A method was developed for the determination of etomidate and its metabolite etomidate acid in hair and urine by high performance liquid chromatography-triple quadrupole tandem mass spectrometer (HPLC-MS/MS). The hair samples were ground and extracted by ultrasonic extraction with methanol. After protein was precipitated by acetonitrile, the urine samples were centrifuged at high speed and passed through 0.22 μm filter membrane, and then Agilent Eclipse Plus C18 RRHD (3.0 mm×150 mm×1.8 μm) column was used for analysis. Gradient elution was performed with 0.1% formic acid aqueous solution-acetonitrile as mobile phase at the flow rate of 0.5 mL/min. Electrospray ion source and multiple reaction monitoring (MRM) positive ion mode were selected. The results showed that the linear relationship of the two compounds exhibited good linearity in the concentration range of 0.5 to 50 ng/mL in urine and 0.025 to 2.5 ng/mg in hair, and the R² value was greater than 0.992 5. The extraction recoveries ranged from 91.0% to 107.5%, and the intra-day and inter-day precision RSD was 0.4% to 7.4%, while the intra-day and inter-day accuracy was 91.5% to 110.8%. In the actual cases, six hair samples and 4 urine samples were tested, and the test results showed that etomidate was detected in all six hair samples, and the metabolite etomidate acid was detected in two hair samples, but the concentration was much lower than the original. Etomidate and metabolite etomidate acid were detected in two urine samples, and the metabolite concentration was much higher than the original. In conclusion, this method can be used for rapid qualitative and quantitative analysis of etomidate and its metabolite in the hair and urine of etomidate users.

With the swift progress of artificial intelligence (AI), the field of forensic DNA examination is witnessing a technological transformation. AI has been integrated into multiple facets of forensic DNA analysis, encompassing intelligent DNA expert systems, AI-assisted optimization of examination procedures, innovative AI-assisted DNA statistics and analysis, rapid electrophoresis data analysis powered by AI, complex mixture sample analysis, and big data inference models. These advancements have significantly enhanced the precision and efficiency of forensic DNA testing. However, the integration of AI has also introduced challenges such as data privacy, model interpretability, algorithmic bias, and legal regulation. Addressing these issues necessitates close collaboration among forensic DNA experts, bioinformatics specialists, and AI professionals. Additionally, it requires the establishment of appropriate legal and regulatory frameworks to ensure that AI applications adhere to ethical standards and effectively support judicial fairness. This article provides an in-depth examination of the application of AI in forensic DNA analysis and the challenges it presents. It analyzes specific case studies to illustrate how AI contributes to the automation and intelligence of forensic DNA analysis, while also highlighting potential risks and challenges. The paper aims to offer guidance and references for the application of AI in the forensic DNA field.

Detecting abnormal behavior is crucial for maintaining public security, especially in densely populated critical areas. Traditional target detection algorithms often struggle to deliver satisfactory results under these conditions due to challenges like dense target distribution, significant scale variation, and complex backgrounds. YOLOv8 is one of the better perforing detection models effect among the object detection models. This study introduces a novel approach to improve detection accuracy by integrating advanced mechanisms into the YOLOv8 backbone network. Firstly, the coordinate attention (CA) mechanism is incorporated into the C2f module of the backbone network. This enhances the network’s focus on targets amidst complex backgrounds by emphasizing relevant features and suppressing noise. Secondly, the swin transformer model is integrated into the YOLOv8 backbone. The swin transformer facilitates greater information interaction across the feature map, effectively utilizing the background information and improving object detection accuracy under complex scenarios. The datasets used in the experiments are described, the evaluation indexes of P, R, AP and mAP are listed, and ablation experiments and comparative experiments are carried out. Experiments demonstrate the feasibility and effectiveness of these improvements. The enhanced network is compared with several mainstream networks, showing a significant improvement in average accuracy, reaching 95.1%. Compared to the basic network YOLOv8, the average precision has been improved by 2.4%, which proves the effectiveness of this method. In summary, the innovative integration of the CA mechanism and Swin Transformer model into the YOLOv8 backbone network addresses key challenges in detecting abnormal behavior in densely populated and complex environments. These enhancements lead to improved detection accuracy, making it a promising approach for public security applications.

With the popularity of Bitcoin and Ethereum based on blockchain technology, criminals have used various excuses such as “blockchain” and “virtual currency” to engage in illegal activities. Due to the characteristics of virtual currency such as decentralization, anonymity, global convertibility, convenient transactions, and irreversibility, it is widely used in criminal activities such as fraud, gambling, and money laundering. Detecting and combating virtual currency crimes faces difficulties and challenges such as complex and diverse activity scenarios, highly concealed behaviors, and difficulty in finding evidence. Therefore, the fight against virtual currency crimes is becoming increasingly serious, and necessary and effective measures need to be taken. This study explore the current state of virtual currency-related crimes and proposed strategies to overcome the challenges faced by law enforcement agencies in combating these crimes. Investigating virtual currency crimes requires the use of technologies in various fields, including address tag library construction technology, virtual currency tracking technology, unsupervised learning and artificial intelligence technology. These technologies can help law enforcement agencies collect and process virtual currency transaction data to trace the source and flow of funds for illegal transactions. They can also analyze large amounts of data to quickly identify suspicious transactions and entities, thereby improving the investigation efficiency of law enforcement agencies. This article focuses on the current status, difficulties and response strategies of combating virtual currency crimes. The technical process of investigation, evidence collection, tracking and survey analysis by law enforcement agencies after the occurrence of virtual currency cases is discussed in detail. In the technical process of combating virtual currency crimes, this article proposes and conducts in-depth research on key technologies and their applications, bringing new directions for detecting virtual currency crimes and providing strategic ideas for combating increasingly rampant new cyber-crimes.

