Enhancing the contemporary human and water isotope reference database for the Netherlands: New insights from Sr-O-C-N-H isotope data.

The determination of an individual's geographic origin is an essential aspect of forensic investigations. When primary identifiers cannot be used to make a positive identification, isotope analysis can be utilized to provide new leads. Modern reference data are essential for accurate interpretation of human isotopic data in terms of diet and origin. This article presents Sr-O-C-N-H isotope data of modern individuals (hair, dental enamel, and dentine collagen) and drinking water from the Netherlands. The δ15N values of human hair fall within the range of values observed worldwide and cannot be utilized to differentiate from other countries. Distinct disparities in the hair δ13C are evident between European countries and other regions, making it possible to exclude the Netherlands as a region of origin. Comparing Dutch dental isotope data to those of other nations has proven difficult due to the limited availability of reference data. The same limitation applies to tap water δ2H data.

Biomarker profiling in plants to distinguish between exposure to chlorine gas and bleach using LC-HRMS/MS and chemometrics.

Since its first employment in World War I, chlorine gas has often been used as chemical warfare agent. Unfortunately, after suspected release, it is difficult to prove the use of chlorine as a chemical weapon and unambiguous verification is still challenging. Furthermore, similar evidence can be found for exposure to chlorine gas and other, less harmful chlorinating agents. Therefore, the current study aims to use untargeted high resolution mass spectrometric analysis of chlorinated biomarkers together with machine learning techniques to be able to differentiate between exposure of plants to various chlorinating agents. Green spire (Euonymus japonicus), stinging nettle (Urtica dioica), and feathergrass (Stipa tenuifolia) were exposed to 1000 and 7500 ppm chlorine gas and household bleach, pool bleach, and concentrated sodium hypochlorite. After sample preparation and digestion, the samples were analyzed by liquid chromatography high resolution tandem mass spectrometry (LC-HRMS/MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS). More than 150 chlorinated compounds including plant fatty acids, proteins, and DNA adducts were tentatively identified. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed clear discrimination between chlorine gas and bleach exposure and grouping of the samples according to chlorine concentration and type of bleach. The identity of a set of novel biomarkers was confirmed using commercially available or synthetic reference standards. Chlorodopamine, dichlorodopamine, and trichlorodopamine were identified as specific markers for chlorine gas exposure. Fenclonine (Cl-Phe), 3-chlorotyrosine (Cl-Tyr), 3,5-dichlorotyrosine (di-Cl-Tyr), and 5-chlorocytosine (Cl-Cyt) were more abundantly present in plants after chlorine contact. In contrast, the DNA adduct 2-amino-6-chloropurine (Cl-Ade) was identified in both types of samples at a similar level. None of these chlorinated biomarkers were observed in untreated samples. The DNA adducts Cl-Cyt and Cl-Ade could clearly be identified even three months after the actual exposure. This study demonstrates the feasibility of forensic biomarker profiling in plants to distinguish between exposure to chlorine gas and bleach.

Untargeted metabolomics for lifestyle biomarker discovery in human hair.

There is a risk of crimes remaining unsolved when no matching DNA profiles or fingermarks are found. If this is the case, forensic investigations are faced with a significant shortage of evidence and information regarding the unknown perpetrator and/or victim as well as any missing persons. However, a rather commonly found biological trace encountered at crime scenes is human hair. As hair acts as a biochemical reservoir, it may contain valuable information regarding one's characteristics and habits. This study aimed to build an analytical method capable of determining a marker set of relevant metabolites in hair, eventually building up a profile of its donor. To find potential markers, an untargeted metabolomics approach was developed to select and identify statistically significant features. For that purpose, a total of 68 hair samples were collected at several hairdresser shops in varying neighbourhoods. Compound extraction was achieved via methanolic incubation overnight and analysis performed using a high-resolution mass spectrometry (HRMS) Orbitrap Q Exactive Focus. The acquired data was uploaded and statistically evaluated using two free online software/libraries, where a total of eight compounds have given a match on both tools. Their presumptive identity was confirmed using reference standards and consequently added to a dynamic target donor profiling list. These results show the potential of using untargeted metabolomics for the search for lifestyle biomarkers capable of differentiating individuals. Such tools are of paramount importance in a forensic setting with little or no evidence available and no clear tactical leads.

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.

Verification of exposure to chemical warfare agents through analysis of persistent biomarkers in plants.

The continuing threats of military conflicts and terrorism may involve the misuse of chemical weapons. The present study aims to use environmental samples to find evidence of the release of such agents at an incident scene. A novel approach was developed for identifying protein adducts in plants. Basil (Ocimum basilicum), bay laurel leaf (Laurus nobilis) and stinging nettle (Urtica dioica) were exposed to 2.5 to 150 mg m-3 sulfur mustard, 2.5 to 250 mg m-3 sarin, and 0.5 to 25 g m-3 chlorine gas. The vapors of the selected chemicals were generated under controlled conditions in a dedicated set-up. After sample preparation and digestion, the samples were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and liquid chromatography high resolution tandem mass spectrometry (LC-HRMS/MS), respectively. In the case of chlorine exposure, it was found that 3-chloro- and 3,5-dichlorotyrosine adducts were formed. As a result of sarin exposure, the o-isopropyl methylphosphonic acid adduct to tyrosine could be analyzed, and after sulfur mustard exposure the N1- and N3-HETE-histidine adducts were identified. The lowest vapor exposure levels for which these plant adducts could be detected, were 2.5 mg m-3 for sarin, 50 mg m-3 for chlorine and 12.5 mg m-3 for sulfur mustard. Additionally, protein adducts following a liquid exposure of only 2 nmol Novichock A-234, 0.4 nmol sarin and 0.2 nmol sulfur mustard could still be observed. For both vapor and liquid exposure, the amount of adduct formed increased with the level of exposure. In all cases synthetic reference standards were used for unambiguous identification. The window of opportunity for investigation of agent exposure through the analysis of plant material was found to be remarkably long. Even three months after the actual exposure, the biomarkers could still be detected in the living plants, as well as in dried leaves. An important benefit of the current method is that a relatively simple and generic sample work-up procedure can be applied for all agents studied. In conclusion, the presented work clearly demonstrates the possibility of analyzing chemical warfare agent biomarkers in plants, which is useful for forensic reconstructions, including the investigation into alleged use in conflict areas.

