4.6 Article

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

Journal

MOLECULES
Volume 26, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/molecules26216643

Keywords

chromatographic fingerprint; alignment optimization; design of experiments; data pre-processing; comparison intra- and inter-location samples

Funding

  1. European Union's Horizon 2020 research and innovation programme [833787]
  2. Fund for Scientific Research (FWO), Vlaanderen, Belgium [G033816N]

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The study aimed to evaluate the variability of illicit Cannabis samples using GC-FID and GC-MS herbal fingerprints, and establish thresholds for linking seized samples. By optimizing fingerprint-alignment parameters and using different data pre-treatments, improvements in discrimination between seized Cannabis samples were achieved. The study also highlighted the importance of accurate data pre-processing and calibration in enhancing the reliability of the results.
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.

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