Progressive expansion of albumin adducts for organophosphorus nerve agent traceability based on single and group adduct collection.

Protein adducts are important biological targets for traceability of organophosphorus nerve agents (OPNAs). Currently, the recognized biomarkers that can be used in actual samples in the field of chemical forensics only include Y411 in albumin and the active nonapeptide in butyrylcholinesterase (BChE). To explore stable and reliable protein adducts and increase the accuracy of OPNAs traceability further, we gradually expanded OPNAs-albumin adducts based on single and group adduct collection. Several stable peptides were found via LC-MS/MS analysis in human serum albumin (HSA) exposed to OPNAs in a large exposure range. These adducts were present in HSA samples exposed to OPNAs of each concentration, which provided data support for the reliability and stability of using adducts to trace OPNAs. Meanwhile, the formation mechanism of OPNAs-cysteine adduct was clarified via computer simulations. Then, these active sites found and modified peptides were used as raw materials for progressive expansion of albumin adducts. We constructed an OPNAs-HSA adducts group, in which a specific agent is the exposure source, and three or more active peptides constitute data sets for OPNAs traceability. Compared with single or scattered protein adducts, the OPNAs-HSA adduct group improves OPNAs identification by mutual verification using active peptides or by narrowing the identity range of the exposure source. We also determined the minimum detectable concentration of OPNAs for the adduct group. Two or more peptides can be detected when there is an exposure of 50 times the molar excess of OPNAs in relation to HSA. This improved the accuracy of OPNAs exposure and identity confirmation. A collection of OPNAs-albumin adducts was also examined. The collection was established by collecting, classifying, and integrating the existing albumin adducts according to the species to which each albumin belongs, the types of agents, and protease. This method can serve as a reference for discovering new albumin adducts, characteristic phosphonylated peptides, and potential biomarkers. In addition, to avoid a false negative for OPNAs traceability using albumin adducts, we explored OPNAs-cholinesterase adducts because cholinesterase is more reactive with OPNAs than albumin. Seven active peptides in red blood cell acetylcholinesterase (RBC AChE) and serum BChE can assist in OPNAs exposure and identity confirmation.

Nontarget Chemical Composition of Surface Waters May Reflect Ecosystem Processes More than Discrete Source Contributions.

We investigated environmental, landscape, and microbial factors that could structure the spatiotemporal variability in the nontarget chemical composition of four riverine systems in the Oregon Coast Range, USA. We hypothesized that the nontarget chemical composition in river water would be structured by broad-scale landscape gradients in each watershed. Instead, only a weak relationship existed between the nontarget chemical composition and land cover gradients. Overall, the effects of microbial communities and environmental variables on chemical composition were nearly twice as large as those of the landscape, and much of the influence of environmental variables on the chemical composition was mediated through the microbial community (i.e., environment affects microbes, which affect chemicals). Therefore, we found little evidence to support our hypothesis that chemical spatiotemporal variability was related to broad-scale landscape gradients. Instead, we found qualitative and quantitative evidence to suggest that chemical spatiotemporal variability of these rivers is controlled by changes in microbial and seasonal hydrologic processes. While the contributions of discrete chemical sources are undeniable, water chemistry is undoubtedly impacted by broad-scale continuous sources. Our results suggest that diagnostic chemical signatures can be developed to monitor ecosystem processes, which are otherwise challenging or impossible to study with existing off-the-shelf sensors.

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data.

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

Chemical forensic profiling and attribution signature determination of sarin nerve agent using GC-MS, LC-MS and NMR.

Sarin is a highly toxic nerve agent classified by the Chemical Weapon Convention as a Schedule 1 chemical with no use other than to kill or injure. Moreover, in recent times, chemical warfare agents have been deployed against both military and civilian populations. Chemical warfare agents always contain minor impurities that can provide important chemical attribution signatures (CAS) that can aid in forensic investigations. In order to understand the trace molecular composition of sarin, various analytical approaches including GC-MS, LC-MS and NMR were used to determine the chemical markers of a set of sarin samples. Precursor materials were studied and the full characterisation of a synthetic process was undertaken in order to provide new insights into potential chemical attribution signatures for this agent. Several compounds that were identified in the precursor were also found in the sarin samples linking it to its method of preparation. The identification of these CAS contributes critical information about a synthetic route to sarin, and has potential for translation to related nerve agents.