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.

Elucidation of in Vitro Chlorinated Tyrosine Adducts in Blood Plasma as Selective Biomarkers of Chlorine Exposure.

Chlorine is a widely available industrial chemical and involved in a substantial number of cases of poisoning. It has also been used as a chemical warfare agent in military conflicts. To enable forensic verification, the persistent biomarkers 3-chlorotyrosine and 3,5-dichlorotyrosine in biomedical samples could be detected. An important shortfall of these biomarkers, however, is the relatively high incidence of elevated levels of chlorinated tyrosine residues in individuals with inflammatory diseases who have not been exposed to chlorine. Therefore, more reliable biomarkers are necessary to distinguish between endogenous formation and exogeneous exposure. The present study aims to develop a novel diagnostic tool for identifying site-specific chlorinated peptides as a more unambiguous indicator of exogeneous chlorine exposure. Human blood plasma was exposed in vitro to various chlorine concentrations, and the plasma proteins were subsequently digested by pronase, trypsin, or pepsin. After sample preparation, the digests were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). In line with other studies, low levels of 3-chlorotyrosine and 3,5-dichlorotyrosine were found in blank plasma samples in this study. Therefore, 50 site-specific biomarkers were identified, which could be used as more unambiguous biomarkers for chlorine exposure. Chlorination of the peptides TY*ETTLEK, Y*KPGQTVK, Y*QQKPGQAPR, HY*EGSTVPEK, and Y*LY*EIAR could already be detected at moderate in vitro chlorine exposure levels. In addition, the latter two peptides were found to have dichlorinated fragments. Especially, Y*LY*EIAR, with a distinct chlorination pattern in the MS spectra, could potentially be used to differentiate exogeneous exposure from endogenous causes as other studies reported that this part of human serum albumin is nitrated rather than chlorinated under physiological conditions. In conclusion, trypsin digestion combined with high-resolution MS analysis of chlorinated peptides could constitute a valuable technique for the forensic verification of exposure to chlorine.

Characterization and comparison of smokeless powders by on-line two-dimensional liquid chromatography.

Smokeless powders (SPs) are one of the most commonly used propellants for ammunition but can also be abused as energetic material in improvised explosive devices (IEDs) such as pipe bombs. After a shooting or explosion, unburnt or partially burnt particulates may be observed which can be used for forensic investigation. SPs comprise mainly nitrocellulose (NC) and additives. Therefore, the characterization of both NC and the additives is of significant forensic importance. Typically, the identification, classification, and chemical profiling of smokeless powders are based exclusively on the analysis of the additives. In this study, information regarding the NC base component was combined with the chemical analysis of the additives using two-dimensional liquid chromatography (2D-LC). The system combines size-exclusion chromatography (SEC) and reversed-phase liquid chromatography (RPLC) in an on-line heart-cut 2D-LC configuration. In the first dimension, the NC is characterized by its molecular-weight distribution (MWD) while being separated from the additives. The additives are then transferred to the second-dimension separation using a novel analyte-transfer system. In the second dimension, the additives are separated to obtain a detailed profile of the low-molecular-mass compounds in the SP. With this approach, the MWD of the NC and the composition of the additives in SP have been obtained within an hour. A discrimination power of 90.53% was obtained when studying exclusively the NC MWD, and 99.47% for the additive profile. This novel combination enables detailed forensic comparison of intact SPs. Additionally, no extensive sample preparation is required, making the developed method less labor intensive.

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.

Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry.

The differentiation of positional isomers is a well established analytical challenge for forensic laboratories. As more novel psychoactive substances (NPSs) are introduced to the illicit drug market, robust yet efficient methods of isomer identification are needed. Although current literature suggests that Direct Analysis in Real Time-Time-of-Flight mass spectrometry (DART-ToF) with in-source collision induced dissociation (is-CID) can be used to differentiate positional isomers, it is currently unclear whether this capability extends to positional isomers whose only structural difference is the precise location of a single substitution on an aromatic ring. The aim of this work was to determine whether chemometric analysis of DART-ToF data could offer forensic laboratories an alternative rapid and robust method of differentiating NPS positional ring isomers. To test the feasibility of this technique, three positional isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone) were analyzed. Using a linear rail for consistent sample introduction, the three isomers of each type were analyzed 96 times over an eight-week timespan. The classification methods investigated included a univariate approach, the Welch t test at each included ion; a multivariate approach, linear discriminant analysis; and a machine learning approach, the Random Forest classifier. For each method, multiple validation techniques were used including restricting the classifier to data that was only generated on one day. Of these classification methods, the Random Forest algorithm was ultimately the most accurate and robust, consistently achieving out-of-bag error rates below 5%. At an inconclusive rate of approximately 5%, a success rate of 100% was obtained for isomer identification when applied to a randomly selected test set. The model was further tested with data acquired as a part of a different batch. The highest classification success rate was 93.9%, and error rates under 5% were consistently achieved.

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.