From Sanger sequencing to high-throughput sequencing, the rapid development of sequencing technology has been providing better technical support for combating crime through forensic DNA analysis. In recent years, the third-generation sequencing technology, mainly based on nanopore sequencing technology, has been widely applied in life science research, in vitro diagnosis, public health, food safety and other fields. Nanopore sequencing technology with super-long reading and real-time sequencing has great potential in the field of forensic genetics. Many authorities and experts have already realized the great potential of nanopore sequencing applications for forensic purposes, although its application in forensic science is still in its infancy. There is little relevant research literature in the field of forensic science, and we should research and explore it further. In this article, the authors attempt to describe the basic principle and characteristics of nanopore sequencing technology, and share the updates of nanopore sequencing-based STR typing, MH typing, mtDNA, DNA methylation and RNA sequencing during the past several years. Meanwhile, non-human genetic material can provide medical examiners with special evidence and clues. The past decade has witnessed the enormous potential of nanopore sequencing technology in non-human forensic genetics. Especially in the areas of microorganisms, plant, and animal forensics, the application of nanopore sequencing to species identification can exert a huge implication, and provide the vital evidence and clues for the public security. In addition, nanopore sequencing has been used to detect viruses at the scene. In the field of forensic genetics, the nanopore sequencing with portability and real-time sequencing makes it most likely to sequence directly of biological samples at the crime scene. This development of the nanopore sequencing has opened up new possibilities by bringing “the laboratory into the field”. This draw the incomparable attraction to the practical application in public security. Moreover, several problems with nanopore sequencing in forensic genetics are discussed, including complex data analysis, high error rate, high sample quality requirements, and analytical methods, and there is a certain distance from the daily application of forensic genetics, which need in-depth research. Finally, we hope that this review can provide a reference for related research and applications, opening up ideas for relevant personnel.

Within the realm of law enforcement, the utilization of WeChat data has emerged as an indispensable investigation tool, extensively employed in crime investigations and clues tracking. This paper focuses on the information shared by WeChat users in their Moments, with particular attention to interactions between friends. A method for extracting and analyzing clues based on the WeChat Moments relationship network is proposed. Firstly, social connections between users and friends are extracted by analyzing interactions such as likes and comments in WeChat Moments. The WeChat Moments relationship network is then constructed using force-directed graph techniques, providing a visual representation of the relationships between users and their friends. Subsequently, in-depth analysis is conducted through graph clustering and centrality analysis methods. By identifying closely connected individuals, potential associated groups and social circles are revealed, offering key leads for subsequent investigative work. Lastly, focusing on these closely connected individuals, a thorough analysis of their chat records is performed using word cloud technology and the TextRank algorithm. By mining keywords and topics, a more comprehensive understanding of communication content is obtained, aiding in the accurate assessment of the activities and intentions of individuals involved in the case. Through application and validation in real cases, this method demonstrates the ability to rapidly construct the WeChat Moments relationship network, identify closely connected individuals, and perform targeted analysis of their chat records. The results of the experiments show significant achievements in improving the efficiency, accuracy, and depth of lead acquisition, providing robust support for law enforcement investigations. The proposed method, based on the WeChat Moments relationship network, offers new perspectives and technological means for law enforcement investigations. Future work may involve further optimizing algorithms and enhancing the capability to handle large-scale data to adapt to the complex and dynamic nature of criminal environments, thereby providing more effective support for investigative efforts.

A gas chromatography-tandem mass spectrometry (GC-MS/MS) based method was developed for the determination of etomidate and metomidate in e-cigarette liquid. The samples were extracted with methanol and separated on a HP-5MS column (30 m×0.25 mm×0.25 μm). Etomidate and metomidate were detected by GC-MS/MS under multiple reaction monitoring (MRM) mode, and quantified by an internal standard method. The mass spectrometry parameters, including parent ions, product ions, and collision energy, were optimized in this experiment. The results showed that etomidate and metomidate exhibited good linearity within the range of 0.1 to 10 μg/mL, with R² no lower than 0.999. The recovery rates of spiked matrix were between 98% and 107%. The relative standard deviations (RSDs) for intra-day and inter-day precision were less than 8.83% and 9.34%, respectively. The concentrations of etomidate and metomidate in seven e-cigarette liquid samples ranged from 4.96% to 18.48%. This method is simple, easy to operate, with high extraction recovery, good reproducibility, and high sensitivity. It can be used for the detection and analysis of etomidate and metomidate in e-cigarette liquids.

As one of the most commonly used reagents for amino acid detection, ninhydrin has a wide range of applications in forensic science and is a classic and effective method for displaying old fingerprints on permeable objects. However, the traditional ninhydrin display method still needs further improvement in the display effect of latent fingerprints on objects with complex background colors. Based on a review of relevant research results at home and abroad, a brief review was conducted on the composition of fingerprint substances, the mechanism of ninhydrin in fingerprint development, and the improvement of traditional ninhydrin development methods. A detailed review was also provided on the methods for enhancing the development effect since ninhydrin was applied in the field of fingerprint development. The innovation of the traditional ninhydrin solution method for enhancing visualization mainly manifests in three aspects: 1) optimization of ninhydrin solution method reagent formula, such as screening of the best solvent, exploring the optimal concentration, and discussing the influence of pH value on visualization effect; 2) The innovation of ninhydrin display methods, such as solid medium method, ninhydrin vacuum fumigation method, spray display method, etc., mainly solves the problems of carbonization interference and background ink interference in thermosensitive paper; 3) The fingerprint enhancement treatment using ninhydrin, mainly includes metal salt enhancement, rare earth-Ruhmann’s Purple violet coordination compound enhancement, and trypsin enhancement. The metal salt enhancement method and the rare earth-Ruhmann’s Purple coordination compound enhancement method have great research potential in the future development trend of latent fingerprints.

The forensic science is undergoing a paradigm shift from the traditional paradigm to the features LR paradigm and the similarity scores LR paradigm, in an era of parallel three paradigms. Given the advantages and development opportunities brought by Bayesian LR paradigm, paradigm shift has become a major trend in forensic science in the global. However, paradigm shifts have not yet been fully realized on a global scale, and the development of the forensic paradigm is imbalance in various forensic discipline and in different countries and regions. The main reasons that hinder the paradigm shift of forensic science include limitation of the technology methods of new paradigm, misconceptions of some personnel about the new paradigms, insufficient abilities related the new paradigms, and legal application issues. Except for DNA evidence identification, the application of new paradigms and paradigm shift in China are relatively lagging behind. This article proposes an implementation path for the paradigm shift of forensic science in China, including scholars and practitioners conducting scientific research on new paradigms, regulatory authorities making paradigm shift strategies and plans, forensic lab developing and confirming LR methods, developing LR verbal scales, collecting data, training examiner and taking proficiency tests, decision-makers receiving relevant education, and legislators adjusting relevant regulations.

