Trapped ion mobility mass spectrometry of new psychoactive substances: Isomer-specific identification of ring-substituted cathinones.

New psychoactive substances (NPS) are synthetic derivatives of illicit drugs designed to mimic their psychoactive effects. NPS are typically not controlled under drug acts or their legal status depends on their molecular structure. Discriminating isomeric forms of NPS is therefore crucial for forensic laboratories. In this study, a trapped ion mobility spectrometry time-of-flight mass spectrometry (TIMS-TOFMS) approach was developed for the identification of ring-positional isomers of synthetic cathinones, a class of compounds representing two-third of all NPS seized in Europe in 2020. The optimized workflow features narrow ion-trapping regions, mobility calibration by internal reference, and a dedicated data-analysis tool, allowing for accurate relative ion-mobility assessment and high-confidence isomer identification. Ortho-, meta- and para-isomers of methylmethcathinone (MMC) and bicyclic ring isomers of methylone were assigned based on their specific ion mobilities within 5 min, including sample preparation and data analysis. The resolution of two distinct protomers per cathinone isomer added to the confidence in identification. The developed approach was successfully applied to the unambiguous assignment of MMC isomers in confiscated street samples. These findings demonstrate the potential of TIMS-TOFMS for forensic case work requiring fast and highly-confident assignment cathinone-drug isomers in confiscated samples.

Instrument-independent chemometric models for rapid, calibration-free NPS isomer differentiation from mass spectral GC-MS data.

Chemometric analysis of mass spectral data for the purpose of differentiating positional isomers of novel psychoactive substances has seen a substantial increase in popularity in recent years. However, the process of generating a large and robust dataset for chemometric isomer identification is time consuming and impractical for forensic laboratories. To begin to address this problem, three sets of ortho/meta/para positional ring isomers (fluoroamphetamine (FA), fluoromethamphetamine (FMA), and methylmethcathinone (MMC)) were analyzed using multiple GC-MS instruments at three distinct laboratories. A diverse assortment of instrument manufacturers, model types, and parameters was utilized in order to incorporate substantial instrumental variation. The dataset was randomly split into 70% training and 30% validation sets, stratified by instrument. Following an approach based on Design of Experiments, the validation set was used to optimize the preprocessing steps performed prior to Linear Discriminant Analysis. Using the optimized model, a minimum m/z fragment threshold was determined to allow analysts to assess whether an unknown spectrum is of sufficient abundance and quality to be compared to the model. To assess the robustness of the models, a test set was developed utilizing two instruments from a fourth laboratory that was not involved in the generation of the primary dataset in addition to spectra from widely used mass spectral libraries. Of the spectra that reached the threshold, the classification accuracy was 100% for all three isomer types. Only two of the test and validation spectra that did not reach the threshold were misclassified. The results indicate that forensic illicit drug experts world-wide can use these models for robust NPS isomer identification on the basis of preprocessed mass spectral data without the need for acquiring reference drug standards and creating instrument specific GC-MS reference datasets. The continued robustness of the models could be ensured through international collaboration to collect data that captures all potential GC-MS instrumental variation encountered in forensic illicit drug analysis laboratories. This would allow every forensic institute to confidently assign isomeric structures without the need for additional chemical analysis.

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis.

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

Dataset of near-infrared spectral data of illicit-drugs and forensic casework samples analyzed by five portable spectrometers operating in different wavelength ranges.

The increasing amount of globally seized controlled substances in combination with the more diverse drugs-of-abuse market encompassing many new psychoactive substances (NPS) provides challenges for rapid and reliable on-site presumptive drug testing. Long-established colorimetric spot tests tend to fail due to the unavailability of reliable tests for novel drugs and to false-positive reactions on commonly encountered substances. In addition, handling of samples and chemicals is required. Spectroscopic techniques do not have these disadvantages as spectra are compound-specific and non-invasive tests are possible. Near-infrared (NIR) spectroscopy is a promising technique for on-scene forensic drug detection. Numerous portable devices were introduced in the market in recent years. However, most handheld spectrometers operate in different and relatively confined wavelength ranges compared to the full 780 - 2500 nm NIR wavelength range. In addition, their spectral resolution is limited compared to benchtop instruments. This dataset presents the NIR spectra of 430 forensic samples, including regularly encountered illicit-drugs, NPS, commonly used adulterants, bulking-agents and excipients, and seized casework materials (powders and tablets). Data is available from 5 different NIR spectrometers; including a benchmark high-resolution, full range 350-2500 nm laboratory grade instrument and 4 portable spectrometers operating in the ranges of 1300-2600 nm, 1550-1950 nm, 950-1650 nm and 740-1070 nm. Via this dataset, spectra of illicit-drugs become available to institutes that typically do not have access to controlled substances. This data can be used to develop chemometric detection and classification models for illicit-drugs and provide insight in diagnostic spectral features that need to be recorded for reliable detection models. Additionally, the high-resolution, full range VIS-NIR spectra of the benchmark ASD instrument can be used for in-silica predictions of spectra in a certain wavelength range to provide insight in the optimal resolution and wavelength range of a prospective portable device.

