Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

Abstract

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.

Analytical strategies for herbal Cannabis samples in forensic applications: A comprehensive review

“…The United Nations Office of Drugs and Crime (UNODC) also defined guidelines to handle herbal material, based on the previously mentioned European method (United Nations Office on Drugs and Crime, 2013). Other laboratories reported yet another approach, that is, plant samples are dried at 40°C in an oven for at least 12 h (Hädener, König, & Weinmann, 2019; Slosse et al, 2021; Tipparat et al, 2012). Moreover, the dried material has a heterogeneous composition (European Network of Forensic Science Institutes—Drugs Working Group, 2014).…”

“…It aims to find links between seizures, based on the chemical composition of the samples (Collins et al, 2007). Consequently, a Cannabis profiling approach, consisting of a combination of multivariate data analysis and statistical methodology, was developed by Slosse et al (2020Slosse et al ( , 2021. It needs to be emphasized that herbal Cannabis is a natural product, meaning that its chemical content is very dependent on many growth factors.…”

Cannabis profiling of seized samples: An intra‐location variability study using gas chromatography–mass spectrometry profiles and multivariate data analysis

“…[10][11][12] However, published studies with regard to the profiling of cannabis are scarce, although it remains the number one confiscated illicit drug. 13,14 Worldwide, Cannabis sativa L., a flowering annual plant from the Cannabaceae family, remains the most frequently consumed drug. 6,15 The recreational use is still prohibited, except in Uruguay, Canada and 12 states of the United States (but federally forbidden).…”

“…For the data analysis, different data preprocessing, followed by Pearson correlation coefficient (PCC) calculations were then used. 13,14 This approach, like other illicit drug profiling approaches, aims to determine a decision threshold which discriminates or connects seized material. 19 To obtain a reliable threshold value, it is important to select appropriate data sets containing known linked (true positives [TPs]) and unlinked (true negatives [TNs]) samples.…”

“…Even more, plants from the same cultivation room, can already show a high variability. 13,14,25 The Drugs Working Group of the European Network of Forensic Science Institutes also stated that the maximum heterogeneity of Δ 9 -tetrahydrocannabinol (THC) on about 1 g of cannabis buds is 30%. 26 Hence, to show an accurate result of a link or not between samples, it is very important for the profiling model to take this naturally occurring variation into account.…”