This paper aims to validate the multiplex amplification system of 9 CpG sites reported in the literature, and explore its applicability in the Chinese population. The SNaPshot multiplex amplification system was validated in terms of accuracy, analysis of the initial amount of converted DNA templates, and detection of mixed samples. A total of 236 samples of five types of body fluids including saliva, semen, blood, vaginal secretion, and menstrual blood were selected. The SNaPshot multiplex amplification system was used to detect the methylation values of 9 CpG sites. The detection threshold of CpG sites was that the methylation value is greater than 0.1. Analyze the starting amount of transformed DNA templates in this system after converting DNA using sodium bisulfite (template amount ranging from 0.5 ng to 10 ng). DNA extracted from four body fluids, including saliva, semen, blood, and vaginal secretion, were mixed in the following ratios: 1∶1, 1∶5, 1∶10, and 1∶20. Finally, the detection data set of 232 samples of the five types of body fluids was used to optimize the existing body fluid source determination method. The train set (n=162) was used to construct a random forest model, and the test set (n=70) was used to predict the body fluid type and evaluated the predictive performance of the model. Furthermore, an external data set (n=40) was added to validate the prediction model. In saliva, semen, blood, vaginal secretion samples, and menstrual blood, the body fluid type was determined directly based on the specific sites of body fluids, and the accuracy rates of body fluid identification were 100%, 98%, 98%, 94% respectively. Due to the influence of the menstrual cycle, some sites were missing, and the average accuracy of menstrual blood identification was 21%. This system could effectively detect the amount of transformed DNA from 1 ng to 10 ng. Among the mixed sample, both body fluid sources were correctly identified in all 1∶1 mixed samples. The main components could be detected in the other mixed samples (ratio 1∶5, 1∶10 and 1∶20), while there was a significant difference in the detection of secondary components. A random forest model was built from 232 samples, and the accuracy of identifying the five fluid sources in both the test and validation sets was 100%. The above results show that the multiplex amplification system has high accuracy for the identification of saliva, semen, blood and vaginal secretion, and is suitable for the identification of trace samples, mixed samples (ratio 1∶1) or main components of other ratios. Compared with direct interpretation based on body fluid specific peaks, the new random forest model can better identify menstrual blood. In summary, the multiplex amplification system for tissue identification of five types of forensic body fluids based on DNA methylation is potential for good forensic application.

This paper explores the progress of artificial intelligence technology in the identification and reconstruction of crime scene elements. With the development of information technology, there are challenges faced by crime scene element identification and reconstruction. The paper discusses the application benefits of artificial intelligence, the relevant applications of artificial intelligence in forensic examination, and outlines the key steps of artificial intelligence in crime scene element identification and reconstruction, to explore the possibility of applying this method to crime scene element identification and reconstruction. Finally, the paper looks forward to the future development of artificial intelligence in forensic examination and suggests that it may play an important role in improving the intelligence level of crime scene examination and increasing the efficiency of case investigation. It is hoped that relevant research will provide a solution for the technical transformation of crime scene examiners and lay a foundation for the intelligent and digital development of forensic technology.

Dried blood spot (DBS) is a sampling technique in which a small volume of blood is collected on a specific type of filter paper. Since the 1960s, DBS has been primarily used for the newborn screening of metabolic diseases. With the development of highly sensitive analytical instruments, the dried blood spot technique allows for accurate quantification of ethanol, stimulants, drugs of abuse and heavy metal elements in biological samples. Due to its obvious advantages, the application of DBS in forensic toxicology has witnessed significant growth in the past decade, which is highlighted and presented in this article. Some challenges and suggestions of dried blood spot applications were summarized for the further research as well.

Fingerprint, one of the most reliable and valuable evidence in the crime scenes, has long been recognized as a powerful tool for personal identification and worldwide law-enforcing departments to fight against relevant crimes. For many years, in practice, fingerprint analysis has been developed based on the latent fingerprint visualization, primarily. However, most of these fingerprints in crime scenes have been ambiguous, deformed or fragmentary, which contributed to the difficulty in fingerprint analysis. So, recently, some researchers have gradually paid increasing attention on the fingerprint age for fingerprint analysis, though their researches were almost taken placed in the lab. To improve the accuracy and reliability of fingerprint age analysis, and effectively promoting the application of fingerprints age in practice, in this article, according to these researchers’ reports, the morphological characteristics of fingerprints related to the fingerprint age, including two-dimensional (2D) morphological characteristics (e.g. ridge widths and color contrast between ridges and furrows) and three-dimensional (3D) morphological characteristics (e.g. ridge heights), were reviewed, respectively. Furthermore, fingerprint residues which are transferred onto the object surfaces when fingertips touch object surfaces, reveal a wealthy of information, especially fingerprint age. Therefore, the changes of fingerprint residues involving electrical effect, the optical characteristics, as well as the change of compositions such as squareness, wax esters and fatty acids, were also comprehensively summarized in this review. Particularly, the future research directions and prospects were discussed about the methods and the reagents of latent fingerprint development, the equipment and the technologies of fingerprint detection, the composition and the degradation rates of fingerprint residues, and the influencing factors model of age determination of fingerprint.

With the advancement of paperless offices, handwritten electronic signatures have become widely adopted across various sectors, including banking, government affairs, and commerce. These signatures refer to the textual impressions created by fingers or stylus pens on electronic screens, such as smartphones and digital tablets. Concurrently, dynamic attributes like writing duration, stroke sequence, and pen pressure are captured and stored electronically on computers. From an implementation perspective, handwritten electronic signatures fall within the broader category of electronic signatures. However, they differ from digital signatures in terms of implementation technology, presentation format, verification methods, and technological requirements. Given the distinct verification methodologies, handwritten electronic signatures should not be subject to the authentication framework outlined in The Electronic Signature Law. Instead, their unique handwritten characteristics should be analyzed from a morphological angle. Nevertheless, shifts in writing devices and mediums have diminished certain static characteristics, often resulting in imperfections in the identification process. This complexity is further compounded by difficulties in leveraging dynamic features for identification, challenges in distinguishing copied and pasted handwriting, and a scarcity of experienced authenticators. Consequently, during the identification process, it is crucial to: collect comparable original specimens, specify the writing devices used, gather samples from identical or similar sources as the specimens, and utilize identification expertise to identify stable traits with individual variations. By adopting a comprehensive quantitative analysis approach and corroborating with electronic data, the accuracy of identification conclusions can be significantly enhanced.

This paper aims to establish a solid-phase support liquid-liquid extraction-high performance liquid chromatography/triple quadrupole tandem mass spectrometry (SLE-HPLC-MS/MS) method for the detection of scopolamine, anisodamine and atropine in blood and urine. The effects of protein precipitated method, solid phase extraction method and solid-phase supported liquid-liquid extraction method on the extraction of drugs from blood and urine samples were investigated. Scopolamine, anisodamine and atropine, in blood and urine samples were analyzed by high performance liquid chromatography triple quadrupole tandem mass spectrometry. The results showed that the recovery rate of solid-phase supported liquid-liquid extraction was the highest. The linear relationship between the concentration of scopolamine, anisodamine and atropine in blood and urine and the peak area (r>0.999 2) was good in the range of 0.1－100 ng/mL (blood) and 0.5－100 ng/mL (urine). The minimum detection limit of scopolamine, anisodamine and atropine in blood was 0.01 ng/mL, and the quantitative limit was 0.1ng/mL; The minimum detection limit of scopolamine, anisodamine and atropine in urine was 0.05 ng/mL, and the quantification limit was 0.5 ng/mL. The solid-phase supported liquid-liquid extraction liquid chromatography/tandem mass spectrometry method is characterized by simple operation, less solvent usage and high recovery rate. It is suitable in the detection of scopolamine, anisodamine and atropine in blood and urine for three types of drug poisoning cases (incidents).

