Elemental Characterization and Discrimination of Nontoxic Ammunition Using Scanning Electron Microscopy with Energy Dispersive X‐Ray Analysis and Principal Components Analysis

Concerns over the toxic by‐products produced by traditional ammunition have led to an increase in popularity of nontoxic ammunition. In this work, the chemical composition of six brands of nontoxic ammunition was investigated and compared to that of a road flare, which served as an environmental source with similar composition. Five rounds of each brand were fired while a further five were disassembled and the primer alone was fired. Particles collected from all samples, including the road flare, were analyzed by scanning electron microscopy with energy dispersive X‐ray analysis. Common elements among the different ammunition brands included aluminum, potassium, silicon, calcium, and strontium. Spectra were then subjected to principal components analysis in which association of the primer to the intact ammunition sample was generally possible, with distinction among brands and from the road flare sample. Further, PCA loadings plots indicated the elements responsible for the association and discrimination observed.     

Differentiation of unevaporated gasoline samples according to their brands, by SPME-GC-MS and multivariate statistical analysis

One of the aims of fire investigations is to identify associations among accelerants according to their source. In this study, 50 gasoline samples--representing five brands--were analyzed using solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). Chemometric procedures, such as principal component analysis (PCA) and discriminant analysis (DA), were applied to a data matrix obtained by the target compound chromatogram method, to discriminate samples according to their brand. PCA was successful in finding a natural grouping of samples according to their brand, suggesting that aromatic compounds were more useful than aliphatics for the purpose of this study. DA, if applied to aromatic compounds, gave both a classification ability and a prediction ability of 100%. The outstanding results obtained by this work provide the basis of a data matrix that could be used in real cases of arson to link a sample of unevaporated gasoline to its brand or refinery.     

Classification of Opium by UPLC‐Q‐TOF Analysis of Principal and Minor Alkaloids

Opium is the raw material for the production of heroin, and the characterization of opium seizures through laboratory analysis is a valuable tool for law enforcement agencies to trace clandestine opium production and trafficking. In this work, a method for opium profiling based on the relative content of five principal and 14 minor opium alkaloids was developed and validated. UPLC‐Q‐TOF was adopted in alkaloid analysis for its high selectivity and sensitivity, which facilitated the sample preparation and testing. The authentic sample set consisted of 100 “Myanmar” and 45 “Afghanistan” opium seizures; based on the data set of the 19 alkaloid variables in them, a partial least squares discriminant analysis classification model was successfully achieved. Minor alkaloids were found to be vitally important for opium profiling, although combined use of both principal and minor alkaloids resulted in the best geographical classification result. The developed method realized a simple and accurate way to differentiate opium from Myanmar and Afghanistan, which may find wide application in forensic laboratories.     

Forensic Discrimination of Dyed Hair Color: II. Multivariate Statistical Analysis*,†

Abstract: This research is intended to assess the ability of UV–visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty‐five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two‐dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.     

Sex Classification Using the Three‐Dimensional Tibia Form or Shape Including Population Specificity Approach

The aims of this study were to enable geometric morphometric sex classification using tibial proximal and distal sexual dimorphism and to evaluate the secular trend of tibial shape/form from the early 20th century to the present day. The study samples consisted of 61 adult tibias from an early 20th‐century Czech population and 57 three‐dimensional tibias from a 21st‐century population. Discriminant function analysis with cross‐validation was carried out to assess the accuracy of sex classification. Shape analysis revealed significant sex differences in both tibial extremities of the 21st‐century sample and in the proximal tibia of the 20th‐century population. Sex‐based divergence varied between the analyzed samples, raising the issues of population specificity and diachronic change. Classification using tibial form was more successful than using tibial shape. The highest values of correct assignment (91.80% and 88.52%) were found using the form from the early 20th Czech population.     