Portable near infrared spectroscopy for the isomeric differentiation of new psychoactive substances.

Rapid and efficient identification of the precise isomeric form of new psychoactive substances (NPS) by forensic casework laboratories is a relevant challenge in the forensic field. Differences in legal status occur for ring-isomeric species of the same class, thus leading to different penalties and judicial control. Portable systems such as near-infrared (NIR) spectroscopy recently emerged as suitable techniques for the on-scene identification of common drugs of abuse such as cocaine, MDMA and amphetamine. This way, the overall forensic process becomes more efficient as relevant information on substance identity becomes available directly at the scene of crime. Currently, no NIR-based applications exist for the rapid, on-scene detection of NPS isomers. Herein, we present the differentiation of cathinone and phenethylamine-type NPS analogues based on their NIR spectrum recorded in 2 seconds on a portable 1350 - 2600 nm spectrometer. A prior developed data analysis model was found suitable for the identification of the methylmethcathinone (MMC) isomers 2-MMC, 3-MMC and 4-MMC. In 51 mixtures and 22 seized casework samples, the correct isomeric form was detected in all cases except for a few mixtures with an active ingredient content of 10 wt%. These results show the feasibility of on-site NPS detection as presumptive test performed directly at the scene of crime with a small size NIR-spectrometer. Additionally, in the illicit drug analysis laboratory the combination of NIR and GC-MS analysis might be suitable for robust identification of NPS isomers and analogues.

On-site forensic analysis of colored seized materials: Detection of brown heroin and MDMA-tablets by a portable NIR spectrometer.

The increasing workload for forensic laboratories and the expanding complexity of the drug market necessitates efficient approaches to detect drugs of abuse. Identification directly at the scene of crime enables investigative forces to make rapid decisions. Additionally, on-site identification of the material also leads to considerable efficiency and cost benefits. As such, paperwork, transportation, and time-consuming analysis in a laboratory may be avoided. Near-infrared (NIR) spectroscopy is an analysis technique suitable for rapid drug testing using portable equipment. A possible limitation of spectroscopic analysis concerns the complexity of seized materials. NIR measurements represent composite spectra for mixtures and diagnostic spectral features can be obscured by excipients such as colorants. Herein, a NIR-based (1300-2600 nm) detection of heroin and MDMA in colored casework (i.e., brown powders and ecstasy tablets) using a portable analyzer is presented. The application includes a multistage data analysis model based on the net analyte signal (NAS) approach. This identification model was specifically designed for mixture analysis and requires a limited set of pure reference spectra only. Consequently, model calibration efforts are reduced to a minimum. A total of 549 forensic samples was tested comprising brown heroine samples and a variety of colored tablets with different active ingredients. This investigation led to a >99% true negative and >93% true positive rate for heroin and MDMA. These results show that accurate on-site detection in colored casework is possible using NIR spectroscopy combined with an efficient data analysis model. These findings may eventually help in the transition of routine forensic laboratories from laboratory-based techniques to portable equipment operated on scene.

On-site illicit-drug detection with an integrated near-infrared spectral sensor: A proof of concept.