Diphenidol is a non-phenothiazine anti-vertigo and anti-emetic over-the-counter drug, an analogue of trihexyphenidyl hydrochloride, and usually in the form of its hydrochloride, which was first approved in the USA in 1967. Diphenidol can improve the blood flow of cerebral vertebral artery, reduce the vertigo stimulation of vestibular nerve, inhibit the labyrinth function of inner ear, and block the vomiting center or the medullaoblongata emetic chemosensory area. Then, this drug has good anti-vertigo effect, less adverse reactions, high safety and so on, so it has been widely used in clinical practice, especially employed as an anti-emetic agent in the treatment of vomiting and vertigo associated with surgery, radiotherapy, chemotherapy, Meniere’s disease, and other labyrinthine disorders. Diphenidol is generally considered a relatively safe drug, which is inexpensive and easy to purchase. However, ingesting large doses intentionally or accidentally can cause serious toxic effects. The main symptoms of diphenidol poisoning include dry mouth, irritability, hallucinations, headache, euphoria and temporary hypotension. In severe cases, respiratory failure may result from respiratory depression, hypotensive shock or arrhythmia. In recent years, there have been a number of suicides and accidental poisonings related to diphenidol in China, including preschool children being poisoned by ingestion and adults attempting suicide. In forensic practice, diphenidol poisoning can be difficult to diagnose. Additionally, clinical symptoms and signs of diphenidol poisoning vary and are easily confused with epilepsy, tetanus rabies, hysteria or rodenticide poisoning. In addition, if there are no tablets or bottles left on the scene, a definitive diagnosis of diphenidol poisoning may be overlooked by the forensic doctor. Therefore, the physicochemical properties, pharmacological and toxicological effects, metabolic pathways and products in vivo, human pharmacokinetics, zoological experiments, analysis and detection techniques and other aspects of diphenidol were described in this paper, in order to provide some theoretical references for related cases.

Postmortem interval (PMI) refers to the interval between the discovery or examination of the body and the occurrence of death. Estimation of postmortem interval is one of the important research contents in forensic pathology，and it has always been the focus and hot spot of research work. Different techniques are used to evaluate and analyze the changes in the human body after death to estimate postmortem interval. The traditional methods of estimation of postmortem interval are based on postmortem phenomena such as algor mortis, rigor mortis, livor mortis, etc. These methods rely on the subjective experience of forensic pathologists, and the estimated time of death is a relatively wide range, and the estimated result is susceptible to subjective judgment. In recent years, postmortem computed tomography (PMCT) has become increasingly influential in the field of forensic pathology. Postmortem computed tomography is a non-invasive, rapid, and objective auxiliary means of autopsy, which can significantly improve the quality and efficiency of autopsy and can find imaging features that may not be observed in traditional autopsy. After the death of the human body, the body will undergo a series of postmortem changes according to a certain time law. A series of characteristic imaging changes in cadaver organs and tissues with the passage of time of death can be observed through the application of postmortem computed tomography, and these imaging changes can be quantitatively and objectively described, The potential ideal imaging indicators can be used to estimate postmortem interval and the correlation between these indicators and the postmortem interval can be analyzed. This paper systematically reviewed the research on the correlation between postmortem imaging features of organs and tissues (such as brain, heart, aorta, lung, liver, etc.) after postmortem computed tomography of various parts of the cadaver (head, chest, abdomen) and the estimation of postmortem interval, in order to provide new ideas for the study of the estimation of postmortem interval and further expand the application of virtual anatomy technology in the field of forensic pathology.

In this article, the Raman spectrum inspection performance of six drug seizures at three different excitation wavelengths (532, 785, and 1 064 nm) is compared using Raman spectroscopy. Three to five distinctive scattering peaks in the Raman fingerprint area (100-1 800 cm^-1), with greater scattering intensity and no interference from typical adulterants, were chosen as the bass for the qualitative matching of the drug seizures when compared to the Raman spectra of the drug standards. It was discovered that in order to perform a Raman investigation of drug seizures, appropriate excitation wavelengths must be chosen based on the kind of drug involved. Additionally, if the drug seizures contain adulterated substances, the distinctive peaks of the pharmaceuticals that escape adulteration should be chosen for comparison. For seized heroin (HER), caffeine (CAF) and 3,4-methylenedioxymethamphetamine (MDMA), the near-infrared (NIR) excitation wavelengths of 785 nm and 1 064 nm are better than that of 532 nm for the Raman test, in which the 1 064 nm significantly reduces the background fluorescence of the HER seizure; for seized methamphetamine (MAMP) and ketamine (KET), the effect of the change of excitation wavelength was small, and the Raman response was good at three different excitation wavelengths, and the relative intensities of the Raman peaks in the fingerprint region and the functional group region (2 500 to 3 200 cm^-1) showed regular changes; for the Raman analysis of Magu, the excitation wavelength of 785 nm was superior, with a higher spatial resolution, which could observe more characteristic peak information while avoiding the fluorescence effect. This research work is based on Raman spectroscopy, which will provide a strong reference and basis for drug enforcement.

Currently, smartphone screenshot images, as a swift and convenient tool for capturing and sharing information, have found widespread applications in daily life and professional work. However, simultaneously, the associated security and privacy concerns regarding these images have become increasingly salient. Due to the significant differences in their generation mechanisms between smartphone screenshots and captured images, this fact poses challenges for screenshot image traceability and forensics. In the current research and practical applications, technical research on tracing the sources of screenshot images remains in its nascent stage, with accurate tracing and identifying the source devices of specific images emerging as a pivotal research topic in the field of digital image forensics. This study focuses on developing a recognition methodology and system based on metadata features. By collecting screenshot samples from 52 smartphones of prevalent mobile phone brands in the market, metadata is extracted from the sample images, and a metadata feature database is subsequently established. The source device of the image is matched and identified by comparing multi-dimensional features, including model, device manufacturer, profile creator, rotation angle, file type, image width, and image height. Experimental results demonstrate that the proposed method exhibits high accuracy and reliability in identifying the origins of images for traceability purposes, thereby offering a novel technical approach for the field of digital forensics.

