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The large numbers of 3,4-methylenedioxy-N-methylamphetamine (MDMA) formulations encountered by the police and border security necessitates the need for safe, rapid and reliable tests to be performed on-site. Near-infrared (NIR) spectroscopy is a promising technique for on-scene illicit-drug detection because of its rapid analysis, non-invasive nature, broad scope to detect various substances, and small-sized sensors suitable for portable operation. The NIR spectrum of MDMA shows an intriguing, intense peak at ∼2000 nm that was found characteristic for MDMA within a large set of drugs and drug-related substances. Herein, we show that this peak can be attributed to water molecules of crystallization in the MDMA lattice. Drying experiments showed that both an anhydrous and hydrated form of MDMA·HCl exists with significantly different NIR spectra. At ambient conditions, the anhydrous form converted back to the hydrated form within 2 months. Our data analysis model was able to identify MDMA·HCl in mixtures of both forms. Assessment of seized casework materials showed that the majority of MDMA·HCl in The Netherlands is of the hydrated type. This is explained by the use of water-containing concentrated hydrochloric acid in the final conversion step of MDMA-base to the hydrochloride salt in clandestine laboratories. These findings provide insight in the challenges associated with NIR-based identification of drugs that may appear in various crystalline forms. Awareness on the existence of these forms and the consequences of library and data-model design to cope with this phenomenon will increase the robustness of on-site NIR-based drug detection.

Forensic drug laboratories are confronted with increasing amounts of drugs and a demand for faster results that are directly available on-site. In addition, the drug market is getting more complex with hundreds of new psychoactive substances (NPS) entering the market in recent years. Rapid and on-scene presumptive drug testing therefore faces a shift from manual colorimetric tests towards approaches that can detect a wider range of components and process results automatically. Electrochemical detection offers these desired characteristics, making it a suitable candidate for on-site drug detection. In this study, a two-step electrochemical sensor is introduced for the detection of MDMA and 2C-B. Firstly, a direct electrochemical analysis was performed to detect MDMA. Validation experiments on over 70 substances revealed that 2C-B was the only frequently encountered drug that gave a false positive result for MDMA in this first analysis. A second step using in-situ derivatization was subsequently introduced. To this end, formaldehyde was used for N-methylation of 2C-B thereby enhancing its electrochemical profile. The enriched electrochemical fingerprint in the second step allowed for clear differentiation between MDMA and 2C-B. The applicability of this approach was demonstrated with 71 ecstasy tablets seized by the Amsterdam Police. The MDMA/2C-B sensor correctly identified all 39 MDMA-containing tablets and 10 out of 11 tablets containing 2C-B. Most notably, correct results were also obtained for dark colored tablets in which both spectroscopic analysis and colorimetric tests failed due to obscured signals.

List-based approaches for judicial control of synthetic drugs inevitably introduce a group of legal highs that do not fall under the scope of legislation but may exhibit similar effects and associated health risks as illicit substances. Differences between controlled and uncontrolled components may be as minor as a single molecular group rearrangement. This phenomenon complicated forensic drug analysis in recent years due to both the rise of new psychoactive substances (NPS), and selectivity limitations of the workhorse gas chromatography-mass spectrometry (GC−MS) technique, especially with respect to ring-isomers. Our study demonstrates the value of GC-solid deposition-Fourier-transform infrared spectroscopy (FTIR) as a complementary technique for NPS identification in multi-drug mixtures. The instrument design using direct deposition of the GC effluent on a cryogenically cooled ZnSe-disk allows for signal enhancement of minor constituents by collecting eluting peaks of multiple GC injections. Highly diagnostic spectra were obtained for all ortho, meta and para-isomers of fluoroamphetamine (FA), fluoromethamphetamine (FMA), methylmethcathinone (MMC) and methylethcathinone (MEC). Combined results of GC−MS and GC-solid deposition-FTIR revealed the presence of up to 11 individual NPS mixed together in liquid samples sold as research chemicals or room odorizer in The Netherlands. Sample compositions rapidly evolved over time with recently controlled substances such as 4-fluoroamphetamine (4-FA), pentedrone, ethylone and 4-methylethcathinone (4-MEC) being replaced by uncontrolled isomers or analogues as 2-fluoroamphetamine (2-FA), 2- or 4-fluoromethamphetamine (2-FMA, 4-FMA) and dimethylone. In 12 different samples all marketed under two brand names, a total of 9 different compositions were identified in samples from 2018 and 2019.

Forensic examination of fire debris evidence is a notoriously difficult analytical task due to the complexity and variability of sample composition. The use of comprehensive two-dimensional gas chromatography with mass spectrometry detection (GC × GC–MS) allows the coupling of orthogonal retention mechanisms and therefore a high peak capacity.We demonstrate recent innovations in combining chemometric techniques for data reduction and feature selection, with evaluation of the evidence for forensic questions pertaining to the detection and subsequent classification of ignitable liquid residue (ILR) in fire debris samples. Chromatograms are divided into non-overlapping spatially delimited regions; for each of these regions a Local Ion Signature (LIS) is computed by summing the intensities, per nominal mass/charge over all points contained within each region. This yields a reduced feature space representing the original data as a set of consolidated ion traces. Subsequent feature selection is performed by evaluating the individual efficacy of each feature using a univariate score-based likelihood ratio (LR) approach for discriminating between pairs of same or different type samples. The retained features are used to model each ILR class using linear discriminant analysis (LDA).Results are demonstrated for 155 arson samples containing a diversity of substrate compounds and several known ignitable liquids. ILR detection is performed at 84% accuracy with fewer than 1% false positives followed by subsequent classification. Likelihood ratio distributions are presented referring to both detection and classification tasks.

Drug isomer differentiation has become a relevant problem in forensic drug testing laboratories as new psychoactive substances include many closely related and isomeric forms. Both legislative and public safety requirements demand unambiguous identification of the isomers. A novel approach for ring-isomeric differentiation using GC–MS by means of low energy Electron Ionization (EI) in combination with chemometric data analysis was developed. An ionization energy of 15 eV combined with a high efficiency EI source yields more information rich and thus discriminating mass spectra for ring-isomeric, cathinone-type drugs. Through multivariate statistics using Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) mass spectral data can be exploited to confidently distinguish isomeric classes. Including feature selection of the mass spectra further enhanced the discriminative power of these models. In this way, all examined classes of cathinone and fluoroamphetamine isomers could be robustly identified, even through their conventional 70 eV mass spectrum from a quadrupole-MS instrument. A characteristic Likelihood Ratio (LR) based indicator was developed to quantify the selectivity of the models which proved to be useful for comparison, optimization and identification purposes. The potential of the method was demonstrated with six forensic case samples, providing 100% correct isomer identification. In general, this new approach enables robust classification of drug isomers that is currently not possible with conventional GC–MS methods without the use of additional spectroscopic analyses.