Simultaneous quantification of THC‐COOH, OH‐THC, and further cannabinoids in human hair by gas chromatography–tandem mass spectrometry with electron ionization applying automated sample preparation
Tobias Kieliba, Oliver Lerch, Hilke Andresen‐Streichert, Markus A. Rothschild, Justus Beike
- 发表年份
- 2018
- 引用次数
- 23
摘要
Abstract The detection of Δ 9 ‐tetrahydrocannabinol (THC), cannabidiol (CBD) and cannabinol (CBN) in hair, for the purpose of identifying cannabis consumption, is conducted in many forensic laboratories. Since external contamination of hair with these cannabis components cannot be excluded, even after hair decontamination, only the detection of THC metabolites such as 11‐nor‐9‐carboxy‐Δ 9 ‐tetrahydrocannabinol (THC‐COOH) or 11‐hydroxy‐Δ 9 ‐tetrahydrocannabinol (OH‐THC), is considered to prove cannabis consumption. At present, testing for THC metabolites is not standard practice due to its analytical complexity. For these reasons, we developed a novel method for the detection of THC‐COOH and OH‐THC as well as THC, CBD, and CBN in one single analytical run using gas chromatography–tandem mass spectrometry (GC–MS/MS) with electron ionization. After manual hair washing and grinding, sample preparation was fully automated, by means of a robotic autosampler. The hair extraction took place by digestion with sodium hydroxide. A solid‐phase extraction (SPE) was chosen for sample clean‐up, using a mixed‐mode anion exchange sorbent. Derivatization of all analytes was by silylation. The method has been fully validated according to guidelines of the Society of Toxicological and Forensic Chemistry (GTFCh), with a limit of detection (LOD) of 0.2 pg/mg for THC‐COOH and OH‐THC and 2 pg/mg for THC, CBD and CBN, respectively, thus fulfilling the Society of Hair Testing (SoHT) recommendations. The validated method has been successfully applied to our routine forensic case work and a summary of data from authentic hair samples is given, as well as data from proficiency tests.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992