Physical evidence is the lifeline of investigation, and scientific and effective examination and identification of physical evidence can provide crucial support in reconstructing cases or events. In the context of fire scene evidence, the focus revolves around the nature of the fire and the analysis and determination of the fire’s causes. Particularly in the determination of the fire’s causes, identifying the ignition source, the fuel, and the conditions that lead to ignition are the key factors in the final analysis of the fire’s origin. During the investigation of a complex fire incident in a production workshop, difficulties were encountered in identifying the fuel and the ignition conditions. Through the application of simultaneous thermal analysis techniques to analyze the thermal properties of relevant physical evidence, parameters such as the thermal decomposition initiation temperature were determined. Upon identifying the point of origin, the heat of ignition was determined through the melting point temperature, and the material first ignited was identified through the pyrolysis temperature. Even though the pyrolysis temperature differs from the ignition temperature, the majority of combustion occurs in the gaseous phase, and combustible gas forms the basis of the combustion process. For polymeric materials, the generation of combustible gas must occur through pyrolysis, making pyrolysis a prerequisite for ignition. Only when the combustible material undergoes pyrolysis and produces a sufficient concentration of combustible gas can combustion potentially occur. Materials were collected from the point of origin in this fire investigation, and using thermal analysis techniques, the initial pyrolysis temperature was theoretically used to reasonably infer and identify the material first ignited, offering crucial reference for the analysis and identification of similar fire incidents.

The likelihood ratio paradigm of facial similarity score is the theory and method for interpreting the evidential significance of score finding from facial comparison. Facial similarity score likelihood ratio is the latest method of Bayesian likelihood ratio paradigm for forensic science. The likelihood ratio (LR) of facial similarity score is the ratio of the occurring probability of the facial score finding quantitatively assigned based on the probability distribution data of facial scores, under a pair of conflicting propositions that usually represent the claims of the prosecution and defense parties. The propositions typically deal with the question of whether a facial image with unknown identity collected at a crime scene comes from a suspect with known identity. The face score LR expresses the relative support direction and strength of the face score finding for the propositions of the prosecution and defense parties, providing quantitative evidence value for decision-makers to determine the disputed fact of the face source. The decision maker determines the fact of the facial source proposition based on the facial score LR opinion, or the posterior probability of the facial source proposition derived from the LR and the prior odds through Bayesian law, combined with other evidence, to exclude reasonable doubt. The likelihood ratio paradigm of facial similarity score is completely different from the traditional paradigm we are accustomed to in terms of scientific logic, opinion formation, expression, understanding, and reasoning applications. It also differs from the widely used LR paradigm of DNA feature findings, which poses new requirements and challenges for forensic examiner and decision-makers.

Footprint features, as one of the biological features of the human body, play an important role in the field of personal identification. At present, most research on footprint recognition focuses on footprint images as experimental data, using deep learning algorithms as the foundation and relying on auxiliary algorithms to complete high-precision footprint recognition tasks. However, there is a problem with models built on footprint images. Due to the similarity of footprints of different people, as the number of samples increases, the differences between the features of footprints of different people will continue to decrease, leading to an increasing false detection rate of the model. In order to reduce the interference of similarity between footprints on model recognition ability, this paper takes dynamic footprints as the research object and proposes a dynamic footprint retrieval method based on multi-class feature fusion. The proposed method uses a spatio-temporal fusion module to integrate the spatio-temporal information of footprints, so that the footprint recognition method is not limited to the apparent information of footprints. Firstly, the convolutional neural network is used to extract the frame level features of dynamic footsteps, and then the feature fusion module calculates the complete apparent features of the fused dynamic footprints through a trainable weight matrix and frame level features. Secondly, the temporal aggregation branch of the spatio-temporal feature fusion module is used to extract long-term temporal features within frame level features, and then the long-term temporal features are fused with frame level features through orthogonal fusion calculation method to form spatio-temporal features. Finally, the visual features and spatio-temporal features are fused for dynamic footprint retrieval. A comparative experiment is conducted on a dynamic footprint dataset of 200 people with existing deep learning algorithms, and the experimental results shows that this method achieved better performance, with Rank1 and mAP being 85.39% and 55.28%, respectively.

The means and forms of drug transmission are becoming increasingly diverse, posing significant challenges to public security investigations, laws and regulations supervision, and identification technology. In terms of identification technology, the detection of isomers, which can easily lead to misjudgments, remains particularly challenging. The comprehensive analysis of multidimensional detection methods has emerged as a trend in addressing the identification and regulation of new psychoactive substances. Currently, a notable synthetic cathinone called 2-dimethylamino- 1-[3,4-(methylenedioxy)phenyl]-1-pentanone(N,N-dimethylpentylone) warrants attention. This paper aims to develop an analytical method for identifying N,N-dimethylpentylone as a new synthetic cathinone. The processed unknown samples are analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR) including ¹H-NMR, ¹³C-NMR, H-H COSY, HSQC, HMBC, and Fourier transform infrared spectroscopy (FTIR). GC-MS analysis results show that the qualitative ion fragments are m/z 100.1, m/z 58.1, m/z 121.1, m/z 149.0, with a retention time at 11.888 minutes. LC/MS analysis results show that the parent ion is m/z 250.05, and the product ions are m/z 100.10, m/z 205.10, and m/z 135.10, with a retention time at 9.772 minutes. FTIR analysis results show that there area carbonyl absorption peak at 1 676 cm^-1, and a benzene ring vibration absorption peak at 1 506 and 1 441cm^-1. One-dimensional nuclear magnetic spectrum results show 9 hydrogen signals with different chemical shifts and 14 carbon signals with different chemical shifts in the unknown compound. Two-dimensional NMR analysis results show that the unknown compound conforms to the structure of N,N-dimethylpentylone. In summary, N,N-dimethylphenylone components are detected in the unknown sample.

With the rapid development of the Internet of Things and artificial intelligence, people’s dependence on data in their work and life is increasing day by day, making efficient and secure storage of data increasingly important. As an effective solution for organizing multiple disk storage, RAID (redundant array of independent disks) has been widely used in recent years and is also a data storage method that public security organs pay great attention to when extracting data during the process of handling cases. However, in the process of investigation and evidence collection, investigators often need to restore the content and structure of RAID data damaged by suspects, and correctly restructuring RAID disk arrays has become an important prerequisite and necessary link for involved website reconstruction analysis and forensics. This article shared a case of illegal transaction website reconstruction based on RAID recombination, detailed the process of RAID restructuring, explained the simulation restoration method for restoring the original server environment after successful RAID restructuring, and provided the operation process for connecting the website and database in a virtual machine environment, and hopes to provide useful reference for electronic data forensics of similar cases.

