Identification of new psychoactive substances (NPS) by Raman spectroscopy

Abstract

There is an increasing need of developing methods for fast recognition and identification of new psychoactive substances (NPS). The chemical identification of these new substances produced with the intention of mimicking the effects of controlled drugs is a challenge for forensic and Customs laboratories. In this study, we aim to test the potential of Raman spectroscopy for the identification and classification of seized Customs samples into three NPS families. The performance of two excitation wavelength lasers (785 and 1064 nm) in a benchtop Raman instrument was compared in a set of seized samples that included cathinone, fentanyl, and synthetic cannabinoid derivatives or analogues. The 1064 nm wavelength laser had significant advantages for identifying NPS samples overcoming the intense fluorescence induced when using 785 nm lasers in some substances. Principal component analysis was employed to create a model that successfully discriminates the three NPS families. In order to provide Customs officers with a fast and nondestructive in‐field testing method, the same approach used with the benchtop Raman spectrometer was applied using three handheld Raman instruments. The developed identification and classification model allows the discrimination of fentanyl, cathinone, and synthetic cannabinoid analogues or derivatives providing an efficient tool for the rapid identification of three NPS families. The approach presented in this study can facilitate rapid decision‐making that could be of high relevance especially in the frame of the fentanyl crisis.

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples

“…Both are well‐known effects in direct spectroscopic analysis on solid samples caused by particle size differences, light scattering, and spectral interferences. Spectral preprocessing is a common strategy to extract useful information from the raw spectral data and remove nonselective systematic and random signal fluctuations 24,25,37 . Figure 2 shows the (a) raw spectral data, (b) data after SNV preprocessing, (c) subsequent smoothing, and (d) a 1,560‐ to 1,756‐cm −1 selection with baseline correction for both cocaine HCl and cocaine base.…”

“…Spectral preprocessing is a common strategy to extract useful information from the raw spectral data and remove nonselective systematic and random signal fluctuations. 24,25,37 Figure 2 shows the (a) raw spectral data, (b) data after SNV preprocessing, (c) subsequent smoothing, and (d) a 1,560-to 1,756-cm −1 selection with baseline correction for both cocaine HCl and cocaine base. Spectra of the cutting agents levamisole, paracetamol, and procaine are shown in the same plots to demonstrate the spectral selectivity.…”

“…Ali and Edwards demonstrated the use of principal component analysis (PCA) on Raman spectra to distinguish clothing samples impregnated with either cocaine, MDMA or amphetamine 11 . Both Muhamadali et al 23 and Omar et al 24 applied PCA for group classification of various new psychoactive substances (NPS). The spectral selectivity of Raman spectroscopy for pure drug substance detection and classification was demonstrated by Calvo Castro et al, who analyzed a large set of 478 NPS and visualized several group‐specific features using PCA 25 .…”

Recent advances in linear and nonlinear Raman spectroscopy. Part XIV

“…Their approach is applicable where an analyte is either a minor contributor or is spatially distributed on a strongly interfering substrate. Omar et al [ 175 ] reported the use of Raman to identify new psychoactive substances (NPS). Principal component analysis was employed to create a model that successfully discriminates three major NPS families.…”

Preliminary study on the identification of synthetic cathinones in street seized samples by Raman spectroscopy and chemometrics

“…For an extensive review on chemometric applications in forensics, refer to Silva et al [ 12 ] and Sena et al [ 18 ] Recently, three studies reported the use of Raman spectroscopy and Principal Component Analysis (PCA) to evaluate differences and similarities among NPS samples and improve chemical characterization. [ 15,19,20 ] In another study, [ 21 ] Partial Least Squares Discriminant Analysis (PLS‐DA) was employed to classify NPS infrared spectral data. Although these studies showed the great potential of the spectroscopic techniques to analyze a diversity of NPS samples, the chemometric models presented were not robust for field applications, and their performance was negatively influenced by the diversity of chemical components usually found in street NPS samples.…”