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%.
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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•Relative humidity affects the amount of dimethyl methylphosphonate (DMMP) sorbed on office materials.•Cardboard retains captured DMMP longer than paint layer and polyurethane ...foam.•DMMP, a nerve agent simulant, was retained after 6 weeks on office materials.
Dimethyl methylphosphonate (DMMP) was used as a simulant to investigate the sorption and desorption of G-series nerve agents such as sarin (GB) released into an office space. DMMP was disseminated systematically as an aerosol and vapor on three common office materials, cardboard, polyurethane foam, and cured acrylic-based paint. The amount of DMMP initially captured on the office materials and the amount retained over a period of 10 h was tracked gravimetrically and by solvent extraction and gas chromatography/mass spectrometry (GC/MS). Physicochemical properties of the office media including the surface area per mass, polarity, and Henry’s constant were determined to help explain the capture and release of DMMP that was observed. Polyurethane foam was found to have the largest sorption capacity for DMMP in a low-humidity setting, however, cardboard was found to have the largest sorption capacity at higher humidity. A fraction of the collected DMMP desorbed with first-order kinetics from all of the office materials in an open-air atmosphere over 8 h. However, importantly for forensic purposes relevant to an indoor attack, a significant quantity (most notably on cardboard) of the nerve agent simulant was retained after 6 weeks and could be analyzed efficiently using solvent extraction followed by GC/MS.
Introduction
Analyses of cerebrospinal fluid (CSF) metabolites in large, healthy samples have been limited and potential demographic moderators of brain metabolism are largely unknown.
Objective
Our ...objective in this study was to examine sex and race differences in 33 CSF metabolites within a sample of 129 healthy individuals (37 African American women, 29 white women, 38 African American men, and 25 white men).
Methods
CSF metabolites were measured with a targeted electrochemistry-based metabolomics platform. Sex and race differences were quantified with both univariate and multivariate analyses. Type I error was controlled for by using a Bonferroni adjustment (0.05/33 = .0015).
Results
Multivariate Canonical Variate Analysis (CVA) of the 33 metabolites showed correct classification of sex at an average rate of 80.6% and correct classification of race at an average rate of 88.4%. Univariate analyses revealed that men had significantly higher concentrations of cysteine (
p
< 0.0001), uric acid (
p
< 0.0001), and N-acetylserotonin (p = 0.049), while women had significantly higher concentrations of 5-hydroxyindoleacetic acid (5-HIAA) (
p
= 0.001). African American participants had significantly higher concentrations of 3-hydroxykynurenine (
p
= 0.018), while white participants had significantly higher concentrations of kynurenine (
p
< 0.0001), indoleacetic acid (
p
< 0.0001), xanthine (
p
= 0.001), alpha-tocopherol (
p
= 0.007), cysteine (
p
= 0.029), melatonin (
p
= 0.036), and 7-methylxanthine (
p
= 0.037). After the Bonferroni adjustment, the effects for cysteine, uric acid, and 5-HIAA were still significant from the analysis of sex differences and kynurenine and indoleacetic acid were still significant from the analysis of race differences.
Conclusion
Several of the metabolites assayed in this study have been associated with mental health disorders and neurological diseases. Our data provide some novel information regarding normal variations by sex and race in CSF metabolite levels within the tryptophan, tyrosine and purine pathways, which may help to enhance our understanding of mechanisms underlying sex and race differences and potentially prove useful in the future treatment of disease.
Near infrared (NIR) reflectance spectroscopy has been applied to the problem of differentiating four genotypes of durum wheat: ‘waxy’, Wx A1 null null, wx-B1 null and wild type. The test data ...consisted of 95 NIR reflectance spectra of wheat samples obtained from a USDA-ARS wheat breeding program. A two-step procedure for pattern recognition analysis of NIR spectral data was employed. First, the wavelet packet transform 14,15 was applied to the NIR reflectance data using wavelet filters at different scales to extract and separate low-frequency signal components from high frequency noise components. By applying these filters, each reflectance spectrum was decomposed into wavelet coefficients that represented the sample's constituent frequencies. Second, wavelet coefficients characteristic of the waxy condition of the wheat samples were identified using a genetic algorithm for pattern recognition. The pattern recognition GA employed both supervised and unsupervised learning to identify wavelet coefficients that optimized clustering of the spectra by genotype in a plot of the two largest principal components of the data. By sampling key feature subsets, scoring their PC plots, and tracking those genotypes and samples that were difficult to classify, the pattern recognition GA was able to identify a set of wavelet coefficients whose PC plot showed clustering of the wheat samples on the basis of their ‘waxy’ condition. Object validation was also performed to assess the predictive ability of the proposed NIR method to identify the ‘waxy’ condition of the wheat. An overall classification success rate of 78% was achieved for the spectral data.