Kinship analysis of grandparents and grandchildren is an important content in the field of forensic identification, which includes three subtypes: double grandparents and biological mother test (DGBM), double grandparents and alleged mother test (DGAM), and double grandparents test (DG). Until now, no systematic formula has been reported in grandparent index (GI) calculation of grandparent identification in the presence of mutation, apart from few simple calculation methods of partial gene combination in DGBM. In this study, we first created a simplified representation model of grandfather and grandmother in 5 subtypes, in the form of a set of genotypes. The general probability formula of mutation of parent genotype into child genotype (P_(DG→Z)) was derived in 12 subtypes. Furthermore, the general formulas of GI under three conditions were derived: GI of double grandparents and biological mother (GI_DGBM), GI of double grandparents and alleged mother (GI_DGAM) and GI of double grandparents (GI_DG). The detailed formulas were further refined according to genotypes of double grandparents, mother and grandchild. In addition, the percentages of mutations of 5 genetic types in the group of unrelated individuals on 30 STR loci of the EX30 kit were calculated. Finally, models of simulated kinship groups and unrelated individual groups were established respectively. The geometric mean (Π) and 95% confidence interval (CI) of CGI, the percentages of cases where CGI<10 000 in kinship groups, and the percentages of cases where CGI>0.000 1 in unrelated individual groups were calculated in the same 30 STR loci. In this study, the novel formulas can be used to calculate all kinds of GI in the presence of mutation under three conditions. The genotype combinations in all mutation cases were reviewed. Among them, the occurrence rate of both paternal and maternal mutation groups was 13.31%, which could not be ignored. In EX30 kit, the formulas can achieve firm conclusions in DGAM cases for both the kinship group and unrelated individual group; in DGBM cases identifications, all cases in the kinship group and 99.89% cases in the unrelated individual group could be correctly identified; and more genetic markers should be added for DG cases. The geometric mean of CGI in kinship groups sorted by value were as following: Π_DGAM>Π_DGOM>Π_DG, and the rule in unrelated individual groups were as following: Π_DGAM<Π_DGOM<Π_DG. The results of this study show that the here-derived GI formulas could be applied to kinship analysis of grandparents and grandchildren in the presence of mutation under three conditions, which is of high application potential.

With the intensive integration of deep learning and computer vision, a series of advanced technologies such as facial recognition, image (video) generation, and image classification, have made rapid progress. However, deep learning models are considered “black box models” due to their difficulty in explaining internal processes and predicting results, which poses a serious challenge to the interpretability of image evidence in the field of forensic science. Based on this, this review outlines an overview of interpretability issues based on deep learning. Emphasis was placed on the theoretical and methodological research on the interpretability of facial features based on deep learning both domestically and internationally, such as saliency maps method, perturbation-based method, and score/statistics-based method. Their applications in facial recognition and other related fields, especially in the field of forensic science portraits, were summarized. This review proposes the problems of facial feature interpretability methods based on deep learning models, and looks forward to the future development direction of facial feature interpretability based on deep learning.

The subjective likelihood ratio paradigm of pattern features is the theory and method for the interpretation of evidence significance of feature findings of pattern evidence. Subjective likelihood ratio (LR) of pattern features is the probability ratio of occurrence of pattern features assigned under the two opposing propositions representing the prosecution and defense respectively, based on expert knowledge or the combination of expert knowledge and data. The proposition hierarchy for evaluating the subjective LR of pattern features includes source level and active level. Subjective LR of pattern features expresses the relative support direction and intensity of the pattern features findings for the propositions of the prosecution and defense, providing qualitative evidence value for decision makers to determine disputed factual propositions. Decision makers will determine the propositions facts based on subjective LR opinions of pattern features, or based on the posterior probabilities of the propositions derived from LR through Bayes’ theorem, combined with other evidence in the case, in the way to exclude reasonable doubts. The subjective LR paradigm of pattern features differs significantly from the traditional paradigm we are accustomed to in terms of scientific logic and the formation, expression, understanding, and reasoning application of opinions, and it also differs from the objective LR paradigm of DNA feature. This presents new requirements and challenges for forensic examiner and decision-makers.

In the monitoring of the crime scene, recording can comprehensively record all kinds of sounds at the scene, including verbal conversations, abnormal sounds and other sounds that may be related to the case. The recording evidence can play a role in restoring the incident and revealing the facts of the crime. This article takes the audio recordings from the central scene of a shooting incident as the main analysis object. It introduces the method of forensic speaker recognition into the analysis of abnormal verbal cries and air gunshots related to the case. The audio-acoustic-phonetics analysis method is used to examine the cries and gunshots. A total of 142 shouts and 2-suspected shooting sounds appeared in the on-site recording of the case. Through inspection, analysis and confirmation, the shouts were all from the victim, and the two-suspected shooting sounds were air gun shots. Afterwards, combined with the on-site investigation and post-mortem examination results, the incident process was reconstructed and the sequence of events was reproduced, and the time of the incident and the license plate of the suspect’s vehicle were accurately inferred, which provided scientific basis and important clues for finding out the nature of the case, determining the direction of investigation and locking the suspect.

In order to further improve the accuracy of speaker recognition and avoid the complicated process of manual feature extraction required by traditional speaker recognition methods, this paper proposes an end-to-end speaker recognition method based on CBAM attention mechanism and deep neural network. CBAM, a lightweight general module, is introduced into the deep neural network structure and seamlessly integrated into the network architecture. After it is added into the first layer of deep neural network convolution in this paper, the features of speech signals first pass through the CBAM channel attention module to strengthen the model’s attention to the channel dimension of speech features. Then CBAM spatial attention module is used to improve the model’s attention to the spatial dimension of speech features, further improve the model’s sensitivity to important feature information, and use the end-to-end loss function to train the whole model as a whole. At the same time, an embedded court speaker recognition method based on generalized end-to-end loss function training is proposed, and the likelihood ratio is obtained by using the embedded cosine similarity score trained by the improved network model, so as to intuitively and accurately judge whether it is the same speaker, thus providing intuitive and powerful evidence for the court. Finally, taking deep neural network BILSTM and GRU as examples, the mainstream data set CN-Celeb was used to train the model to ensure that the model can achieve better combat effects in a complex and rich voice environment. Zhaishell, a subset of Zhvoice, and the audio of actual combat cases collected by ourselves were used for combat test, to ensure that the model in this paper has a good recognition effect for both Mandarin and dialect. The results show that the method proposed in this paper can effectively improve the recognition accuracy, quickly construct the model and improve the generalization ability.

