Accurately identifying and differentiating the types of injuries in decomposed corpses is a major challenge in forensic identification. Forensic investigations involving decomposed cadavers pose challenges in determining the cause of death. Traditional methods often lack conclusive evidence. However, the implementation of advanced analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOF/MS), shows promise in overcoming these limitations, but the potential in this area remains limited. Therefore, this study aims to bridge this gap by exploring the potential of GC × GC-TOF/MS in the analysis of volatile organic compounds (VOCs) changes within decaying ante- and post-mortem injuries.The research emphasizes the forensic significance of VOCs changes in decomposed cadavers. We used GC × GC-TOF/MS analysis to identify the specific volatile compounds in putrefied corpse tissue samples from mice. The GC × GC-TOF/MS analysis results showed that under winter conditions, PC1 explained 57.16% of the variance, and PC2 explained 25.23% of the variance; while under summer conditions, PC1 explained 71.89% of the variance, and PC2 explained 24.49% of the variance. This demonstrates the potential of GC × GC-TOF/MS in identifying specific VOCs present in tissue samples that can serve as potential biomarkers for distinguishing between antemortem and postmortem injury. GC × GC-TOF/MS analysis revealed distinct VOC patterns in both conditions. Comprehensive use of GC × GC-TOF/MS analysis enhances accuracy in identifying and characterizing ante- and post-mortem injuries in decomposed cadavers. This study can significantly contribute to the field of forensic medicine and improve the accuracy of forensic investigations.

The forensic analysis of amide-based synthetic cannabinoids (SCs) in seized materials is routinely performed using gas chromatography-mass spectrometry (GC-MS); however, a major challenge associated with GC-MS is the thermolytic degradation of substances with sensitive functional groups. Herein, we report the comprehensive thermal degradation and ester transformation of amide-based SCs, such as AB-FUBINACA, AB-CHMINACA, and MAB-CHMINACA, during GC-MS analysis and their treatment with analyte protectants (APs). These SCs were found to undergo thermolytic degradation during GC-MS in the presence of non-alcohol solvents. Using methanol as an injection solvent resulted in the conversion of the amide group to an ester group, producing other SCs such as AMB-FUBINACA, MA-CHMINACA, and MDMB-CHMINACA. Degradant and ester product formation has been interpreted as the adsorption of target SCs on glass wool via hydrogen bonding interactions between the active silanol and amide groups of the SCs, followed by an addition and/or elimination process. The factors found to influence the thermal degradation and/or esterification of the amide functional group include residence time, activity of glass wool, and injection volume. This report presents the fragmentation patterns of all compounds that were produced by degradation and esterification. Using 0.5 % sorbitol (AP) in MeOH as an injection solvent resulted in complete protection and improvement of the chromatographic shape of the compounds. This method has been successfully confirmed in terms of sensitivity, linearity, accuracy, and precision for standard solutions and tablet extraction using 0.5 % sorbitol in MeOH. Using AP increased the sensitivity by ten times or more compared to the use of only MeOH. The limit of detection for all analytes was determined as 25 ng/mL, and the calibration curves were linear over the concentration range of 50-2000 ng/mL. The values of accuracy error were below 11 %, and precision was less than 13 %. The effects of phytochemicals of herbal products, tablet ingredients, and biological matrices on the degradation and/or esterification and APs performance have also been evaluated in this work.

Lysergic acid diethylamide (LSD) and two phenethylamine classes (NBOHs and NBOMes) are the main illicit drugs found in seized blotter papers. The preliminary identification of these substances is of great interest for forensic analysis. In this context, this work constitutes the inaugural demonstration of an efficient methodology for the selective detection of LSD, NBOHs, and NBOMes, utilizing a fully 3D-printed electrochemical double cell (3D-EDC). This novel 3D-EDC enables the use of two working electrodes and/or two supporting electrolytes (at different pHs) in the same detection system, with the possibility of shared or individual auxiliary and pseudo-reference electrodes. Thus, the selective voltammetric detection of these substances is proposed using two elegant strategies: (i) utilizing the same 3D-EDC platform with two working electrodes (boron-doped diamond (BDD) and 3D-printed graphite), and (ii) employing two pH levels (4.0 and 12.0) with 3D-printed graphite electrode. This comprehensive framework facilitates a fast, robust, and uncomplicated electrochemical analysis. Moreover, this configuration enables a rapid and sensitive detection of LSD, NBOHs, and NBOMes in seized samples, and can also provide quantitative analysis. The proposed method showed good stability of the electrochemical response with RSD <9 % for Ip and <5 % for Ep, evaluating all oxidation processes observed for studied analytes (n = 7) at two pH levels, using the same and different (n = 3) working electrodes. It demonstrates a broad linear range (20-100 and 20-70 μmol L-1) and a low LOD (1.0 μmol L-1) for quantification of a model molecule (LSD) at the two pHs studied. Hence, the 3D-EDC combined with voltammetric techniques using BDD and 3D-printed graphite electrodes on the same platform, or only with this last sensor at two pH values, provide a practical and robust avenue for preliminary identification of NBOHs, NBOMes, and LSD. This method embodies ease, swiftness, cost-efficiency, robustness, and selectivity as an on-site screening tool for forensic analysis.

