Ignitable liquid identification using gas chromatography/mass spectrometry data by projected difference resolution mapping and fuzzy rule-building expert system classification

The gasoline and kerosene collected from different locations in the United States were identified by gas chromatography/mass spectrometry (GC/MS) followed by chemometric analysis. Classifications based on two-way profiles and target component ratios were compared. The projected difference resolution (PDR) mapping was applied to measure the differences among the ignitable liquid (IL) samples by their GC/MS profiles quantitatively. Fuzzy rule-building expert systems (FuRESs) were applied to classify individual ILs. The FuRES models yielded correct classification rates greater than 90% for discriminating between samples. PDR mapping, a new method for characterizing complex data sets was consistent with the FuRES classification result.     

Determining the human origin of fragments of burnt bone: a comparative study of histological, immunological and DNA techniques.

In situations where badly burnt fragments of bone are found, identification of their human or non-human origin may be impossible by gross morphology alone and other techniques have to be employed. In order to determine whether histological methods were redundant and should be superseded by biomolecular analyses, small fragments of artificially burnt bone (human and non-human) were examined by quantitative and standard light microscopy, and the findings compared with newer biomolecular analyses based on identifying specific human albumin by ELISA and amplifying human mitochondrial DNA by PCR. For quantitative microscopy, reference data were first created using burnt bones from 15 human and 20 common domestic and farm animals. Measured osteon and Haversian canal parameters were analysed using multivariate statistical methods. Highly significant differences were found between values for human and non-human bone, and a canonical discriminant function equation was derived, giving a predicted correct classification of 79%. For the main study, samples of cortical bone were taken from three fresh cadavers, six human skeletons and ten freshly slaughtered animals and burnt by exposure to temperatures ranging from 800 to 1200 degrees C; charred fragments of human cortical bone from two forensic cases were also tested. Quantitative microscopy and canonical discriminant function gave the correct origin of every sample. Standard microscopy falsely assigned burnt bone from one human skeleton and one forensic case to a non-human source, but otherwise gave correct results. Human albumin was identified in five individuals, including one of the forensic cases, but mitochondrial DNA could not be amplified from any of the human bone. No false positive test results were seen with either biomolecular method; and human albumin and mitochondrial DNA were correctly identified in all unburnt control specimens. It was concluded that histological methods were not redundant and that quantitative microscopy provided an accurate and consistent means of determining the human or non-human origin of burnt bone and was more reliable than standard microscopy or the newer immunological and DNA techniques tested here.     

A new method for discriminating African-American from European-American skeletons using postcranial osteometrics reflective of body shape.

A discriminant function analysis based on seven postcranial measurements for the metric assessment of race is presented. A sample from the Terry Collection (NMNH) was used to create independent functions for African-American males and females, and European-American males and females. The functions were tested using known forensic cases from the Maxwell Museum of Anthropology and the C.A. Pound Human Identification Laboratory. Based on the Terry Collection sample, correct classification of race for males was 87.0%, and for females 100.0%. For the independent test population, correct classification for males was 81.8%, and for females only 57.1%. The low classification for females is most likely due to sample bias.     

Evaluation of the (hu)MANid program for sex and ancestry estimation in a diverse,contemporary CT scan-based sample

Human remains from forensic and bioarcheological contexts are often fragmentary, requiring methods for estimating a forensic profile that are based upon limited skeletal features. In 2017, Berg and Keryhercz created an online application, (hu)MANid, that provides sex and ancestry estimation from mandibular morphoscopic traits and linear measurements. In this study, we examine the utility of the (hu)MANid application in a diverse, urban US adult sample (aged 20–45; n = 143) derived from computed tomography (CT) scans. We secondarily conduct a preliminary analysis of the program's utility in a sample of adolescents (aged 15–17; n = 40). Six morphoscopic, and eleven morphometric traits were recorded as directed by the literature associated with the (hu)MANid program. Percent correct classification and posterior predictive values were calculated for the sex and ancestry estimations output by the program; chi-squared tests were employed to compare self-reported and predicted ancestry. In the adult sample, sex was accurately predicted for 75.52% of the sample. Ancestry prediction, however, was less favorable ranging from 19.3% to 50% correct. For the adolescent sample, correct sex estimation (45%) did not surpass what could occur by chance alone, though ancestry prediction fared better than in the larger adult sample (percent correct prediction overall average: 47.5%, range 35.71%–71.43%). The (hu)MANid application shows utility for use with CT scan-derived adult samples for sex estimation, but caution is warranted for ancestry estimation and use with samples that may not have reached full adult maturity.     

