Toward the identification of marijuana varieties by headspace chemical forensics

In vivo profiling of phytocannabinoids in Cannabis spp. varieties via SPME-LC-MS analysis

BACKGROUND

In vivo solid-phase microextraction (SPME) is a minimally invasive, non-exhaustive sample-preparation technique that facilitates the direct isolation of low molecular weight compounds from biological matrices in living systems. This technique is especially useful for the analysis of phytocannabinoids (PCs) in plant material, both for forensic purposes and for monitoring the PC content in growing Cannabis spp. plants. In contrast to traditional extraction techniques, in vivo SPME enables continuous tracking of the changes in the level of PCs during plant growth without the need for plant material collection. In this study, in vivo SPME utilizing biocompatible C18 probes and liquid-chromatography coupled to quadrupole time-of flight mass spectrometry (LC-Q-TOF-MS) is proposed as a novel strategy for the extraction and analysis of the acidic forms of five PCs in growing medicinal cannabis plants.

RESULTS

The SPME method was optimized by testing various parameters, including the extraction phase (coating), extraction and desorption times, and the extraction temperature. The proposed method was validated with satisfactory analytical performance regarding linearity (10-3000 ng/mL), limits of quantification, and precision (relative standard deviations below 5.5 %). The proposed method was then successfully applied for the isolation of five acidic forms of PCs, which are main components of growing medicinal cannabis plants. As a proof-of-concept, SPME probes were statically inserted into the inflorescences of two varieties of Cannabis spp. plants (i.e., CBD-dominant and Δ9-THC-dominant) cultivated under controlled conditions for 30 min extraction of tetrahydrocannabinolic acid (Δ9-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabiviarinic acid (CBVA), and tetrahydrocannabivarinic acid (THCVA).

SIGNIFICANCE AND NOVELTY

The results confirmed that the developed SPME-LC-Q-TOF-MS method is a precise and efficient tool that enables direct and rapid isolation and analysis of PCs under in vivo conditions. The proposed methodology is highly appealing option for monitoring the metabolic pathways and compositions of multiple PCs in medicinal cannabis at different stages of plant growth.

SPME as a green sample-preparation technique for the monitoring of phytocannabinoids and endocannabinoids in complex matrices

The endocannabinoid system (ECS), particularly its signaling pathways and ligands, has garnered considerable interest in recent years. Along with clinical work investigating the ECS' functions, including its role in the development of neurological and inflammatory conditions, much research has focused on developing analytical protocols enabling the precise monitoring of the levels and metabolism of the most potent ECS ligands: exogenous phytocannabinoids (PCs) and endogenous cannabinoids (endocannabinoids, ECs). Solid-phase microextraction (SPME) is an advanced, non-exhaustive sample-preparation technique that facilitates the precise and efficient isolation of trace amounts of analytes, thus making it appealing for the analysis of PCs and ECs in complex matrices of plant and animal/human origin. In this paper, we review recent forensic medicine and toxicological studies wherein SPME has been applied to monitor levels of PCs and ECs in complex matrices, determine their effects on organism physiology, and assess their role in the development of several diseases.

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

Analysis of the Volatile Components in Selaginella doederleinii by Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Selaginella doederleinii (SD) is a perennial medicinal herb widely distributed in China. In this study, the volatile components of SD from two regions (24 batches), namely Zhejiang and Guizhou, were determined by combining headspace solid phase microextraction and gas chromatography-mass spectrometry (HS-SPME/GC-MS). After investigating different influence factors, the optimal conditions for extraction were as follows: The sample amount of 1 g, the polydimethylsiloxane-divinylbenzene (PDMS-DVB) fiber of 65 µm, the extraction time of 20 min, and the extraction temperature of 100 °C. Based on the above optimum conditions, 58 volatiles compounds, including 20 terpenes, 11 alkanes, 3 alcohols, 6 ketones, 3 esters, 11 aldehydes, 1 ether, 1 aromatic, 1 phenol, and 1 furan, were found and identified in SD. Furthermore, hierarchical cluster analysis and principal component analysis were successfully applied to distinguish the chemical constituents of SD from two regions. Additionally, anethol, zingerone, 2,4-di-tert-butylphenol, ledene, hexyl hexanoate, α-cadinol, phytone, hinesol, decanal, octadecene, cedren, 7-tetradecene, copaene, β-humulene, 2-butyl-2-octenal, tetradecane, cedrol, calacorene, 6-dodecanone, β-caryophyllene, 4-oxoisophorone, γ-nonanolactone, 2-pentylfuran, 1,2-epoxyhexadecane, carvacrol, n-pentadecane, diisobutyl phthalate, farnesene, n-heptadecane, linalool, 1-octen-3-ol, phytane, and β-asarone were selected as the potential markers for discriminating SD from 24 habitats in Zhejiang and Guizhou by partial least squares discrimination analysis (PLS-DA). This study revealed the differences in the components of SD from different regions, which could provide a reference for the future quality evaluation.