Identification and Classification of New Psychoactive Substances Using Raman Spectroscopy and Chemometrics
Creators: name: Guirguis, Amira
Publisher: name: UH Research Archive
Year: 2017
Subjects: New psychoactive substances (NPS)
Raman
spectroscopy
handheld
chemometrics
principal components analysis (PCA)
pre-processing
adulterants
cutting agents
mixtures
identification
detection
classification

description: 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 identification of pure NPS. This model suggested chemical scaffolds in 89% of samples compared to 76% obtained with the benchtop instrument, which generally had higher fluorescent backgrounds. In the ‘NPS and non-NPS/handheld’ model (laser λex = 1064 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 was the most successful in differentiating between NPS internet samples dependent on their purity. In all models, the main challenges for identification of NPS were spectra displaying high fluorescent backgrounds and low purity profiles. The ‘first pass’ matching identification of NPS internet samples on a handheld platform was improved to ~50% using a laser source of 1064 nm because of a reduction in fluorescence at this wavelength. We outline limitations in using a handheld platform that may have added to problems with appropriate identification of NPS in complex mixtures. However, the developed models enabled the appropriate selection of Raman signals crucial for identification of NPS via data reduction, and the extraction of important patterns from noisy and/or corrupt data. The models constitute a significant contribution in this field with respect to suggesting the likely chemical scaffold of an ‘unknown’ molecule. This insight may accelerate the screening of newly emerging NPS in complex matrices by assigning them to: a structurally similar known molecule (supercluster/ cluster); or a substance from the same EMCDDA/EDND class of known compounds. Critical challenges in instrumentation, chemometrics, and the complexity of samples have been identified and described. As a result, future work should focus on: optimising the pre-processing of Raman data collected with a handheld platform and a 1064 nm laser λex; and optimising the ‘representative’ database by including other properties and descriptors of existing NPS.