To establish a high performance liquid chromatography-tandem hybrid triple-quadrupole linear ion trap mass-spectrometry method for qualitative and quantitative determination of etomidate in blood and hair. Deuterium cocaine was selected as the internal standard, and blood sample was extracted by acetonitrile (V/V, 1/6), then centrifuged at high speed; hair sample (about 20 mg) was ground by a ball mill, then extracted by methanol and passed through the organic membrane. Using 0.1% (V/V) formic acid aqueous solution and 0.1% (V/V) formic acid acetonitrile as mobile phase, the analyte was separated and analyzed by an ACQUITY UPLC^®C18 (2.1 mm×100 mm×1.7 μm) column. Electrospray positive multiple reaction monitoring/information-dependent acquisition / enhanced product ion scanning (MRM-IDA-EPI) and secondary library retrieval were used for analysis. The results showed that etomidate in blood had a good linear relationship in the range of 1.0 to 100.0 ng/mL (r>0.995). The linear relationship of etomidate in hair was good in the range of 0.05 to 5.0 ng/mg (r>0.995). The detection limits (S/N≥3) for blood and hair were 0.2 ng/mL and 0.002 ng/mg, and the quantitative limits (S/N≥10) were 0.5 ng/mL and 0.005 ng/mg respectively. The recoveries of 1.0, 10.0, 100.0 ng/mL in spiked blood were 97.1% to 103.4%, and 0.5, 2.5, 5.0 ng/mg in spiked hair were 84.0% to 99.8%. The relative standard deviation was less than 15%. This method can be applied to the qualitative and quantitative analysis of etomidate in blood and hair in the juridical practice.

The aim of this study is to provide guidance and reference for further research in related fields by analyzing in-depth the current research status, emerging themes and future development trend of forensic entomology. In order to achieve this goal, the authors systematically searched the literature related to forensic entomology from CNKI (China National Knowledge Infrastructure), Wanfang database, and Pubmed database during the period from 2003 to 2023. Using CiteSpace 6.2.R4 Advanced software, the authors visualized and analyzed these literatures in terms of annual publication trends, authors, countries, institutions, and keywords. After rigorous screening, a total of 499 domestic and 2 137 foreign literatures, totaling 2 636 articles, were included in this study. The analysis results showed that although China has made some progress in the field of forensic entomology, there is still much room for improvement in international cooperation and exchange. Meanwhile, the inference of the time of death (PMI) of corpses is a research hotspot of common concern at home and abroad, but there are differences in research focus among countries. We conclude that China must strengthen empirical research and field investigation, focus on the combination of theory and practice, improve the application value and influence of research, and enhance communication and cooperation between scholars internationally to jointly promote the development of forensic entomology.

The identification of the characteristics of shooting bullet trace image is the main content of gunshot trace inspection, and also one of the challenges. This article introduces an advanced automatic annotation method for shooting bullet trace features based on the High-resolution networks (HRNet) framework, which can achieve automatic labeling of the land-engraved trace area, groove-engraved trace area, and slippage trace area. A database of 5 985 images containing seven different sizes of shooting bullet traces extracted by BalScan (3D trace image scanning system) was constructed and divided into training, validation, and testing datasets at a ratio of 7:1.5:1.5. The training dataset was manually annotated to identify the land-engraved trace area, groove-engraved trace area, and slippage trace area, which were used to train the high-resolution network model. Then, the unlabeled testing dataset was input into the trained model for automatic annotation of the feature areas. Finally, the annotation results were manually reviewed and the accuracy was recorded. The results showed that the proposed method achieved an average accuracy of 94.1% in the automatic annotation task, demonstrating its effectiveness. This annotation algorithm for shooting bullet trace images without manual annotation can significantly reduce the workload of inspectors and provide a feasible new approach to improve the efficiency of firearm trace inspection.

The distribution and degradation of the synthetic cannabinoid ADB-BUTINACA of alkyl formyl indazoles in rats were explored in this paper. Through previous animal studies, it was found that the metabolic markers of ADB- BUTINACA in rats were dealkylation metabolic markers: N-(1-aminoformyl-2,2-dimethylpropyl)-indazole-3-formamide (hereinafter referred to as A1) and hydrolytic metabolic marker: 3,3-dimethyl-2- [1-Butyl-1H-indazole-3-formamido] butyric acid (hereinafter referred to as A2). An intoxication model of rat administered with ADB-BUTINACA by gavage established by simulating oral administration of cannabis products, and the concentration of ADB-BUTINACA and metabolic markers A1 and A2 in tissues, blood, urine and feces were detected by high performance liquid chromatography-triple quadrupole tandem mass spectrometry (HPLC-MS/MS), and the distribution and degradation of ADB-BUTINACA and metabolic markers in rats were monitored. The results showed that ADB-BUTINACA was rapidly metabolized and distributed in various tissues and blood after entering the rat body, and showed different regularity of distribution and degradation within 12 hours. In the monitoring of urine and feces collected within 1 to 10 days, ADB-BUTINACA and its metabolic markers were detected only in a small amount of urine, while they were all detected in feces collected within 24 to 72 hours. The rat intoxication model established in this study monitors the distribution and degradation of ADB-BUTINACA and metabolic markers in rats, providing scientific basis and data reference for the test of ADB-BUTINACA in vivo.

This study uses the Web of Science Core Collection as its search dataset, employing the visualization tools VOSviewer 1.6.20 and CiteSpace 6.2R6 to analyze 509 publications related to the statistical quantification of trace evidence, spanning 57 countries, 976 institutions, and 267 journals. The study examines key literature nodes from four perspectives: publication volume, publication outlets, keyword co-occurrence, and keyword clustering, providing researchers with a comprehensive and intuitive understanding of the research trends and emerging hotspots in the field. The findings reveal that over the past decades, the volume of research on statistical methods for trace evidence has shown fluctuating growth. European countries have shown significant collaboration on this topic, forming a closely-knit regional cooperation network, with the Netherlands Forensics Institute being the most prolific institution. Fingerprints are a crucial subject of statistical quantification of trace evidence, with statistical and quantitative methods primarily focusing on a series of methods based on Bayes’ theorem, such as likelihood ratios and Bayesian networks. A trend in research hotspots is observed, transitioning from clusters of subjective quantification methods (such as subjective likelihood ratios) to objective ones (such as feature-based and score-based likelihood ratios). Current challenges in the statistical quantification of trace evidence include difficulties in interpreting high-dimensional data, model error rates, and model parameter estimation. The study suggests improvements such as establishing quality assessment metrics for high-dimensional evidence, developing models with dynamically adjustable error tolerance, employing multiple validation and evaluation strategies, and fostering expert consensus on new paradigms in trace evidence.

