Detection, discrimination and quantification of amphetamine, cathinone and nor-ephedrine regioisomers using benchtop 1 H and 19 F nuclear magnetic resonance spectroscopy.

Amphetamine and cathinone derivatives are abused recreationally due to the sense of euphoria they provide to the user. Methodologies for the rapid detection of the drug derivative present in a seized sample, or an indication of the drug class, are beneficial to law enforcement and healthcare providers. Identifying the drug class is prudent because derivatisation of these drugs, to produce regioisomers, for example, occurs frequently to circumvent global and local drug laws. Thus, newly encountered derivatives might not be present in a spectral library. Employment of benchtop nuclear magnetic resonance (NMR) could be used to provide rapid analysis of seized samples as well as identifying the class of drug present. Discrimination of individual amphetamine-, methcathinone-, N-ethylcathinone and nor-ephedrine-derived fluorinated and methylated regioisomers is achieved herein using qualitative automated 1 H NMR analysis and compared to gas chromatography-mass spectrometry (GC-MS) data. Two seized drug samples, SS1 and SS2, were identified to contain 4-fluoroamphetamine by 1 H NMR (match score median = 0.9933) and GC-MS (RRt = 5.42-5.43 min). The amount of 4-fluoroamphetamine present was 42.8%-43.4% w/w and 48.7%-49.2% w/w for SS1 and SS2, respectively, from quantitative 19 F NMR analysis, which is in agreement with the amount determined by GC-MS (39.9%-41.4% w/w and 49.0%-49.3% w/w). The total time for the qualitative 1 H NMR and quantitative 19 F NMR analysis is ~10 min. This contrasts to ~40 min for the GC-MS method. The NMR method also benefits from minimal sample preparation. Thus, benchtop NMR affords rapid, and discriminatory, analysis of the drug present in a seized sample.

Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils.

Low field (60 MHz) 1H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment.

High-throughput screening of argan oil composition and authenticity using benchtop 1 H NMR.

We use 60-MHz benchtop nuclear magnetic resonance (NMR) to acquire 1 H spectra from argan oils of assured origin. We show that the low-field NMR spectrum of neat oil contains sufficient information to make estimates of compositional parameters and to inform on the presence of minor compounds. A screening method for quality and authenticity is presented based on nearest-neighbour outlier detection. A variety of oil types are used to challenge the method. In a survey of retail-purchased oils, several instances of fraud were found.

Rapid Identification of Novel Psychoactive and Other Controlled Substances Using Low-Field 1H NMR Spectroscopy.

An automated approach to the collection of 1H NMR (nuclear magnetic resonance) spectra using a benchtop NMR spectrometer and the subsequent analysis, processing, and elucidation of components present in seized drug samples are reported. An algorithm is developed to compare spectral data to a reference library of over 300 1H NMR spectra, ranking matches by a correlation-based score. A threshold for identification was set at 0.838, below which identification of the component present was deemed unreliable. Using this system, 432 samples were surveyed and validated against contemporaneously acquired GC-MS (gas chromatography-mass spectrometry) data. Following removal of samples which possessed no peaks in the GC-MS trace or in both the 1H NMR spectrum and GC-MS trace, the remaining 416 samples matched in 93% of cases. Thirteen of these samples were binary mixtures. A partial match (one component not identified) was obtained for 6% of samples surveyed whilst only 1% of samples did not match at all.