Forensic signatures of a chemical weapon precursor DMPADC for determination of a synthetic route.

Chemical forensics has been widely recognized as an important tool to investigate alleged use of chemical weapons and/or to identify the illicit production of chemical warfare agents. This paper describes the use of gas chromatography and mass spectrometry (GC-MS) to determine chemical attribution signatures (CAS) N,N-dimethylphosphoramidic dichloride (DMPADC), a key precursor of tabun, for tracking the production of tabun. Synthetic samples were identified and classified by using GC-MS and chemometrics. Analysis samples (n = 27) were collected from three synthetic DMPADC routes; 20 potential CAS were identified, and the structures of five CAS were assigned. Principal component analysis (PCA) was performed to summarize the distribution trend of the samples and to check for the presence of outliers. A Partial least squares discriminant analysis (PLSDA) model was established to discriminate and classify the synthetic samples. The proposed model in this paper has high predictive ability, and the test set samples can be correctly categorized.

Investigating the chemical impurity profiles of fentanyl preparations and precursors to identify chemical attribution signatures for synthetic method attribution.

From an analytical chemistry standpoint, determining the chemical attribution signatures (CAS) of synthetic reaction mixtures is an impurity profiling exercise. Identifying and understanding the impurity profile and CAS of these chemical agents would allow them to be exploited for chemical forensic information, such as how a particular chemical agent was synthesised. Being able to determine the synthetic route used to make a chemical agent allows for the possibility of batches of the agent, and individual incidents using that agent, to be forensically linked. This information is of particular benefit to agencies investigating the nefarious and illicit use of chemical agents. One such chemical agent of interest to law enforcement and national security agencies is fentanyl. In this study two acylation methods for the final step of fentanyl production, herein termed the Janssen and Siegfried methods, were investigated by liquid chromatography- high resolution mass spectrometry (LC-HRMS) and multivariate statistical analysis (MVA). From these data, fifty-five chemical impurities were identified. Of these, ten were specific CAS for the Janssen method, and five for the Siegfried method. Additionally, analytical data from four different literature methods for production of the fentanyl precursor 4-anilino-N-phenethylpiperidine (ANPP), were compared to the results obtained from the method of production (Valdez) used in this study. Comparison of the LC-HRMS data for these five methods allowed for four Valdez specific impurities to be identified. These may be useful CAS for the Valdez method of ANPP production.

The identification of chemical attribution signatures of stored VX nerve agents using NMR, GC-MS, and LC-HRMS.

The organophosphorous nerve agent VX is classified by the Chemical Warfare Convention (CWC) as a Schedule 1 chemical; namely a substance that is highly toxic with no use that is of benefit to society. Even with this classification, the nefarious use of the Schedule 1 chemical VX has been observed, as demonstrated in 2017 in Malaysia. Therefore, undertaking chemical analysis on samples of VX to identify chemical attribution signatures (CAS) for chemical forensics is required. To further understand the chemical profile of VX, and to aid in the identification of potential CAS, three in house synthesised stocks of VX were investigated. The three VX stocks analysed were synthesised in 2014, 2017 and 2018 using the same method, allowing for a comparison of data between each of the stocks at different stages of storage. As opposed to a majority of literature reports, these agent stocks were not stabilised, nor were they subjected to forced degradation. Using NMR, high resolution (HR) LC-HRMS, GC-(EI)MS and GC-(CI)MS to gain a full insight into the CAS profile, a total of 44 compounds were identified. Of these compounds, 30 were readily identified through accurate mass measurement and NIST library matches. A further seven were identified through extensive LC-HRMS/MS studies, with seven remaining unresolved. Several compounds, identified in minor amounts, were able to be traced back to impurities in the precursor compounds used in the synthesis of VX, and hence may be useful as CAS for source attribution.

Attribution of fentanyl analogue synthesis routes by multivariate data analysis of orthogonal mass spectral data.

