Identification and Classification of New Psychoactive Substances Using Raman Spectroscopy and Chemometrics

The sheer number, continuous emergence, heterogeneity and wide chemical and structural diversity of New Psychoactive Substance (NPS) products are factors being exploited by illicit drug designers to obscure detection of these compounds. Despite the advances in analytical techniques currently used by forensic and toxicological scientists in order to enable the identification of NPS, the lack of a priori knowledge of sample content makes it very challenging to detect an ‘unknown’ substance. The work presented in this thesis serves as a proof-of-concept by combining similarity studies, Raman spectroscopy and chemometrics, underpinned by robust pre-processing methods for the identification of existing or newly emerging NPS. It demonstrates that the use of Raman spectroscopy, in conjunction with a ‘representative’ NPS Raman database and chemometric techniques, has the potential for rapidly and non-destructively classifying NPS according to their chemical scaffolds. The work also demonstrates the potential of indicating the purity in formulations typical of those purchased by end users of the product i.e. ‘street-like’ mixtures. Five models were developed, and three of these provided an insight into the identification and classification of NPS depending on their purity. These are: the ‘NPS and non-NPS/benchtop’ model, the ‘NPS reference standards/handheld’ model and the ‘NPS and non-NPS/handheld’ model. In the ‘NPS and non-NPS/benchtop’ model (laser λex = 785 nm), NPS internet samples were projected onto a PCA model derived from a Raman database comprising ‘representative’ NPSs and cutting agent/ adulterant reference standards. This proved the most successful in suggesting the likely chemical scaffolds for NPS present in samples bought from the internet. In the ‘NPS reference standards/handheld’ model (laser λex = 1064 nm), NPS reference standards were projected onto a PCA model derived from a Raman database comprising ‘representative’ NPSs. This was the most successful of the three models with respect to the accurate ...