P. Esseiva

A methodology for illicit heroin seizures comparison in a drug intelligence perspective using large databases

To characterise links between different illicit drugs chemical profiles, various distance or correlation measurements are available.Different comparison methods have been tested and a method based on a correlation coefficient using a square cosine function was chosen to compare heroin chemical profiles. Its functioning and graphical representation are described. An assessment of the number of false positives is calculated and lead to a negligible number.Moreover, it emerges from the studies that possible variations in impurity peak areas subject to possible degradations do not influence the C correlation value nor question the already established links. This solid, reliable and simple method appears therefore suitable for heroin samples comparison, links profiling and routine use.

Cocaine profiling for strategic intelligence, a cross-border project between France and Switzerland: Part II. Validation of the statistical methodology for the profiling of cocaine

Harmonisation and optimization of analytical and statistical methodologies were carried out between two forensic laboratories (Lausanne, Switzerland and Lyon, France) in order to provide drug intelligence for cross-border cocaine seizures. Part I dealt with the optimization of the analytical method and its robustness. This second part investigates statistical methodologies that will provide reliable comparison of cocaine seizures analysed on two different gas chromatographs interfaced with a flame ionisation detectors (GC-FIDs) in two distinct laboratories. Sixty-six statistical combinations (ten data pre-treatments followed by six different distance measurements and correlation coefficients) were applied. One pre-treatment (N+S: area of each peak is divided by its standard deviation calculated from the whole data set) followed by the Cosine or Pearson correlation coefficients were found to be the best statistical compromise for optimal discrimination of linked and non-linked samples. The centralisation of the analyses in one single laboratory is not a required condition anymore to compare samples seized in different countries. This allows collaboration, but also, jurisdictional control over data.

Applications of a transportable Raman spectrometer for the in situ detection of controlled substances at border controls

A transportable Raman spectrometer was tested for the detection of illicit drugs seized during border controls. In a first step, the analysis methodology was optimized using reference substances such as diacetylmorphine (heroin), cocaine and amphetamine (as powder or liquid forms). Adequate focalisation distance and times of analysis, influence of daylight and artificial light sources, repeatability and limits of detection were studied. In a second step, the applications and limitations of the technique to detect the illicit substances in different mixtures and containers were evaluated. Transportable Raman spectroscopy was found to be adequate for a rapid screen of liquids and powders for the detection and identification of controlled substances. Additionally, it had the advantage over other portable techniques, such as ion mobility spectrometry, of being non-destructive and capable of rapid analysis of large quantities of substances through containers such as plastic bags and glass bottles.

Bayesian classification criterion for forensic multivariate data

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plants chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.

Analysis of illicit drugs in wastewater - is there an added value for law enforcement?

Assessing illicit drug use through the analysis of wastewater is progressively being integrated into existing methods used to monitor the epidemiology of drug use. However, the approachs potential to deliver pertinent information for law enforcement has been discussed only limitedly. Thus, this work focuses on evaluating the added value of the approach from the perspective of law enforcement. Results from wastewater analysis carried out in two cities in Switzerland were scrutinised, taking into account intelligence derived from the work of drug enforcement in the area. Focus was set on three substances, namely cocaine, heroin and methamphetamine. Findings show that results from wastewater analysis can be used by law enforcement to assess the market share held by criminal groups. Combined with intelligence resulting from police work (e.g., investigations and informants), wastewater analysis can contribute to deciphering the structure of drug markets, as well as the local organisation of trafficking networks. The results presented here constitute valuable pieces of information, which can be used by law enforcement to guide decisions at strategic and/or operational levels. Furthermore, intelligence gathered through investigations and surveillance constitutes an alternative viewpoint to evaluate results of wastewater analysis.