OBJECTIVE: Forensic verification of cyanide (CN) poisoning by direct CN analysis in postmortem blood is challenging due to instability of CN in biological samples. CN metabolites, thiocyanate (SCN-) and 2-aminothiazoline-4-carboxylic acid (ATCA), have been proposed as more stable biomarkers, yet it is unclear if either is appropriate for this purpose. In this study, we evaluated the behavior of CN biomarkers in postmortem swine and postmortem blood to determine which serves as the best biomarker of CN exposure.
METHODS: CN, SCN-, and ATCA were measured in postmortem swine (N = 8) stored at 4 °C and postmortem blood stored at 25 °C (room temperature, RT) and 37 °C (typical human body temperature, HBT).
RESULTS: Following CN poisoning, the concentration of each CN biomarker increased well above the baseline. In postmortem swine, CN concentrations declined rapidly (t1/2 = 34.3 h) versus SCN- (t1/2 = 359 h, 15 days) and ATCA (t1/2 = 544 h, 23 days). CN instability in postmortem blood increased at RT (t1/2 = 10.7 h) and HBT (t1/2 = 6.6 h). SCN- and ATCA were more stable than CN at all storage conditions. In postmortem swine, the t1/2s of SCN- and ATCA were 15 and 23 days, respectively. While both the t1/2s of SCN- and ATCA were relatively lengthy, endogenous levels of SCN- were much more variable than ATCA.
CONCLUSIONS: While there are still questions to be answered, ATCA was the most adept forensic marker of CN poisoning (i.e., ATCA produced the longest half-life, the largest increase above baseline levels, and most stable background concentrations).

The present study deals with the development of a solvent-assisted dispersive solid phase extraction method for the extraction of HMX, RDX, and TNT from aqueous samples. Benzophenone and methanol were selected as explosives sorbent and dispersive solvent respectively. Extraction parameters like pH, extraction time, amount of sorbent, volume and type of the disperser solvent and centrifuge time were optimized. Dispersion of 0.5 mL dispersive solution (4% (w/v) benzophenone in methanol) was performed by injection into the 5 mL aqueous sample (pH=7) using a 1.0 mL syringe. After centrifuge, the extracted explosives were analyzed by high performance liquid chromatography with ultraviolet detection (HPLC-Uv). The results indicated that the linear ranges with the correlation coefficients of 0.99 ≤ R2 were 1.6-204.6 μg L-1, 1.4-213.7 μg L-1 and 1.3-225.9 μg L-1 for HMX, RDX and TNT respectively. The limit of detection and limit of quantification obtained for each explosive were: 0.3 μg L-1 and 0.8 μg L-1 for HMX, 0.3 μg L-1 and 0.9 μg L-1 for RDX and 0.2 μg L-1 and 0.7 μg L-1 for TNT. Finally, the practical applicability of the developed method was evaluated for the extraction of some organic explosives in water samples followed their determination by HPLC-Uv.

Latent fingerprints at crime scenes are frequently recovered using forensic gel-lifters, which can help to preserve the crime scene and to enhance visualisation of traces such as blood or paint. In addition to providing fingerprint ridge detail, additional chemical information can also be recovered from gel lifts that may prove pertinent to an investigation. However, while DNA and metal ions have been shown to be able to be detected in gel-lifted fingerprints, the determination of other types of chemical information such as the presence of drugs in gel-lifted prints has not been previously shown. This study demonstrates the application of an ambient ionisation method, sheath flow probe electrospray ionisation-mass spectrometry (sfPESI-MS), to the direct analysis of gel-lifted fingerprints. A model drug compound (zolpidem) is successfully detected from gel-lifted prints from three different surface types: glass, metal, and paper. The surface activity-based separation associated with probe electrospray approaches is shown to resolve zolpidem ions from background phthalate species, significantly enhancing the response obtained from the gel-lifter. A depletion series experiment shows that the drug residue can be detected with up to 100% efficiency after eight consecutive contacts; however, detection efficiency drops to 20% after 30 contacts. The developed approach has potential application to analysis of historical gel-lifters to obtain additional chemical information.

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

iPhone operating system (iOS) devices utilize binary cookies as a data storage tool, encoding user-specific information within an often-neglected element of smartphone analysis. This binary format contains details such as cookie flags, expiration, and creation dates, domain, and value of the cookie. These data are invaluable for forensic investigations. This study presents a comprehensive methodology to decode and extract valuable data from these files, enhancing the ability to recover user activity information from iOS devices. This paper provides an in-depth forensic investigation into the structure and function of iOS binary cookie files. Our proposed forensic technique includes a combination of reverse engineering and custom-built Python scripts to decode the binary structure. The results of our research demonstrate that these cookie files can reveal an array of important digital traces, including user preferences, visited websites, and timestamps of online activities. It concludes that the forensic analysis of iOS binary cookie files can be a tool for forensic investigators and cybersecurity professionals. In the rapidly evolving domain of digital forensics, this research contributes to our understanding of less-explored data sources within iOS devices and their potential value in investigative contexts.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

Papaver somniferum L. is cultivated for its edible seeds and for the production of alkaloids. A serious problem in seed trade and processing is the intentional mixing of excellent food-quality seeds with non-food-grade-quality seeds. Tracking the correct or illegitimate handling of seeds requires an efficient method for discrimination and individualization of poppy varieties. As in human and animal forensics, DNA variable regions containing short tandem repeats (STRs) located either in non-coding DNA or in gene sequences (EST-STRs) are preferred markers for discrimination between genotypes. Primers designed for 10 poppy EST-STR loci not analyzed so far were tested for their discriminatory ability on a set of 23 related P. somniferum L. genotypes. Thirty-three EST-STR alleles were identified together. Their polymorphic information content (PIC) values were in the range of 0.175-0.649. The PI value varied in the range of 0.140-0.669, and the cumulative PI was 1.2 × 10-5. PIsibs values varied between 0.436 and 0.820 and the cumulative value was lower (5.0 × 10-3). All analyzed genotypes were distinguished mutually, each with its own unique EST-STR profile. These newly developed EST-STR markers more effectively discriminated P. somniferum L. genotypes, even those genotypes whose DNA profiles were previously identical.