The Optimized Summed Scored Attributes Method for the Classification of U.S. Blacks and Whites: A Validation Study

Recently, Hefner and Ousley (2014) introduced the optimized summed scored attributes (OSSA) method that maximizes between‐group differences in U.S. black and white populations by dichotomizing six cranial morphoscopic trait scores. This study tests OSSA using an independent skeletal sample (Hamann‐Todd, n = 208) and positively identified forensic cases (Mercyhurst University, n = 28, and New York City Office of Chief Medical Examiner, n = 38). An evaluation of trait frequencies suggests shifting the heuristically selected sectioning point separating U.S. black and white populations from ≤ 3 to ≤ 4. We found a total correct classification of 73.0% (B = 50.9%, W = 89.2%) using the originally suggested sectioning point of ≤3, while the total correct classification increases to 79.2% (B = 80.2%, W = 78.5%) with a modified sectioning point of ≤4. With the increased total correct classification and reduced classification bias between ancestry groups, we suggest the modified sectioning point of ≤4 be used when assessing ancestry in forensic unknowns.     

Identification Accuracy of Children versus Adults: A Meta-Analysis

Identification accuracy of children and adults was examined in a meta-analysis. Preschoolers (M = 4 years) were less likely than adults to make correct identifications. Children over the age of 5 did not differ significantly from adults with regard to correct identification rate. Children of all ages examined were less likely than adults to correctly reject a target-absent lineup. Even adolescents (M = 12–13 years) did not reach an adult rate of correct rejection. Compared to simultaneous lineup presentation, sequential lineups increased the child–adult gap for correct rejections. Providing child witnesses with identification practice or training did not increase their correct rejection rates. Suggestions for children's inability to correctly reject target-absent lineups are discussed. Future directions for identification research are presented.     

Quantification of Maxillary Dental Arcade Curvature and the Estimation of Biological Ancestry in Forensic Anthropology

Previous studies suggest that palate shape is a useful indicator of biological ancestry in human remains. This study evaluates interobserver error in ancestry estimation using palate shape and explores palate shape variation in Gullah (descendants of West Africans) and Seminole (Indigenous American) population samples using geometric morphometric analysis. Ten participants were asked to ascribe biological ancestry and shape to 28 dental casts based on a classification scheme employed in previous studies. The mean correct classification was 42.0%, indicating that the likelihood of assigning the correct ancestry is very poor and not significantly different from random assignment (p = 0.12). The accuracy analysis based on categorical classification of the casts was complemented by geometric morphometric analysis of nine 3D landmarks reflecting palate shape of 158 casts. Principal component analysis results show no difference between populations regarding palate shape, and cross‐validated discriminant function analysis correctly classified only 62.0% of the specimens. Combined, these results show that previous methods to estimate ancestry are inaccurate and that this inaccuracy is probably due to a lack of palate shape differences between groups, rather than limitation of the analytical method per se. Therefore, we recommend caution should be used when choosing to apply the analysis of palate shape in forensically relevant contexts.     

Morphoscopic Trait Expression in “Hispanic” Populations

This study evaluates population variation of eight cranial morphoscopic traits using samples of known southwest Hispanics (n = 72), Guatemalans (n = 106), American Blacks (n = 146), and American Whites (n = 218). We applied the support vector machine (SVM) method to build a prediction model based on a subsample (20%) of the data; the remainder of the data was used as a test sample. The SVM approach effectively differentiated between the four groups with correct classification rates between 72% (Guatemalan group) and 94% (American Black group). However, when the Guatemalan and southwest Hispanic samples were pooled, the same model correctly classified all groups with a higher degree of accuracy (American Black = 96%; American White = 77%; and the pooled Hispanic sample = 91%). This study also identified significant differences between the two Hispanic groups in six of the eight traits using univariate statistical tests. These results speak to the unique population histories of these samples and the current use of the term “Hispanic” within forensic anthropology. Finally, we argue that the SVM can be used as a classification model for ancestry estimation in a forensic context and as a diagnostic tool may broaden the application of morphoscopic trait data for the assessment of ancestry.     

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.     