Chemical attribution signatures (CAS) can be used to obtain useful forensic information and evidence from illicit drug seizures. A CAS is typically generated using hyphenated chemical analysis techniques and consists of a fingerprint of the by-products and additives present in a sample. Among other things, it can provide information on the sample's origin, its method of production, and the sources of its precursors. This work investigates the possibility of using multivariate CAS analysis to identify the synthetic methods used to prepare seized fentanyl analogues, independently of the analogues' acyl derivatization. Three chemists working in two labs synthesized three different fentanyl analogues, preparing each one in duplicate by six different routes. The final collection of analogues (96 samples) and two intermediates (16 + 32 samples) were analysed by GC-MS and UHPLC-HRMS, and the resulting analytical data were used for multivariate modelling. Independently of analogue structure, the tested fentanyls could be classified based on the method used in the first step of their synthesis. The multivariate model's ability to classify unknown samples was then evaluated by applying it to six new fentanyl analogues. Additionally, seized fentanyl samples was analysed and classified by the model.

Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures.

Riot control agents such as pepper sprays can be misused for antagonistic and criminal purposes. Several web-pages and YouTube videos are available describing how to make homemade pepper spray. In this study, we investigated whether it was possible to identify the origin of homemade pepper sprays based on chemical attribution signatures from thirteen different types of chili acquired from six different vendors analyzed by GC-MS. The results showed that it was possible to differentiate chili based on species, chili type and vendor using OPLS-DA. Application of an external test set of chilies acquired and extracted one year later than development of the models resulted in correct classification in all models. The models displayed high predictability, suggesting their use for prediction of the identity and origin of seized homemade pepper sprays.

Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures.

A multivariate model was developed to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.

Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production.

Chemical attribution of the origin of an illegal drug is a key component of forensic efforts aimed at combating illicit and clandestine manufacture of drugs and pharmaceuticals. The results of these studies yield detailed information on synthesis byproducts, reagents, and precursors that can be used to identify the method of manufacture. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3-methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Eighteen crude samples from six synthesis methods were generated, the analysis of which was used to identify signatures (i.e. chemical compounds) that were important in the discrimination of synthetic route. These methods were carefully selected to minimize the use of scheduled precursors, complicated laboratory equipment, number of steps, and extreme reaction conditions. Using gas and liquid chromatographies combined with time-of-flight mass spectrometry (GC-QTOF and LC-QTOF) over 160 distinct species were monitored. Analysis of this combined data set was performed using modern machine learning techniques capable of reducing the size of the data set, prioritizing key chemical attribution signatures, and identifying the method of production for blindly synthesized 3-methylfentanyl materials.

Stable-carbon isotope ratios for sourcing the nerve-agent precursor methylphosphonic dichloride and its products.

The ability to connect a chemical threat agent to a specific batch of a synthetic precursor can provide a fingerprint to contribute to effective forensic investigations. Stable isotope analysis can leverage intrinsic, natural isotopic variability within the molecules of a threat agent to unlock embedded chemical fingerprints in the material. Methylphosphonic dichloride (DC) is a chemical precursor to the nerve agent sarin. DC is converted to methylphosphonic difluoride (DF) as part of the sarin synthesis process. We used a suite of commercially available DC stocks to both evaluate the potential for δ13C analysis to be used as a fingerprinting tool in sarin-related investigations and to develop sample preparation techniques (using chemical hydrolysis) that can simplify isotopic analysis of DC and its synthetic products. We demonstrate that natural isotopic variability in DC results in at least three distinct, isotope-resolved clusters within the thirteen stocks we analyzed. Isotopic variability in the carbon feedstock (i.e., methanol) used for DC synthesis is likely inherited by the DC samples we measured. We demonstrate that the hydrolysis of DC and DF to methylphosphonic acid (MPA) can be used as a preparative step for isotopic analysis because the reaction does not impart a significant isotopic fractionation. MPA is more chemically stable, less toxic, and easier to handle than DC or DF. Further, the hydrolysis method we demonstrated can be applied to a suite of other precursors or to sarin itself, thereby providing a potentially valuable forensic tool.

On the use of spectra from portable Raman and ATR-IR instruments in synthesis route attribution of a chemical warfare agent by multivariate modeling.

