Journal of forensic sciences最新文献

Immunolabeling based on fluorescence is a new technique that has been recently applied in the field of forensic science. In this paper, a new immunofluorescence method based on signal amplification was applied to develop fingermarks and improve the quality of pattern recognition with clear ridge details and high contrast. The high affinity between biotin and avidin and the one-to-many binding mode can connect several fluorescent groups together to achieve a signal amplification effect. The results indicated that the fluorescence intensity of the fingermark sample, as displayed by the biotin-avidin signal amplification system (BAS), was nearly three times higher than that revealed by previous immunolabeling methods based on fluorescence. Specifically, more fluorescent chromophores were bound to the friction ridges in BAS. Two proteins were selected as experimental target proteins for fingermark immunofluorescence visualization to optimize the visualization effect. The results showed that compared to keratin 1, dermcidin as the target protein in BAS achieved a more desirable effect, with 88.9% of the experimental samples left on nonporous objects having identification value. This method provides new insights for the development of fingermark spectra and is expected to become an effective and safe technology in the field of forensic science.

A piece of paper submitted as evidence can both have fingermarks with a high and low amount of sweat. When such paper is treated with 1,2-indanedione/zinc and subsequently heated at 160°C for 10 s, fingermarks with high sweat content may become overdeveloped. Attempts to prevent overdevelopment by reducing the heating time to <10 s were ineffective. However, it was found that maintaining the heating time at 10 s and reducing the temperature to below 160°C effectively prevented overdevelopment. In forensic practice involving latent fingermarks on actual evidence, an effective enhancement technique involves initially placing approximately 15 sheets of paper between the sample and an iron preheated to 160°C, and then applying heat for 10 s. This is subsequently followed by a secondary heating without the paper. This procedure has proven effective in enhancing fingermarks with both high and low sweat contents.

In recent years, there has been a significant increase in metal prices, particularly precious metals. Consequently, property crimes involving metal theft, including the theft of auto parts and catalytic converters, have also increased dramatically. The surge in catalytic converter thefts is linked to the use of precious metals, such as palladium, rhodium, and platinum, in their construction and their high price. A skilled thief can easily and quickly steal the catalytic converter by getting underneath the vehicle and, in a few moments, simply cut the exhaust pipes in front of and behind the catalytic converter using a common cutting tool, such as a reciprocating saw. Beyond the high price and the ease of committing the theft, police investigators experience difficulties finding forensic evidence that will incriminate the suspect. Furthermore, linking the cut items, such as the reciprocating saw, is not a simple challenge because of its mode of operation. The cyclic movement of the saw's blade, back and forth, removes a section of material. Therefore, examining a saw mark is mainly based on details relating to class characteristic features rather than individual characteristics. The authors seek to challenge this approach and demonstrate the feasibility of linking, using microscopic toolmarks comparison, cordless reciprocation saw, and the potential individual marks that may have resulted from the tip of its blade on the catalytic converter during the cutting action.

The house fly, Musca domestica, L. (Diptera: Muscidae), is a filth fly that is often associated with criminal and civil investigations surrounding abuse, neglect, and death of humans and other vertebrates. However, development data, which are crucial for determining the age of immatures collected under forensically relevant circumstances, are limited. Given the lack of data and the recognition of population-specific growth patterns, the aim of this study was to generate data for development of a M. domestica population from Texas, USA, on decomposing lean pork at 24.0°C (i.e., approximate room temperature in Texas) and 37.0°C (i.e., approximate human body temperature). As expected, fly development significantly differed between temperatures with development at the higher temperature taking significantly less time (development from egg to adult emergence occurred c. 48.5% faster at 37.0°C than at 24.0°C). The value of this dataset is demonstrated through an applied comparison with previously published data for the house fly. Differences in development times across life stages for the studies are evident, with shorter time of colonization estimations using the data published by Wang et al. (2018), especially in later life stages. These data represent the first development dataset for the house fly on decomposing flesh in North America. Furthermore, the comparison with the previously published dataset demonstrate data from this study are of value for future forensic investigations in Texas or possibly other parts of the United States where this species is encountered, as they can be used to determine time of colonization.

Decedent positive identification via visual comparisons of frontal sinus radiographs is commonly used in the medicolegal field; however, only a handful of studies have empirically tested this method. This study aimed to test the accuracy of visual assessment in frontal sinus identifications across a large and experientially diverse participant sample. A Qualtrics survey presented participants with 25 pairs of cropped frontal sinus radiographs, asking them to determine if they matched and their confidence level. Radiographs were from the American Association of Orthodontics Legacy Collection. Eighteen radiographic pairs were of the same individual taken a year or more apart. Seven pairs were from different individuals (nonmatches). Euclidean distances were used to select challenging nonmatches with similar outlines. Participants were also asked questions about their profession, training, and experience. The overall accuracy of the 145 respondents (3625 comparisons) was 89.9%, with a median accuracy of 92.0%. The majority of respondents (64.58%) report zero radiographic identification experience. Incorrect responses were biased, with only 3.6% of nonmatches wrongly reported as matches (false positives). Statistical analyses revealed significant effects of profession, radiographic experience, and training on match accuracies and confidence levels (p < 0.05), with a significant correlation between accuracy and confidence level (r_s = 0.302, p < 0.001). These results support the use of frontal sinus visual comparisons in forensic identifications but highlight the importance of training and experience. In practice, accuracy rates are expected to exceed those reported here, given that identifications are made by medicolegal personnel using higher quality radiographs of the entire cranium.