Illicit-drug production, trafficking and seizures are on an all-time high. This consequently raises pressure on investigative authorities to provide rapid forensic results to assist law enforcement and legal processes in drug-related cases. Ideally, every police officer is equipped with a detector to reliably perform drug testing directly at the incident scene. Such a detector should preferably be small, portable, inexpensive and shock-resistant but should also provide sufficient selectivity to prevent erroneous identifications. This study explores the concept of on-site drugs-of-abuse detection using a 1.8 × 2.2 mm2 multipixel near-infrared (NIR) spectral sensor that potentially can be integrated into a smartphone. This integrated sensor, based on an InGaAs-on-silicon technology, exploits an array of resonant-cavity enhanced photodetectors without any moving parts. A 100% correct classification of 11 common illicit drugs, pharmaceuticals and adulterants was achieved by chemometric modelling of the response of 15 wavelength-specific pixels. The performance on actual forensic casework was investigated on 246 cocaine-suspected powders and 39 MDMA-suspected ecstasy tablets yielding an over 90% correct classification in both cases. These findings show that presumptive drug testing by miniaturized spectral sensors is a promising development ultimately paving the way for a fully integrated drug-sensor in mobile communication devices used by law enforcement.

A calibration friendly approach to identify drugs of abuse mixtures with a portable near-infrared analyzer.

Both the increasing number and diversity of illicit-drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on-site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain-of-evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near-infrared (NIR)-based analyzers may overcome these encumbrances by their compound-specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300-2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time-efficient model training. This calibration-light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non-invasive, operator-safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug-related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

Impurities, adulterants and cutting agents in cocaine as potential candidates for retrospective mining of GC-MS data.

Cocaine is one of the most widely used illicit drugs worldwide. Cocaine powders seized by the Police may contain numerous other substances besides the drug itself. These can be impurities originating from the coca plant or the production process, or be purposely added to the drug formulation as adulterants and cutting agents. In forensic laboratories, identification of cocaine is routinely done through GC-MS analysis, but other components are often ignored even if the method allows for their detection. Yet, they can provide valuable insight into the history of a seizure and its potential connection to other samples. To explore this idea, an extensive review of common impurities and adulterants encountered in cocaine is presented. Based on their incidence, concentration in the end product and compatibility with GC-MS methods, their overall usefulness as candidates for the statistical investigation of existing forensic data is evaluated. The impurities cis- and trans-cinnamoylcocaine, tropacocaine, norcocaine and N-benzoylnormethylecgonine as well as the adulterants lidocaine, procaine, tetracaine, benzocaine, caffeine, acetylsalicylic acid, phenacetin, ibuprofen, levamisole, hydroxyzine and diltiazem are promising candidates to provide additional forensic intelligence. Future research on optimized routine GC-MS methods, signal reproducibility, comparison, statistics and databases is suggested to facilitate this concept. Ultimately, such an approach may significantly advance the amount of information that is extracted from routine casework data, elucidate developments in the cocaine markets in the past and facilitate Police work in the future. Preliminary assessment of existing data from the forensic laboratory of the Amsterdam Police has been included to show that the detection of the identified target impurities is feasible, and that small adjustments to the analysis method could significantly increase the detectability of these analytes in prospective drug screenings. Forensic intelligence based on retrospective data mining of cocaine containing casework samples may thus be realized with minimal additional laboratory efforts by using already available instrumentation, samples and data.

Spotting isomer mixtures in forensic illicit drug casework with GC-VUV using automated coelution detection and spectral deconvolution.

Analysis of isomeric mixtures is a significant analytical challenge. In the forensic field, for example, over 1000 new psychoactive substances (NPSs), comprising of many closely related and often isomeric varieties, entered the drugs-of-abuse market within the last decade. Unambiguous identification of the isomeric form requires advanced spectroscopic techniques, such as GC-Vacuum Ultraviolet Spectroscopy (GC-VUV). The continuous development of NPSs makes the appearance of a novel compound in case samples a realistic scenario. While several analytical solutions have been presented recently to confidently distinguish NPS isomers, the presence of multiple isomers in a single drug sample is typically not considered. Due to their structural similarities it is possible that a novel NPS coelutes with a known isomer and thus remains undetected. This study investigates the capabilities of VUV spectral deconvolution for peak detection and identification in incompletely resolved drug mixtures. To mimic worst case scenarios, severe coelution was deliberately induced at elevated GC temperatures. The deconvolution software was nevertheless able to correctly detect both substances, even in case of near-identical VUV spectra at almost full coelution. As a next step, spectra were subsequently removed from the reference library to simulate the scenario in which a novel substance was encountered for the first time in forensic case work. However, also in this situation the deconvolution software still detected the coelution. This work shows that a VUV library match score below 0.998 may serve as a warning that a novel substance may be present in a street sample.

