In Silico Infrared Characterization of Synthetic Cannabinoids by Quantum Chemistry and Chemometrics

A study of major cannabinoids via Raman spectroscopy and density functional theory

Cannabinoids, a class of molecules specific to the cannabis plant, are some of the most relevant molecules under study today due to their widespread use and varying legal status. Here, we present Raman spectra of a series of eleven cannabinoids and compare them to simulated spectra from density functional theory computations. The studied cannabinoids include three cannabinoid acids (Δ9-THC acid, CBD acid, and CBG acid) and eight neutral ones (Δ9-THC, CBD, CBG, CBDVA, CBDV, Δ8-THC, CBN and CBC). All cannabinoids have been isolated from cannabis plant gown at the University of Mississippi. The data presented in this work represents the most resolved experimental and highest-level simulated spectra available to date for each cannabinoid. All cannabinoids displayed higher peak separation in the experimental spectra than CBGA, which is most likely attributable to physical composition of the samples. The overall agreement between the experimental and simulated spectra is good, however for certain vibrational modes, especially those in the -OH stretching region, deviations are observed due to hydrogen bonding, suggesting that the OH stretching region is a good probe for decarboxylation reactions in these and related species.

ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances

Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and "other substances" based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87-1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different "linked groups". ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.