Injuries caused by animals are relatively common in forensic examinations, but those resulting from bird pecking are less frequent. In particular, eyeball injuries caused by bird pecking are even rarer. Such injuries can easily be mistaken for human-inflicted injuries, leading to misunderstandings among the deceased’s family members and even forensic personnel. This can cause the case to reach an impasse or even trigger petitions. This article presents a case of ocular perforating defects caused by bird pecking, aiming to introduce the characteristics of animal injuries and the specific features of injuries caused by bird pecking, as well as how to analyze and evaluate such injuries, thereby providing a basis for determining the nature of the case.

依托咪酯是一种非巴比妥类静脉麻醉药，经静脉入体后与血浆白蛋白结合，迅速分布于脑和心脏组织，具有起效快、维持时间短、循环稳定等特点，目前主要应用于临床麻醉诱导或短期手术麻醉。一些滥用者利用其麻醉特性将其制成烟粉、烟油等添加至电子烟中使用，危害范围广，隐匿性强，长时间大量吸食依托咪酯会对内分泌系统、肝脏和神经系统等造成损害，对神经细胞的毒性尤为突出，严重者可能出现呼吸暂停甚至死亡。目前依托咪酯的滥用已在全球凸显，在我国也已经形成一定滥用趋势。由于依托咪酯易于获得，吸食人群中青少年占绝大多数，严重危害其身心健康，国家药品监督管理局、公安部、国家卫生健康委员会联合发布的《关于调整麻醉药品和精神药品清单的公告》中，将依托咪酯（在中国境内批准上市的含依托咪酯的药品制剂除外）列入第二类精神药品清单，但吸食滥用现象仍然多发。目前对于依托咪酯的研究主要集中在临床用药、不良反
应及检测方法的研究方面，对于毒物代谢动力学、毒理学机制及滥用或成瘾机制研究不足。本文就依托咪酯的药物代谢动力学、麻醉机制、毒性和滥用现状进行回顾性分析，并对气相色谱－质谱联用法、高效液相色谱－质谱联用法和定量核磁共振氢谱检测法等相关检测方法进行了总结，以期为依托咪酯的深入研究和法医学鉴定提供参考。

Rapid non-destructive testing of the formation time of blood stains at different dilution ratios under common temperature and humidity conditions was developed using UV visible reflectance spectroscopy. Dilute fresh blood with anticoagulant by 2, 4, 8, 16, and 32 times with tap water. Take 20 μL diluted blood and drop it in the center of a white cotton cloth. After drying, use the reflection accessory of a UV visible spectrophotometer (UV-2450 type) to collect the reflection spectrum, and Excel was used to do regression analysis of trough values and formation time of blood stains at different concentrations. As time increased, there was a significant blue shift in the reflected wave troughs of normal and diluted blood stains near 577.0 nm and 540.0 nm. The values of R² of the regression equation were all greater than 0.800 0, and there was a good linear relationship between the trough blue shift and the formation time of blood stain. The average speed of the trough blue shift increased with the increase of dilution ratio. Conclusion: The blue shift in the reflected wave valleys at 577.0 nm and 540.0 nm can serve as a basis for determining the formation time of normal blood stains, and combined with visual observation, it can also serve as a reference for determining the formation time of diluted blood stains.

With the wide application of surveillance systems, there is an increasing concern about public safety and security issues. Among them, the rapid detection and recognition of fighting behavior is very important for maintaining social order and security. However, traditional monitoring systems often face many challenges when dealing with large-scale video streams, including high computational complexity and resource-limited environments. In order to cope with these challenges, this paper proposes an improved fighting behavior detection model based on YOLOv5s, which reduces the number of parameters of the model and the computational complexity, so that the model can operate more efficiently in the resource-limited environment and detect various fighting behaviors more accurately. First of all, the open source interactive markup tool Labelimg was used to annotate the data set and train the network model with a large amount of data. Secondly, considering the need for rapid and accurate solutions in public security practice, lightweight network MobileNetv3 is used as the backbone network by comparing various convolutional structures to replace the original backbone network of YOLOv5s model, so as to reduce the number of parameters and calculation amount of the model and improve the model detection accuracy. By setting ablation experiments, the improved model is compared with other models and the original model. The experimental results show that compared with the original network, the detection accuracy of the improved model is increased from 92% to 94.4%, the computational load is reduced from the original 15.8 G to 3.1 G, and the detection speed of the algorithm can reach 0.153 s at the fastest, meeting the real-time requirements. And the detection accuracy is the highest among the three models. This model is suitable for public security practical application scenarios with high precision and limited memory and computing power.

Human body odor arises from the secretion of various glands on the skin's surface, which, when acted upon by microorganisms, evaporate to produce a distinct scent. This odor contains valuable biological information, with certain compounds exhibiting strong stability and individual specificity, serving as “odor fingerprinting” that can distinguish between different populations. Machine learning is an important method for human odor research, which can not only explore the characteristic components of odor in different populations, but also investigate the differences between different individuals. This paper discusses the application of “odor fingerprinting” in individual identification and feature characterization, drawing upon recent literature. It outlines the data processing procedures involved in human odor analysis, highlights the challenges encountered, and explores current research trends. Finally, the application trends of the recognition of human odor are discussed in order to provide reference for odor recognition research.

For the first time, a general ¹H-quantitative nuclear magnetic resonance spectrometry (¹H-qNMR) method for the simultaneous quantification of heroin hydrochloride, cocaine, cocaine hydrochloride, and 7 adulterants was established after the discussion of some key quantitative parameters. According to the solubility and stability of heroin hydrochloride, cocaine, and cocaine hydrochloride in different deuterated solvents, and the spectra comparison of seized samples and reference materials, dimethylsulfoxide-d6 was selected as the solvent, and 1,3,5-trimethoxybenzene was selected as the internal standard. The signals at δ_H 6.09 ppm for internal standard 1,3,5-trimethoxybenzene; at δ_H 6.87, 6.71, 2.24 ppm for heroin hydrochloride; at δ_H 7.90, 7.64, 5.12, 3.61 ppm for cocaine; and at δ_H 7.88, 5.49, 2.79 ppm for cocaine hydrochloride were selected as quantitative peaks．The established ¹H-qNMR method was applied for the quantification of 168 seized samples, the content of heroin and cocaine were basically consistent with the results of High-performance liquid chromatography. This method is simple, fast, accurate, does not require standard samples, and shows good tolerance and high reproducibility. It can provide new ideas for the quantification and profiling analysis of seized drug samples.