Detection of trace drugs of abuse in baby formula using solid‐phase microextraction direct analysis in real‐time mass spectrometry (SPME‐DART‐MS)

The intentional or unintentional adulteration of baby formula with drugs of abuse is one of the many increasingly complex samples forensic chemists may have to analyze. This sample type presents a challenge because of a complex matrix that can mask the detection of trace drug residues. To enable screening of baby formula for trace levels of drugs, the use of solid‐phase microextraction (SPME) coupled with direct analysis in real‐time mass spectrometry (DART‐MS) was investigated. A suite of five drugs was used as adulterants and spiked into baby formula. Samples were then extracted using SPME fibers which were analyzed by DART‐MS. Development of a proof‐of‐concept method was completed by investigating the effects of the DART gas stream temperature and the linear speed of the sample holder. Optimal values of 350°C and 0.2 mm/s were found. Once the method was established, representative responses and sensitivities for the five drugs were measured and found to be in the range of single ng/mL to hundreds ng/mL. Additional studies found that the presence of the baby formula matrix increased analyte signal (relative to methanolic solutions) by greater than 200%. Comparison of the SPME‐DART‐MS method to a traditional DART‐MS method for trace drug detection found at least a factor of 13 improvement in signal for the drugs investigated. This work demonstrates that SPME‐DART‐MS is a viable technique for the screening of complex matrices, such as baby formula, for trace drug residues and that development of a comprehensive method is warranted.     

Progress Toward the Determination of Correct Classification Rates in Fire Debris Analysis,,

Principal components analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to develop a multistep classification procedure for determining the presence of ignitable liquid residue in fire debris and assigning any ignitable liquid residue present into the classes defined under the American Society for Testing and Materials (ASTM) E 1618‐10 standard method. A multistep classification procedure was tested by cross‐validation based on model data sets comprised of the time‐averaged mass spectra (also referred to as total ion spectra) of commercial ignitable liquids and pyrolysis products from common building materials and household furnishings (referred to simply as substrates). Fire debris samples from laboratory‐scale and field test burns were also used to test the model. The optimal model's true‐positive rate was 81.3% for cross‐validation samples and 70.9% for fire debris samples. The false‐positive rate was 9.9% for cross‐validation samples and 8.9% for fire debris samples.     

Provenance Establishment of Stingless Bee Honey Using Multi‐element Analysis in Combination with Chemometrics Techniques

As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location‐specific, multi‐element distribution patterns may prove useful for provenancing such product. Using the inductively coupled‐plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi‐elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross‐validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi‐element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported.     

Characterization of Printing Inks Using DART‐Q‐TOF‐MS and Attenuated Total Reflectance (ATR) FTIR

The rise in improved and widely accessible printing technology has resulted in an interest to develop rapid and minimally destructive chemical analytical techniques that can characterize printing inks for forensic document analysis. Chemical characterization of printing inks allows for both discrimination of inks originating from different sources and the association of inks originating from the same source. Direct analysis in real‐time mass spectrometry (DART‐MS) and attenuated total reflectance Fourier transform infrared spectroscopy (ATR‐FTIR) were used in tandem to analyze four different classes of printing inks: inkjets, toners, offset, and intaglio. A total of 319 samples or ~ 80 samples from each class were analyzed directly on a paper substrate using the two methods. DART‐MS was found to characterize the semi‐volatile polymeric vehicle components, while ATR‐FTIR provided chemical information associated with the bulk components of these inks. Complimentary data results in improved discrimination when both techniques are used in succession resulting in >96% discrimination for all toners, 95% for all inkjets, >92% for all offset, and >54% for all intaglio inks.     

Comparative Forensic Soil Analysis of New Jersey State Parks Using a Combination of Simple Techniques with Multivariate Statistics,

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle‐size distribution, pH analysis in both water and 1 M CaCl₂, and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold‐one‐out cross‐validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications.     

Using Cluster Analysis and ICP‐MS to Identify Groups of Ecstasy Tablets in Sao Paulo State,Brazil

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty‐five elements were determined by ICP‐MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K‐means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave‐one‐out cross‐validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples.     