Collecting data under field conditions for forensic investigations of chemical warfare agents calls for the use of portable instruments. In this study, a set of aged, crude preparations of sulfur mustard were characterized spectroscopically without any sample preparation using handheld Raman and portable IR instruments. The spectral data was used to construct Random Forest multivariate models for the attribution of test set samples to the synthetic method used for their production. Colored and fluorescent samples were included in the study, which made Raman spectroscopy challenging although fluorescence was diminished by using an excitation wavelength of 1064 nm. The predictive power of models constructed with IR or Raman data alone, as well as with combined data was investigated. Both techniques gave useful data for attribution. Model performance was enhanced when Raman and IR spectra were combined, allowing correct classification of 19/23 (83%) of test set spectra. The results demonstrate that data obtained with spectroscopy instruments amenable for field deployment can be useful in forensic studies of chemical warfare agents.

Macondo oil in northern Gulf of Mexico waters - Part 1: Assessments and forensic methods for Deepwater Horizon offshore water samples.

Forensic chemistry assessments documented the presence of Macondo (MC252) oil from the Deepwater Horizon (DWH) spill in offshore water samples collected under Natural Resource Damage Assessment (NRDA) protocols. In ocean depths, oiled water was sampled, observed, photographed, and tracked in dissolved oxygen (DO) and fluorometry profiles. Chemical analyses, sensor records, and observations confirmed the shifting, rising oil plume above the wellhead while smaller, less buoyant droplets were entrapped in a layer at ~1000-1400 m and advected up to 412 km southwest. Near-surface oil samples showed substantial dissolution weathering from oil droplets rising through the water column, as well as enhanced evaporative losses of lighter n-alkanes and aromatic hydrocarbons. Dispersant effects from surface applications and injected at the wellhead were seen in oil profiles as enhanced weathering patterns (increased dissolution), thus implying dispersants were a functionally effective mediation treatment. Forensic assessment methods are detailed in the Supplemental information (SI).

Advice on chemical weapons sample stability and storage provided by the Scientific Advisory Board of the Organisation for the Prohibition of Chemical Weapons to increase investigative capabilities worldwide.

The Scientific Advisory Board (SAB) of the Organisation for the Prohibition of Chemical Weapons (OPCW) has provided advice on the long-term storage and stability of samples collected in the context of chemical weapons investigations. The information they compiled and reviewed is beneficial to all laboratories that carry out analysis of samples related to chemical warfare agents and is described herein. The preparation of this report was undertaken on request from the OPCW Director-General. The main degradation products for chemicals on the Schedules in the Annex on Chemicals of the Chemical Weapons Convention are tabulated. The expertise of the 25 scientists comprising the SAB, a review of the scientific literature on environmental and biomedical sample analysis, and answers to a questionnaire from chemists of nine OPCW Designated Laboratories, were drawn upon to provide the advice. Ten recommendations to ensure the long-term storage and stability of samples collected in relation to the potential use of chemical weapons were provided and are repeated here for the consideration of all laboratories worldwide.

Evaluating and modeling the effects of surface sampling factors on the recovery of organic chemical attribution signatures using the accelerated diffusion sampler and solvent extraction.

In this study, an experimental design matrix was created and executed to test the effects of various real-world factors on the ability of (1) the accelerated diffusion sampler with solid phase micro-extraction (ADS-SPME) and (2) solvent extraction to capture organic chemical attribution signatures (CAS) from dimethyl methylphosphonate (DMMP) spiked onto painted wall board (PWB) surfaces. The DMMP CAS organic impurities sampled by ADS-SPME and solvent extraction were analyzed by gas chromatography/mass spectrometry (GC/MS). The number of detected DMMP CAS impurities and their respective GC/MS peak areas were determined as a function of DMMP stock, DMMP spiked volume, exposure time, SPME sampling time, and ADS headspace pressure. Based on the statistical analysis of experimental results, several general conclusions are made: (1) the amount of CAS impurity detected using ADS-SPME and GC/MS was most influenced by spiked volume, stock, and ADS headspace pressure, (2) reduced ADS headspace pressure increased the amount of detected CAS impurity, as measured by GC/MS peak area, by up to a factor of 1.7-1.9 compared to ADS at ambient headspace pressure, (3) the ADS had no measurable effect on the number of detected DMMP impurities, that is, ADS (with and without reduced pressure) had no practical effect on the DMMP impurity profile collected from spiked PWB, and (4) solvent extraction out performed ADS-SPME in terms of consistently capturing all or most of the targeted DMMP impurities from spiked PWB.