Fingermarks are important forensic evidence for identifying people. In this work, luminescent MOF [Eu₂(BDC)₃(H₂O)₂] (herein referred as EuBDC) was tested as a potential latent fingermark (LF) luminescent developer powder and its acute toxicity evaluated following OECD protocol 423. The results showed that the powder can develop groomed LF on materials such as leather, plastic, metal, glass, cardboard, and aluminum. LFs aged up to 30 days, left on glass slides were developed and classified as level-3. The images presented high quality, enabling correct donor identification as well as through an Automated Fingerprint Identification System (AFIS) search. EuBDC also showed useful results as secondary technique for fixed cyanoacrylate LFs, especially on a reflective, multicolored and non-flat surfaces. Additionally, the EuBDC was tested on ungroomed fingermarks, developed on a split depletion series of successive deposits and compared to a commercially available luminescent powder. Development also occurred on ungroomed aged fingermarks; as a secondary technique for cyanoacrylate fuming; and on transparent adhesive tape when used as a suspension for the latter. Considering that development powders are frequently handled by Papilloscopists and that this may pose a health risk, the acute toxicity and of EuBDC and histopathological analysis were evaluated. The tests showed no signs of toxicity. Therefore, the EuBDC was classified in category 5 in the Globally Harmonized System classification, the least toxic category, with an LD₅₀ >5000 mg/Kg. The set of results shows that EuBDC powder has the potential use as a fingermark developer, as well as being suitable for applications for non-toxic material.

In shooting incident reconstructions, forensic examiners usually deal with scenes involving short-range trajectories, typically ≤30 m. In situations such as this, a linear trajectory reconstruction model is appropriate. However, a forensic expert can also be asked to estimate a shooter's position by reconstructing a long-range trajectory where the bullet's path becomes arced as a result of gravity and the greater time in flight. In this study, the point mass model (PMM) was used, because it is accessible and considered sufficiently accurate. A computer program using PMM can perform long-range trajectory reconstructions starting from an impact point. The reconstruction results in an area where the shot is expected to be fired from, not a single location. This is caused by varying the input parameters of the PMM. The aim of this study is to assess the accuracy of the method and discuss the influence of the most relevant parameters. The model has been validated by comparing its performance with 20 handgun bullet trajectories that were determined using Doppler radar measurements over long ranges, i.e. from 500 m to 1800 m. Comparison between the area calculated using the model and the actual shooter position demonstrates the limits of these reconstructions, particularly at high incident angles. The differences between the reconstructed deflections and the deflections measured by the tracking radar are rather large. This phenomenon is caused by either measurement errors in the cross wind as a function of height or inaccuracy of the radar's deflection measurements.

Modern techniques can generate highly discriminatory DNA profiles from minuscule biological samples, providing valuable information in criminal investigations and court proceedings. However, trace and touch DNA samples, due to their nature, often have lower success rates than other biological materials, such as blood. Further, forensically aware criminals can utilize gloves and meticulously clean the crime scene to remove DNA traces of themselves from contacted surfaces. Air sampling offers a novel approach to the collection of human DNA that has the potential to bypass some of these issues. This study reports on the results of research into the prevalence and persistence of human DNA in the air. The ability to collect human DNA from the air was investigated with the use of an AirPrep Cub Sampler ACD220 in different spaces, with and without the presence of individuals for various durations of sample collection. Results of this study demonstrate that level of occupation and sampling duration each have an influence on quantity and quality of DNA recovered from the air whereas the effects of orientation and distance of participants from the collection device as well as sequence of occupation remain unclear and require further investigation.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0-339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm^-1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0-0.4 mM, medium = 0.4-14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.

As the court put forward higher requirements for quantitative evaluation and scientific standards of forensic evidence, how to objectively and scientifically express identification opinions has become a challenge for traditional forensic identification methods. Score-based likelihood ratios are mathematical methods for quantitative evaluation of forensic evidence. However, due to the subtle differences in inter-class barefootprints, there is no automatic barefootprints matching algorithm with high accuracy under large-scale dataset validation, and there are few studies related to deep learning barefootprint features for evidence evaluation in court. Therefore, score-based likelihood ratios for barefootprint evidence using deep learning features are proposed by this paper. Firstly, the largest barefootprint dataset (BFD) is constructed, which contains 54,118 barefootprint images from 3000 individuals. Then, an automatic barefootprint feature extraction and matching algorithm is proposed, which achieves a retrieval accuracy of 98.4% on BFD and an AUC of 0.989 for barefootprint validation. Next, Cosine distance, Euclidean distance and Manhattan distance are employed to measure the comparison scores between intra-class and inter-class barefootprints using deep learning features in four dimensions of 64, 128, 512 and 1024, respectively. The performance of proposed model is evaluated by comparing the $C_{\mathrm{llr}}$ values and the Tippett plot. Finally, simulated crime scene barefootprint samples are constructed to verify the practical application of the proposed method, which provide further support for the quantitative evaluation of barefootprint evidence in court.