Forensic Analysis of Stains on Fabric Using Direct Analysis in Real‐time Ionization with High‐Resolution Accurate Mass‐Mass Spectrometry

A rapid technique using direct analysis in real ‐ time (DART) ambient ionization coupled to a high ‐ resolution accurate mass‐mass spectrometer (HRAM‐MS) was employed to analyze stains on an individual's pants suspected to have been involved in a violent crime. The victim was consuming chocolate ice cream at the time of the attack, and investigators recovered the suspect's pants exhibiting splatter stains. Liquid chromatography with mass spectral detection (LC‐MS) and stereoscopic light microscopy (SLM) were also utilized in this analysis. It was determined that the stains on the pants contained theobromine and caffeine, known components of chocolate. A shard from the ceramic bowl that contained the victim's ice cream and a control chocolate ice cream sample were also found to contain caffeine and theobromine. The use of DART‐HRAM‐MS was useful in this case due to its rapid analysis capability and because of the limited amount of sample present as a stain.     

A Two‐Level Model for Evidence Evaluation in the Presence of Zeros*

Abstract: Likelihood ratios (LRs) provide a natural way of computing the value of evidence under competing propositions. We propose LR models for classification and comparison that extend the ideas of Aitken, Zadora, and Lucy and Aitken and Lucy to include consideration of zeros. Instead of substituting zeros by a small value, we view the presence of zeros as informative and model it using Bernoulli distributions. The proposed models are used for evaluation of forensic glass (comparison and classification problem) and paint data (comparison problem). Two hundred and sixty‐four glass samples were analyzed by scanning electron microscopy, coupled with an energy dispersive X‐ray spectrometer method and 36 acrylic topcoat paint samples by pyrolysis gas chromatography hyphened with mass spectrometer method. The proposed LR model gave very satisfactory results for the glass comparison problem and for most of the classification tasks for glass. Results of comparison of paints were also highly satisfactory, with only 3.0% false positive answers and 2.8% false negative answers.     

Chemical Differentiation of Osseous and Nonosseous Materials Using Scanning Electron Microscopy–Energy-Dispersive X-Ray Spectrometry and Multivariate Statistical Analysis

Identification of osseous materials is generally established on gross anatomical features. However, highly fragmented or taphonomically altered materials may be problematic and may require chemical analysis. This research was designed to assess the use of scanning electron microscopy–energy-dispersive X-ray spectrometry (SEM/EDX), elemental analysis, and multivariate statistical analysis (principal component analysis) for discrimination of osseous and nonosseous materials of similar chemical composition. Sixty samples consisting of osseous (human and nonhuman bone and dental) and non-osseous samples were assessed. After outliers were removed a high overall correct classification of 97.97% was achieved, with 99.86% correct classification for osseous materials. In addition, a blind study was conducted using 20 samples to assess the applicability for using this method to classify unknown materials. All of the blind study samples were correctly classified resulting in 100% correct classification, further demonstrating the efficiency of SEM/EDX and statistical analysis for differentiation of osseous and nonosseous materials.     

Evaluation of a Headspace Solid‐Phase Microextraction Method for the Analysis of Ignitable Liquids in Fire Debris

The chemical analysis of fire debris represents a crucial part in fire investigations to determine the cause of a fire. A headspace solid‐phase microextraction (HS‐SPME) procedure for the detection of ignitable liquids in fire debris using a fiber coated with a mixture of three different sorbent materials (Divinylbenzene/Carboxen/Polydimethylsiloxane, DVB/CAR/PDMS) is described. Gasoline and diesel fuel were spiked upon a preburnt matrix (wood charcoal), extracted and concentrated with HS‐SPME and then analyzed with gas chromatography/mass spectrometry (GC/MS). The experimental conditions—extraction temperature, incubation and exposure time—were optimized. To assess the applicability of the method, fire debris samples were prepared in the smoke density chamber (SDC) and a controlled‐atmosphere cone calorimeter. The developed methods were successfully applied to burnt particleboard and carpet samples. The results demonstrate that the procedure that has been developed here is suitable for detecting these ignitable liquids in highly burnt debris.     

