Journal of forensic sciences最新文献

Firearm examiners may face challenges with subclass characteristics. Subclass characteristics have the potential to be confused with individual characteristics if a careful analysis for the presence of subclass characteristics is not performed. Several case studies are reported in the literature that discuss the presence of subclass characteristics and advise firearm examiners to disregard these characteristics for identification purposes. As far as can be determined, no fundamental study has been performed that explores the various methods used in firearm manufacturing and assess the potential of subclass characteristics being produced. In this study broaching, plunge milling, and lathe turning were used to produce consecutively manufactured breech faces for a Thompson/Center® (T/C®) Contender® G2 pistol. The breech faces were finished by tumbling or glass bead blasting. The 3D topographical scans of the breech faces were collected using a confocal microscope and then compared using the National Institute of Standards and Technology (NIST) congruent matching cells (CMC) algorithm. The results of this study demonstrated the presence of subclass characteristics after manufacturing by broaching and plunge milling. Lathe turning did not produce any subclass characteristics. ANOVA and Kruskal–Wallis H tests were used to determine significant differences between finishing methods within each manufacturing method. Significant differences were found in glass bead blasting versus tumbling and glass bead blasting versus no finish. No significant differences were found in no finish versus tumbling.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Insects have long played a role in forensic investigations and can be used to estimate minimum time since death, corpse translocation, and link an individual to a crime scene. Bed bugs (Cimex lectularius) are wingless ectoparasitic insects of potential forensic utility, given that all mobile life stages feed on vertebrate blood. Successful profiling of autosomal short tandem repeats (STRs) from human DNA isolated from bed bugs has been previously reported. This proof-of-concept study looked to expand this work and determine any possible limitations of using bed bugs for both rapid stain identification (RSID™) for human blood and Y-STR profiling. To achieve this, bed bugs were fed either human male only or human pooled (female:male) blood for 30 min and subsequently collected at 12-h intervals up to 108 h post-blood meal (PBM). RSID™ blood testing was successful from the bed bug carcass remaining after DNA isolation, regardless of blood meal type and time of collection PBM. Complete Y-STR profiles were generated from bed bugs <60 h PBM. As the time PBM increased, DNA quantity decreased, while the degradation index increased. Collection of bed bugs at a crime scene could provide a valuable source of human blood for Y STR profiling and be used to link an individual to a crime scene or for potential male suspect exclusion. Future studies should look to replicate the results of this proof-of-concept study with larger numbers of bed bugs, more diverse blood donors, and additional STR profiling kits.

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context-specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time-consuming and often impractical for large-scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high-resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen–Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS-based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally.

In the field of questioned document examination, determining the sequence of intersecting lines is still a technical challenge. This study aims to validate whether confocal Raman spectroscopy can determine the sequence of intersecting lines created by laser prints toner and seal ink through empirical research. The study collected 110 varieties of seal ink and 1074 test pages from 66 models of laser printers available in the Chinese market. Based on the Raman spectral characteristics of the seal ink and the microscopic morphology of the toner, 13 types of seal ink and three types of laser printers were selected for further analysis, producing 78 representative samples of intersecting lines. Confocal Raman spectroscopy was applied to the examination of these samples using a point-scanning mode for enhanced accuracy and efficiency. The experimental results demonstrate that confocal Raman spectroscopy can non-destructively and quickly examine the sequence of intersecting lines produced by laser printer toner and seal ink. Variations in toner forms result in differing levels of difficulty in resolving intersection problems, with the sequence of intersecting lines from toner-dense laser printers being the easiest to ascertain. In contrast, those from printers with porous and dispersed toner present a more significant challenge in examination. This study can be corroborated with other methods proposed, and more significantly, it lays the groundwork for addressing intersection problems related to other printing or writing instruments.