Corneal‐Smart Phone: A novel method to intelligently estimate postmortem interval

The changes of postmortem corneal opacity are often used to roughly estimate the postmortem interval (PMI) in forensic practice. The difficulty associated with this time estimate is the lack of objective means to rapidly quantify postmortem corneal changes in crime scenes. This study constructed a data analysis model of PMI estimation and implemented an intelligent analysis system for examining the sequential changes of postmortem corneal digital images, named Corneal‐Smart Phone, which can be used to quickly estimate PMI. The smart phone was used in combination with an attachment device that provided a darkroom environment and a steady light source to capture postmortem corneal images. By segmenting the corneal pupil region images, six color features, Red (R), Green (G), Blue (B), Hue (H), Saturation (S), Brightness (V) and four texture features Contrast (CON), Correlation (COR), Angular Second Moment (ASM), and Homogeneity (HOM), were extracted and correlated with PMI model. The results indicated that CON had the highest correlation with PMI (R² = 0.983). No intra/intersubject variation in CON values were observed (p > 0.05). With the increase in ambient temperature or the decrease in humidity, the CON values were increased. PMI prediction error was <3 h within 36 h postmortem and extended to about 6–8 h after 36 h postmortem. The correct classification rate of the blind test samples was 82%. Our study provides a method that combines postmortem corneal image acquisition and digital image analysis to enable users to quickly obtain PMI estimation.     

Probabilistic Classification of Age by Third Molar Development: The Use of Soft Evidence

Abstract: This study introduces a method to classify individuals according to an age threshold, given sex and third molars’ dental maturity measured on the Demirjian scale by expressing uncertainty on dental evidence (soft evidence). We introduced a procedure to learn the parameters of the Naïve Bayes model, and we discussed two classification rules. The model was estimated and tested on 559 Italians aged 16–22. Two experts provided the dental evaluations, and the model was estimated for each of them. We evaluated the coherence of the evidence provided by the experts. Some indexes have been proposed to evaluate the effectiveness of the models, emphasizing how the experts’ ability and the technology affect the results. We introduced two benchmarks, one based on the sample distribution per sex and age: in this case, probability of correct classification increases 22% and the proportion of false adults impressively decreases 80.2%; the other benchmark, obtained by simulating hard evidence, shows how the use of soft evidence increases the proportion of correct classification 3.1% and decreases the crucial proportion of false adults about 20%. Similarly, the proportion of false minors decreases about 5.3%.     

Introducing Human Mandible Identification [(hu)MANid]: A Free,Web‐Based GUI to Classify Human Mandibles

Statistical programs have revolutionized the way in which forensic anthropologists conduct casework by allowing practitioners to use computationally complex analytics at the click of a button. Importantly, the products of these statistical programs are reproducible and contain measures of error or uncertainty, thereby strengthening conclusions. This paper is an introduction to (hu)MANid, a free, web‐based application that uses linear and mixture discriminant function analyses to classify human mandibles into one of many worldwide and/or periodic reference groups. The mechanics, development, and use of the application will be discussed. Further, the program was tested against other software to compare model performances and classifications. Total correct classifications among the test cases and programs were identical. Ten mandibles were tested using both statistical procedures. Mixture discriminant analysis improved classification by an average of 9.3% and correctly identified three more mandibles than LDA. Therefore, we believe (hu)MANid will be an asset to the anthropological community.     

Trace Element Analysis of Rough Diamond by LA‐ICP‐MS: A Case of Source Discrimination?*,†

Abstract: Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation–inductively coupled plasma–mass spectrometry (LA‐ICP‐MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA‐ICP‐MS methodology that we developed for analyzing ultra‐trace element impurities in rough diamond for origin determination (“profiling”). Diamonds from two sources were analyzed by LA‐ICP‐MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications.     

Time Radically Alters Ex Situ Evidentiary Soil 16S Bacterial Profiles Produced Via Next‐Generation Sequencing,,

Previous research has revealed the potential of soil bacterial profiling for forensic purposes; however, investigators have not thoroughly examined fluctuations in microbial profiles from soil aged on evidence. In this research, soils collected from multiple habitats were placed on evidence items and sampled over time, and then bacterial profiles were generated via next‐generation sequencing of the 16S rRNA locus. Bacterial abundance charts and nonmetric multidimensional scaling plots provided visual representation of bacterial profiles temporally, while supervised classification was used to statistically associate evidence to a source. The ex situ evidence soils displayed specific, consistent taxonomic changes as they aged, resulting in their drift in multidimensional space, but never toward a different habitat. Ninety‐five percent of the 364 evidentiary profiles statistically classified to the correct habitat, with misclassification generally stemming from evidence type and increased age. Ultimately, understanding bacterial changes that occur temporally in ex situ soils should enhance their use in forensic investigations.     