Elemental Analysis of Variably Contaminated Cremains Using X‐ray Fluorescence Spectrometry

Analyzing and identifying skeletal remains becomes increasingly difficult when remains have been cremated, especially in cases where the cremated material may have been intentionally contaminated with nonskeletal material. This study examined the potential of X‐ray fluorescence spectrometry (XRF) to detect the presence of nonskeletal contaminants in samples of cremains. Eleven samples of cremains were variably combined with concrete mix and analyzed using XRF. Photon counts of elements in each sample were analyzed, and the coefficient of determination (R²) using unweighted linear regression as a function of percent cremains was calculated. Results showed that with changes in the proportion of skeletal material and contaminant, there were significant (R² > 0.90) changes in detected levels of phosphorus, potassium, zinc, aluminum, and sulfur. The use of XRF is concluded to be a valid approach in the identification of the presence of nonskeletal material in potentially contaminated cremains.     

“YFlag” – A Single‐base Extension Primer Based Method for Gender Determination

Assigning the gender of a DNA contributor in forensic analysis is typically achieved using the amelogenin test. Occasionally, this test produces false‐positive results due to deletions occurring on the Y chromosome. Here, a four‐marker “YFlag” method is presented to infer gender using single‐base extension primers to flag the presence (or absence) of Y‐chromosome DNA within a sample to supplement forensic STR profiling. This method offers built‐in redundancy, with a single marker being sufficient to detect the presence of male DNA. In a study using 30 male and 30 female individuals, detection of male DNA was achieved with c. 0.03 ng of male DNA. All four markers were present in male/female mixture samples despite the presence of excessive female DNA. In summary, the YFlag system offers a method that is reproducible, specific, and sensitive, making it suitable for forensic use to detect male DNA.     

Optimizing Accu Time‐of‐Flight/Direct Analysis in Real Time for Explosive Residue Analysis

The use of a direct analysis in real time (DART) mass spectrometer (MS) instrument was optimized for 22 compounds of organic explosive residues to provide a guide for DART‐MS users in rapid screening of explosive compounds. Samples were introduced as neat solutions and sequential dilutions to determine optimal instrument conditions and lowest concentration detectable. Most compounds were optimized to 250°C in the negative ion mode, and several compounds benefited from the addition of a chloride dopant from methylene chloride (amino‐dinitrotoluenes, RDX, EGDN, and PETN). Few compounds were more sensitive in the positive ion mode (TEGDN, DEGDN, HNS, and DMNB). Mixtures of compounds were detected using clean room wipes, directly from their surfaces and from subsequent extractions. Compounds from the mixtures were also successfully detected in soil and from swipes of spiked surfaces. The instrument showed merit in detection of pg/μL solutions for most of the compounds and among the substrates tested.     

Application of Volume‐of‐Fluid Method to Analyze the Viscosity Effect on the Spine Formation of Bloodstains

In bloodstain pattern analysis, the blood droplet volume and surface impact velocity play an important role, and many related experimental studies have been carried out. If an appropriate computational fluid dynamics (CFD) model that could solve bloodstain patterns, especially spine formation bloodstain patterns, can be obtained, the blood droplet volume and impact speeds at various crime scenes can be predicted more accurately. For this purpose, Flow‐3D software using the volume‐of‐fluid method was applied to analyze the behavior of human blood droplets during an impact event, especially focusing on the viscous effect on splashing, which forms the spine which can be used to predict the impact velocity. To obtain a non‐Newtonian viscosity model of blood for a computational fluid dynamic analysis, the venous blood samples of 163 people were tested using a hemorheology instrument. Among the venous blood samples of 163 people, 37 samples for which all blood test results were in a normal range were selected for the non‐Newtonian viscosity modeling. From the CFD analysis, it could be concluded that a non‐Newtonian viscosity model is more appropriate than a constant viscosity model for predicting splashing that forms the spine. The gradient of the non‐Newtonian model at a high shear rate has more of an effect on spine formation than that at a low shear rate. The lowest viscosity with a high velocity at the outer front of the radiating flow plays an important role in forming the splashing pattern.