•Waxy condition of wheat can be assessed using NIR reflectance spectroscopy.•Four genotypes of durum wheat were differentiated from wavelet preprocessed spectra.•Genetic algorithm was used to identify informative wavelet coefficients.•A two-step procedure for pattern recognition analysis of NIR data was employed.
Search prefilters developed from spectral data collected on two 6700 Thermo-Nicolet FTIR spectrometers were able to identify the respective manufacturing plant and the production line of an ...automotive vehicle from its clear coat paint smear using IR transmission spectra collected on a Bio-Rad 40A or Bio-Rad 60 FTIR spectrometer. All four spectrometers were equipped with DTGS detectors. An approach based on instrumental line functions was used to transfer the classification model between the Thermo-Nicolet and Bio-Rad instruments. In this study, 209 IR spectra of clear coat paint smears comprising the training set were collected using two Thermo-Nicolet 6700 IR spectrometers, whereas the validation set consisted of 242 IR spectra of clear coats obtained using two Bio-Rad FTIR instruments.
Identifying a motor vehicle from microscopic paint chips left at the crime scene. Display omitted
•A general approach to pattern recognition analysis and classification of similar spectra has been developed.•The make and model of an automobile can be identified through pattern recognition analysis of clear coat paint smears.•Search prefilters can be developed using the master instrument's line function to preprocess IR spectra collected on other instruments.
Clear coat searches of the infrared (IR) spectral library of the paint data query (PDQ) forensic database often generate an unusable number of hits that span multiple manufacturers, assembly plants, ...and years. To improve the accuracy of the hit list, pattern recognition methods have been used to develop search prefilters (i.e., principal component models) that differentiate between similar but non-identical IR spectra of clear coats on the basis of manufacturer (e.g., General Motors, Ford, Chrysler) or assembly plant. A two step procedure to develop these search prefilters was employed. First, the discrete wavelet transform was used to decompose each IR spectrum into wavelet coefficients to enhance subtle but significant features in the spectral data. Second, a genetic algorithm for IR spectral pattern recognition was employed to identify wavelet coefficients characteristic of the manufacturer or assembly plant of the vehicle. Even in challenging trials where the paint samples evaluated were all from the same manufacturer (General Motors) within a limited production year range (2000–2006), the respective assembly plant of the vehicle was correctly identified. Search prefilters to identify assembly plants were successfully validated using 10 blind samples provided by the Royal Canadian Mounted Police (RCMP) as part of a study to populate PDQ to current production years, whereas the search prefilter to discriminate among automobile manufacturers was successfully validated using IR spectra obtained directly from the PDQ database.
Identifying a motor vehicle from microscopic paint chips left at the crime scene Display omitted
•Make and model of automobile is identified from IR spectrum of clear coat paint smear.•Wavelet transformed IR spectra proved to be the most informative.•IR search prefilters for make and model were developed using PC plots.•Genetic algorithm was used to identify informative wavelet coefficients.
An integrated chemical and microbiological approach was used to develop a new sampling and analytical methodology to characterize the fungal load of a contaminated area or building. A set of ...microbial volatile organic compound (MVOC) profiles were developed with corresponding bioaerosol measurements as input–output pairs for a discriminant to predict the presence or absence of mold contamination in indoor environments. Spore collection to characterize the indoor air quality of the residences and buildings was performed using an Anderson N6 impactor. Simultaneously, solid phase microextraction was used as a passive sampling device to collect VOCs from the air for GC/MS analysis. The volatile organic signatures that molds emit as reflected by the gas chromatographic profiles were compared to the impactor data collected from each sampling site. By comparing the bioaerosol data to the volatile organic profiles, a discriminant could be trained to classify a residence with potential mold growth based on its MVOCs.
► A new sampling and analytical methodology based on an integrated chemical and microbiological approach was developed to assess the fungal load of a contaminated area or building. ► A microbial VOC profile indicative of the degree of contamination of an indoor environment by fungal molds can measured using SPME and GC/MS. ► GC profiles of microbial VOCs sampled by SPME can be correlated to Anderson impactor data using discriminant analysis.
Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear ...coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.
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