Isomer-Specific Two-Color Double-Resonance IR2MS3 Ion Spectroscopy Using a Single Laser: Application in the Identification of Novel Psychoactive Substances.

The capability of an ion trap mass spectrometer to store ions for an arbitrary amount of time allows the use of a single infrared (IR) laser to perform two-color double resonance IR-IR spectroscopic experiments on mass-to-charge (m/z) selected ions. In this single-laser IR2MS3 scheme, one IR laser frequency is used to remove a selected set of isomers from the total trapped ion population and the second IR laser frequency, from the same laser, is used to record the IR spectrum of the remaining precursor ions. This yields isomer-specific vibrational spectra of the m/z-selected ions, which can reveal the structure and identity of the initially co-isolated isomeric species. The use of a single laser greatly reduces the experimental complexity of two-color IR2MS3 and enhances its application in fields employing analytical MS. In this work, we demonstrate the methodology by acquiring single-laser IR2MS3 spectra in a forensic context, identifying two previously unidentified isomeric novel psychoactive substances (NPS) from a sample that was confiscated by the Amsterdam Police.

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

Handheld Raman spectroscopy is an emerging technique for rapid on-site detection of drugs of abuse. Most devices are developed for on-scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0-100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography-mass spectrometry (GC-MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on-screen results with combined partial least square regression (PLS-R) and discriminant analysis (PLS-DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built-in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm-1 (cocaine HCl). Combined PLS-R and PLS-DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.

On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.

Mass-Spectrometry-Based Identification of Synthetic Drug Isomers Using Infrared Ion Spectroscopy.

Infrared ion spectroscopy (IRIS), a mass-spectrometry-based technique exploiting resonant infrared multiple photon dissociation (IRMPD), has been applied for the identification of novel psychoactive substances (NPS). Identification of the precise isomeric forms of NPS is of significant forensic relevance since legal controls are dependent on even minor molecular differences such as a single ring-substituent position. Using three isomers of fluoroamphetamine and two ring-isomers of both MDA and MDMA, we demonstrate the ability of IRIS to distinguish closely related NPS. Computationally predicted infrared (IR) spectra are shown to correspond with experimental spectra and could explain the molecular origins of their distinctive IR absorption bands. IRIS was then used to investigate a confiscated street sample containing two unknown substances. One substance could easily be identified by comparison to the IR spectra of reference standards. For the other substance, however, this approach proved inconclusive due to incomplete mass spectral databases as well as a lack of available reference compounds, two common analytical limitations resulting from the rapid development of NPS. Most excitingly, the second unknown substance could nevertheless be identified by using computationally predicted IR spectra of several potential candidate structures instead of their experimental reference spectra.

Distinguishing drug isomers in the forensic laboratory: GC-VUV in addition to GC-MS for orthogonal selectivity and the use of library match scores as a new source of information.

Currently, forensic drug experts are facing chemical identification challenges with the increasing number of new isomeric forms of psychoactive substances occurring in case samples. Very similar mass spectra for these substances could easily result in misidentification using the regular GC-MS screening methods in combination with colorimetric testing in forensic laboratories. Building on recent work from other groups, this study demonstrates that GC-VUV is a powerful technique for drug isomer differentiation, showing reproducible and discriminating spectra for aromatic ring-isomers. MS and VUV show complementary selectivity as VUV spectra are ring-position specific whereas MS spectra are characteristic for the amine moieties of the molecule. VUV spectra are very reproducible showing less than 0.1‰ deviation in library match scores and therefore small spectral differences suffice to confidently distinguish isomers. In comparison, MS match scores gave over 10‰ deviation and showed significant overlap in match score ranges for several isomers. This poses a risk for false positive identifications when assigning compounds based on retention time and GC-MS mass spectrum. A strategy was developed, based on Kernel Density Estimations of match scores, to construct Receiver Operating Characteristic (ROC) curves and estimate likelihood ratios (LR values) with respect to the chemical differentiation of drug related isomers. This approach, and the added value of GC-VUV is demonstrated with the chemical analysis of several samples from drug case work from the Amsterdam area involving both compounds listed in Dutch drug legislation (3,4-MDMA; 3,4-MDA; 4-MMC; 4-MEC and 4-FA) as well as their unlisted and thus uncontrolled isomers (2,3-MDMA; 2,3-MDA; 2- and 3-MMC; 2- and 3-MEC and 2- and 3-FA).