Thomas Coombs Amor Abdelkader Dhiya Al-Jumeily https://orcid.org/0000-0002-9170-0568 Sulaf Assi https://orcid.org/0000-0002-5142-9179


The last decade has witnessed the emergence of new psychoactive substances that are analogues of classical drugs of abuse in order to escape the regulations surrounding the latter drugs. These drugs were of both herbal and synthetic origin and were advertised initially as ‘legal highs’; thus, they were perceived as safe by users. Hence, upon their emergence, they were not controlled by the Misuse of Drugs Act 1971, which contributed to their popularity and increased sales online and within street markets. In 2016, the Psychoactive Substance Act introduced a blanket ban on all new psychoactive substances except for caffeine, alcohol, and nicotine. This in turn, contributed to the change in the sale of new psychoactive substances products that have been sold as concealed in different matrices, including herbal products, papers, fabrics, and textiles. Concealing drugs in paper has been very popular, especially since the drug product is of lightweight and can be sent via postal services. However, new psychoactive concealed in papers are toxic not only to the users; but also, to the person handling them (i.e. mail employees). One of the classes of new psychoactive substances that have been commonly concealed in papers and that have been linked to toxicity and hospitalization cases is synthetic cannabinoids. Therefore, there is a need to identify new psychoactive substances concealed in papers non-destructively and rapidly to prevent toxicity linked to them. Handheld Raman spectroscopy offers this advantage as it is of lightweight and carries the sample to the matrix. Therefore, this work used handheld Raman spectroscopy for identifying synthetic cannabinoids concealed in papers using Raman spectroscopy combined with machine learning analytics. Synthetic cannabinoid and paper samples were measured non-destructively using a handheld Raman spectrometer equipped with a 1064 nm laser wavelength. Spectral data was exported into Matlab 2020b where machine learning analytics including identification and prediction was. The results showed that Raman spectroscopy could identify specific synthetic cannabinoids in papers that were either deposited on the surface of the paper or diffused inside the paper substrate. When machine learning analytics were applied to the Raman spectra of the papers, quantitative information was obtained regarding the amount of synthetic cannabinoid deposited on the paper surface. In summary, handheld Raman spectroscopy could identify and quantify synthetic cannabinoids on paper rapidly and non-destructively. Future work involves testing other classes of new psychoactive substance and applying deep learning analytics


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