Elemental source attribution signatures for calcium ammonium nitrate (CAN) fertilizers used in homemade explosives.

Calcium ammonium nitrate (CAN) is a widely available fertilizer composed of ammonium nitrate (AN) mixed with some form of calcium carbonate such as limestone or dolomite. CAN is also frequently used to make homemade explosives. The potential of using elemental profiling and chemometrics to match both pristine and reprocessed CAN fertilizers to their factories of origin for use in future forensic investigations was examined. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentrations of 64 elements in 125 samples from 11 CAN stocks from 6 different CAN factories. Using Fisher ratio and degree-of-class-separation, the elements Na, V, Mn, Cu, Ga, Sr, Ba and U were selected for classification of the CAN samples into 5 factory groups; one group was two factories from the same fertilizer company. Partial least squares discriminant analysis (PLSDA) was used to develop a classification model which was tested on a separate set of samples. The test set included samples that were analyzed at a different time period and samples from factory stocks that were not part of the training set. For pristine CAN samples, i.e., unadulterated prills, 73% of the test samples were matched to their correct factory group with the remaining 27% undetermined using strict classification. The same PLSDA model was used to correctly match all CAN samples that were reprocessed by mixing with powdered sugar. For CAN samples that were reprocessed by mixing with aluminum or by extraction of AN with tap or bottled water, correct classification was observed for one factory group, but source matching was confounded with adulterant interference for two other factories. The elemental signatures of the water-insoluble (calcium carbonate) portions of CAN provided a greater degree of discrimination between factories than the water-soluble portions of CAN. In summary, this work illustrates the strong potential for matching unadulterated CAN fertilizer samples to their manufacturing facility using elemental profiling and chemometrics. The effectiveness of this method for source determination of reprocessed CAN is dependent on how much an adulterant alters the recovered elemental profile of CAN.

Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery.

Preprocessing software, which converts large instrumental data sets into a manageable format for data analysis, is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines. Here, four freely available and published preprocessing tools known as MetAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor and method blanks. Parameters were optimized for each of the four tools for the untargeted detection, matching, and cataloging of chromatographic peaks from impurities present in the stock samples. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock and absent in the method blanks. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every known component present in a stock. For the nominal mass GC/MS data, MetAlign had the most component detections followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 41%, respectively. For the accurate mass LC/MS data, the order was MetAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining MetAlign with MZmine for the GC/MS data and 93% by combining MetAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities from each tool had averaged absolute biases (relative to peak intensities obtained using instrument software) of 41, 4.4, 1.3 and 1.3% for SpectConnect, MetAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the averaged absolute biases were 22, 4.5, and 3.1% for MetAlign, MZmine, and XCMS, respectively. In summary, MetAlign performed the best in terms of the number of component detections; however, more than one preprocessing tool should be considered to avoid missing impurities or other trace components as potential chemical signatures.

Simultaneous observation of 2H and 13C enrichment of methyl phosphonic acid via Orbitrap-IRMS with applications to nerve agent forensics.

Quantification of the stable isotopes within a compound aids forensic investigations as it provides a fingerprint which can determine that compound's source substrates, synthetic route, and possible mechanisms of degradation. Previous stable isotope studies have explored 13C and 2H measurements of the sarin precursors methylphosphonic dichloride (DC) and methylphosphonic difluoride (DF) as forensic signatures. However, these measurements required different sample preparations and measurement techniques. Orbitrap isotope ratio mass spectrometry (Orbitrap-IRMS) is a developing technique which can characterize multiple stable isotopes simultaneously. Here, we apply Orbitrap-IRMS to simultaneously observe the 13C and 2H content of methylphosphonic acid (MPA), the hydrolysis product of DC and DF, which can be used as a proxy for the isotopic content of DC and DF. Our method requires 20 min analyses and consumes ≈60 nmol of sample, with precisions of ≈0.9 ‰ (13C) and ≈3.6 ‰ (2H). We apply our method to both commercially acquired MPA and MPA obtained from the hydrolysis of commercially acquired DC. We validate our methods via comparison to elemental-analyzer isotope ratio mass spectrometry (EA-IRMS). The combined 13C and 2H measurement creates a more robust forensic tool than either isotope individually. Our results demonstrate the viability of Orbitrap-IRMS for chemical forensic measurements.