicit drug, cutting may take place. Moreover, researches studying how much cutting occurs in the country of destination are analysed. Lastly, the reasons for cutting are addressed. According to the literature, adulterants are added during production of the illicit drug or at a relatively high level of its distribution chain (e.g. before the product arrives in the country of destination or just after its importation in the latter). Their addition seems hardly justified by the only desire to increase profits or to harm consumers' health. Instead, adulteration would be performed to enhance or to mimic the illicit drug effects or to facilitate administration of the drug. Nowadays, caffeine, diltiazem, hydroxyzine, levamisole, lidocaïne and phenacetin are frequently detected in cocaine specimens, while paracetamol and caffeine are almost exclusively identified in heroin specimens. This may reveal differences in the respective structures of production and/or distribution of cocaine and heroin. As the relevant information about cutting is spread across different scientific fields, a close collaboration should be set up to collect essential and unified data to improve knowledge and provide information for monitoring, control and harm reduction purposes. More research, on several areas of investigation, should be carried out to gather relevant information

Chemical profiling of different hashish seizures by gas chromatography-mass spectrometry and statistical methodology: a case report

Limited information is available regarding the methodology required to characterize hashish seizures for assessing the presence or the absence of a chemical link between two seizures. This casework report presents the methodology applied for assessing that two different police seizures were coming from the same block before this latter one was split. The chemical signature was extracted using GC-MS analysis and the implemented methodology consists in a study of intra- and inter-variability distributions based on the measurement of the chemical profiles similarity using a number of hashish seizures and the calculation of the Pearson correlation coefficient. Different statistical scenarios (i.e., a combination of data pretreatment techniques and selection of target compounds) were tested to find the most discriminating one. Seven compounds showing high discrimination capabilities were selected on which a specific statistical data pretreatment was applied. Based on the results, the statistical model built for comparing the hashish seizures leads to low error rates. Therefore, the implemented methodology is suitable for the chemical profiling of hashish seizures

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming 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 anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (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 wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties