Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

• Published in: Molecules (Basel, Switzerland) Vol. 26; no. 21; p. 6643
• Main Authors: Slosse, Amorn, Van Durme, Filip, Samyn, Nele, Mangelings, Debby, Vander Heyden, Yvan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02-11-2021; MDPI
• Subjects: alignment optimization; Area Under Curve; Calibration; Cannabis; Cannabis - chemistry; chromatographic fingerprint; Chromatography; Chromatography, Gas - methods; Cocaine; comparison intra- and inter-location samples; Confidence limits; Data Analysis; data pre-processing; Datasets; design of experiments; Design optimization; Drug abuse; Fingerprints; Flowers & plants; Gas chromatography; Gas Chromatography-Mass Spectrometry; Heterogeneity; Illicit Drugs - analysis; Illicit Drugs - chemistry; Law enforcement; Marijuana; Plantations; Pretreatment; Reproducibility of Results; ROC Curve; Seizing; Tetrahydrocannabinol; THC; Thresholds; chromatographic fingerprint; alignment optimization; comparison intra- and inter-location samples; design of experiments; data pre-processing
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules26216643

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.