•Preprocessing tools used to detect impurity components in a nerve-agent precursor.•MetAlign performed the best of four tools in terms of the number of detected components.•∼15% more components were ...detected by combining the top two tool performers.•Extensive manual examinations needed to deal with high prevalence of false peaks.
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.
This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography−mass spectrometry (LC−MS) data for the discovery of trace forensic signatures for sample matching of ...ten stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). XCMS, a software tool primarily used in bioinformatics, was used to comprehensively search and find candidate LC−MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source. Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by K-nearest neighbors. Using this approach, a test set of seven dichlor samples were all correctly matched to their source stock. The sample preparation and LC−MS method permitted the detection of dichlor impurities quantitatively estimated to be in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to assist investigations following chemical attacks.
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.
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•Isotopic variability among thirteen DC stocks enables forensic fingerprinting.•Hydrolysis of DC and DF improves isotopic measurement and sample stability.•Carbon isotopic measurement provides a feasible tool for tracking sarin precursors.
Chemical attribution signatures (CAS) are being investigated for the sourcing of chemical warfare (CW) agents and their starting materials that may be implicated in chemical attacks or CW ...proliferation. The work reported here demonstrates for the first time trace impurities from the synthesis of tris(2-chloroethyl)amine (HN3) that point to the reagent and the specific reagent stocks used in the synthesis of this CW agent. Thirty batches of HN3 were synthesized using different combinations of commercial stocks of triethanolamine (TEA), thionyl chloride, chloroform, and acetone. The HN3 batches and reagent stocks were then analyzed for impurities by gas chromatography/mass spectrometry. All the reagent stocks had impurity profiles that differentiated them from one another. This was demonstrated by building classification models with partial least-squares discriminant analysis (PLSDA) and obtaining average stock classification errors of 2.4, 2.8, 2.8, and 11% by cross-validation for chloroform (7 stocks), thionyl chloride (3 stocks), acetone (7 stocks), and TEA (3 stocks), respectively, and 0% for a validation set of chloroform samples. In addition, some reagent impurities indicative of reagent type were found in the HN3 batches that were originally present in the reagent stocks and presumably not altered during synthesis. More intriguing, impurities in HN3 batches that were apparently produced by side reactions of impurities unique to specific TEA and chloroform stocks, and thus indicative of their use, were observed.
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.
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•ADS-SPME analysis of DMMP impurities on painted wall board were studied under both vacuum and non-vacuum environments.•An in-depth 48-run experiment was created and executed.•Solvent extraction is a more robust method than the ADS-SPME approach.
Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal ...investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ13C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.
•A ‘reversed column’ GC×GC format was used to separate ten RP-1 fuels.•Partial least squares (PLS) analysis was used to analyze the GC×GC–TOFMS data.•Replicate data sets were separately analyzed ...using leave-one-out cross validation.•Connections between fuel composition and physical properties were investigated.•Compounds that appeared most influential for physical properties were identified.
There is an increased need to more fully assess and control the composition of kerosene-based rocket propulsion fuels such as RP-1. In particular, it is critical to make better quantitative connections among the following three attributes: fuel performance (thermal stability, sooting propensity, engine specific impulse, etc.), fuel properties (such as flash point, density, kinematic viscosity, net heat of combustion, and hydrogen content), and the chemical composition of a given fuel, i.e., amounts of specific chemical compounds and compound classes present in a fuel as a result of feedstock blending and/or processing. Recent efforts in predicting fuel chemical and physical behavior through modeling put greater emphasis on attaining detailed and accurate fuel properties and fuel composition information. Often, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is employed to provide chemical composition information. Building on approaches that used GC–MS, but to glean substantially more chemical information from these complex fuels, we recently studied the use of comprehensive two dimensional (2D) gas chromatography combined with time-of-flight mass spectrometry (GC×GC–TOFMS) using a “reversed column” format: RTX-wax column for the first dimension, and a RTX-1 column for the second dimension. In this report, by applying chemometric data analysis, specifically partial least-squares (PLS) regression analysis, we are able to readily model (and correlate) the chemical compositional information provided by use of GC×GC–TOFMS to RP-1 fuel property information such as density, kinematic viscosity, net heat of combustion, and so on. Furthermore, we readily identified compounds that contribute significantly to measured differences in fuel properties based on results from the PLS models. We anticipate this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an improved approach to fuel formulation and specification for advanced engine cycles.
The goal of this study was to determine the physicochemical properties of a variety of geologic materials using inverse gas chromatography (IGC) by varying probe gas selection, temperature, carrier ...gas flow rate, and humidity. This is accomplished by measuring the level of interaction between the materials of interest and known probe gases. Identifying a material’s physicochemical characteristics can help provide a better understanding of the transport of gaseous compounds in different geologic materials or between different geological layers under various conditions. Our research focused on measuring the enthalpy (heat) of adsorption, Henry’s constant, and diffusion coefficients of a suite of geologic materials, including two soil types (sandy clay-loam and loam), quartz sand, salt, and bentonite clay, with various particle sizes. The reproducibility of IGC measurements for geologic materials, which are inherently heterogeneous, was also assessed in comparison to the reproducibility for more homogeneous synthetic materials. This involved determining the variability of physicochemical measurements obtained from different IGC approaches, instruments, and researchers. For the investigated IGC-determined parameters, the need for standardization became apparent, including the need for application-relevant reference materials. The inherent physical and chemical heterogeneities of soil and many geologic materials can make the prediction of sorption properties difficult. Characterizing the properties of individual organic and inorganic components can help elucidate the primary factors influencing sorption interactions in more complex mixtures. This research examined the capabilities and potential challenges of characterizing the gas sorption properties of geologic materials using IGC.
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.
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•Elemental profiling and chemometrics link CAN fertilizers to manufacturing source.•Sourcing of adulterated CAN fertilizers is demonstrated.•Water-insoluble CAN constituents provide greater source discrimination.
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