DNA‐Based Identification of Forensically Important Lucilia (Diptera: Calliphoridae) in the Continental United States*

Abstract: Correct species identification is critical when dipteran larvae are used for inference of the postmortem interval. To facilitate DNA‐based identification of forensically important flies of the genus Lucilia in the continental United States, we develop a vouchered reference collection and DNA sequence database. A total of 122 specimens were collected for nine of the 10 species of Lucilia reported to occur in the continental United States. Using the polymerase chain reaction and DNA sequencing, data were obtained for an 1100‐bp region of the mitochondrial gene encoding cytochrome oxidase I (COI). We consider a species suitable for DNA‐based identification if it is exclusively monophyletic in >95% of bootstrap pseudoreplicate phylogenetic analyses. Seven of the nine species meet that criterion. Two species (Lucilia coeruleiviridis and Lucilia mexicana) share COI sequence and cannot be distinguished using our reference database. We conclude that DNA‐based identification is likely to be successful for the other seven species.     

Angular Determination of Toolmarks Using a Computer‐Generated Virtual Tool

A blind study to determine whether virtual toolmarks created using a computer could be used to identify and characterize angle of incidence of physical toolmarks was conducted. Six sequentially manufactured screwdriver tips and one random screwdriver were used to create toolmarks at various angles. An apparatus controlled tool angle. Resultant toolmarks were randomly coded and sent to the researchers, who scanned both tips and toolmarks using an optical profilometer to obtain 3D topography data. Developed software was used to create virtual marks based on the tool topography data. Virtual marks generated at angles from 30 to 85° (5° increments) were compared to physical toolmarks using a statistical algorithm. Twenty of twenty toolmarks were correctly identified by the algorithm. On average, the algorithm misidentified the correct angle of incidence by ?6.12°. This study presents the results, their significance, and offers reasons for the average angular misidentification.     

Source Camera Identification for Heavily JPEG Compressed Low Resolution Still Images*

Abstract: In this research, we examined whether fixed pattern noise or more specifically Photo Response Non‐Uniformity (PRNU) can be used to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 × 480 pixels. We extracted PRNU patterns from both reference and questioned images using a two‐dimensional Gaussian filter and compared these patterns by calculating the correlation coefficient between them. Both the closed and open‐set problems were addressed, leading the problems in the closed set to high accuracies for 83% for single images and 100% for around 20 simultaneously identified questioned images. The correct source camera was chosen from a set of 38 cameras of four different types. For the open‐set problem, decision levels were obtained for several numbers of simultaneously identified questioned images. The corresponding false rejection rates were unsatisfactory for single images but improved for simultaneous identification of multiple images.     

Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine

The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X‐ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross‐validation test and independent test for accuracy (P_A). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.     

Whole-face procedures for recovering facial images from memory

Research has indicated that traditional methods for accessing facial memories usually yield unidentifiable images. Recent research, however, has made important improvements in this area to the witness interview, method used for constructing the face and recognition of finished composites. Here, we investigated whether three of these improvements would produce even-more recognisable images when used in conjunction with each other. The techniques are holistic in nature: they involve processes which operate on an entire face. Forty participants first inspected an unfamiliar target face. Nominally 24 h later, they were interviewed using a standard type of cognitive interview (CI) to recall the appearance of the target, or an enhanced ‘holistic’ interview where the CI was followed by procedures for focussing on the target's character. Participants then constructed a composite using EvoFIT, a recognition-type system that requires repeatedly selecting items from face arrays, with ‘breeding’, to ‘evolve’ a composite. They either saw faces in these arrays with blurred external features, or an enhanced method where these faces were presented with masked external features. Then, further participants attempted to name the composites, first by looking at the face front-on, the normal method, and then for a second time by looking at the face side-on, which research demonstrates facilitates recognition. All techniques improved correct naming on their own, but together promoted highly-recognisable composites with mean naming at 74% correct. The implication is that these techniques, if used together by practitioners, should substantially increase the detection of suspects using this forensic method of person identification.