Semi-synthetic cannabinoids (SSCs) are derivatives of phytocannabinoids with slight chemical modifications. SSCs have appeared as legal alternatives to tetrahydrocannabinol (Δ⁹-THC) in recent years. This study investigates the prevalence of SSCs in seized drug samples from Danish police and custom authorities seized in Eastern Denmark in the period 2018–2023. Screening data obtained by gas chromatography–mass spectrometry (GC–MS) were reprocessed to enable detection of SSCs. Seized drug samples were categorized into six types of formulations. Δ⁸-THC was the first SSC observed and appeared in 2019 followed by hexahydrocannabinol (HHC), tetrahydrocannabidiol (H4-CBD), hexahydrocannabinol acetate (HHC-O-Acetate), hexahydrocannabiphorol (HHCP) and tetrahydrocannabiphorol (Δ⁹-THCP). Only one sample positive for SSCs was observed before the third quarter of 2021, with positive samples increasing from third quarter of 2022. Over the study period, a total of 15% (n = 216) of seized cannabis products were positive for SSCs. HHC was the most frequently identified SSC and found in 10% (n = 137) of samples, followed by H4-CBD at 4% (n = 53), Δ⁸-THC at 3% (n = 44), and HHC-O-Acetate, HHCP, and THCP each at 1% (n = 10–20). SSCs appeared in 56% of E-cigarette products, 20% of hashish, 17% of concentrates, 10% of edibles, and 10% of plant materials. In conclusion, SSCs represent a new type of cannabinoids with a rapidly growing popularity and with specific compounds dominating at different periods. Some of the observed trends were likely influenced by the scheduling of HHC in May of 2023 in Denmark.

This technical note describes in detail a method for associating individual sheets of blank A4 white paper from the same ream by the physical fit of machine-cut edges. A large-scale laboratory trial involving ~700 sheets of paper from 24 different reams (plus one spoiled sample), and more than 20,000 potential physical fits, correctly associated and sequenced 219 pairs of sheets together with a 100% empirical success rate and no false associations. The edge profile of each short machine-cut end of a sheet of A4 paper allows us to physically fit sheets of paper from the same ream to each other and use this to predict the sequence of sheets in a set of documents. In a real-life scenario, it may now be possible to detect the substitution or addition of a sheet in a multipage document, link documents from different sources to each other or to a common source of paper (e.g. to paper from a seized printer or from an accused's address) or to date documents. The study provides data for the application of this method in forensic casework and supports the practitioner when forming conclusions in this type of case.

The larval excretions/secretions (ES) of blowflies contain proteolytic enzymes and bacteria that assist with tissue breakdown. Decomposition fluid (DF) contains organic and inorganic waste products from cell death. This study investigated if human DNA recovery from blood was impacted by exposure to ES and DF over time. Lucilia sericata ES were collected daily from 50 larvae, and all available DF was collected from two fetal piglets left to decompose for 2 weeks. Daily for 3–5 days, 28 μL-30 μL of ES, DF, or a 1:1 mixture of the fluids was added to 30 μL of blood on cotton. Three bloodstains per treatment were sampled every 12 h up to 3 days and at 1 and 2 weeks after initial addition of fluid. No PCR inhibition was detected, but DNA degradation increased over time, primarily in samples exposed to ES and ES/DF mixtures. The amount of DNA recovered decreased over time, but generally more DNA was recovered from DF samples than other samples. Full profiles, or partial profiles suitable for routine database searching (14–39 alleles), were generated from all DF and ES samples and at least one mixture sample at all timepoints. Partial profiles of between 1 and 13 alleles were obtained from all other mixture samples, except one mixture sample which generated no profile. These findings indicate bloodstain evidence recovered from maggot-infested and/or decomposing bodies may generate forensically useful DNA evidence and should be analyzed as quickly as possible after collection or stored appropriately to prevent further degradation.

Various samples—including two vials with a pharmaceutical appearance—were submitted to the laboratory for identification. The aim of this work was to describe the unique characteristics observed during the analysis of the powder contained in the vial. Samples were submitted to HPLC-DAD, UHPLC-TOF-MS, and/or UPLC-MS–MS analysis. The majority of the samples were easily identified as standard drugs of abuse. The main difficulty lay in identifying the powder in the vials. No match was found in the library through HPLC-DAD analysis. Fortunately, the vials were labeled as “Melanotan II”, although the UV spectrum was not available. Mass spectrometric analysis of melanotan II was challenging, as it is a small peptide with a molecular weight of 1024 Da, which is significantly heavier than classical drugs that the laboratory usually handles. As a result, mass spectrometer's parameters can be limited to detect masses up to 1000 Da. Additionally, melanotan II is multi-charged which is also unusual for compounds typically targeted in our daily work. Finally, the reference standard allowed us to confirm the identification with both instruments, and determine the purity of 30%. Melanotan II is not approved on the market due to safety concerns. It is used illegally mainly for tanning, explaining its nickname “Barbie drug”. To conclude, analysis of melanotan II was challenging as it is heavy and doubly charged. Moreover, its UV spectrum was initially not available in the literature. The difficulties faced by forensic scientists in detecting this drug may explain its popularity on the illicit market.

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X-ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine-tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10–20]) to 13.40 years (age group [90–100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real-world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios.