Evaluation of Nile Red‐Loaded Mesoporous Silica Nanoparticles for Developing Water‐Soaked Fingerprints on Thermal Paper

Nile red has been an alternative reagent for detecting latent fingerprints on wetted substrates. However, the presence of methanol in nile red solution could make injury to handlers and destroy the traces on surfaces, such as texts on thermal papers. A novel small particle reagent formulation constituting of mesoporous silica nanoparticles (MSNs) based on nile red was prepared to overcome the problem. Compared with the conventional reagents Oil Red O or nile red solution, the nile red‐loaded MSNs are highly selective to lipid residues of fingerprints and showed a greater ability to develop clear, sharp, and detailed fingerprints on thermal papers after these were immersed in water. In addition, it can retain texts on the thermal papers well and use only water as a solvent. These suggested that nile red‐loaded MSNs are a safe, efficient, and convenient method to develop latent fingerprints on wide range of substrates of forensic importance.     

Rapid Separation of Organophosphate Pesticides using Micellar Electrokinetic Chromatography and Short‐end Injection,

Organophosphate (OP) pesticides are highly toxic substances and are frequently represented as poisons. In order to qualify and quantify the selected OP pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion, fenitrothion, and ethyl parathion), micellar electrokinetic chromatography and short‐end injection were investigated. This is the first time that this combination has been used to separate OP pesticides. A capillary with 8.5 cm effective length was used, and the analytes were separated within 2.1 min. Separation conditions including buffer (type, pH, and concentration), sodium dodecyl sulfate concentration, and separation voltage were optimized. The limit of detection (LOD) was estimated in the range of 10–20 μM. The OP pesticides spiked in artificial saliva and drinking water gave superior peak profiles, and good average recoveries 95.6% and 62.3%, respectively. Overall, a rapid method with excellent resolution and efficiency was developed and successfully applied in the analysis of potential sample matrixes.     

Development and Validation of a Method Using Dried Oral Fluid Spot to Determine Drugs of Abuse

A liquid chromatography–mass spectrometry method using dried oral fluid spots was developed and validated for the simultaneous quantification of cocaine, benzoylecgonine, cocaethylene, amphetamine, and 3,4‐methylenedioxymethamphetamine. The oral fluid was applied to a Whatman 903 grade paper and submitted to a drying time of 2.5 h. The extraction procedure was optimized by chemometric approach using simplex centroid design. Spots were extracted with a mixture of acetonitrile, buffer, and methanol. Calibration curves covered a linear concentration range of 40–500 ng/mL. Validation parameters of linearity, precision, accuracy, selectivity, carryover, matrix effects, and stability were evaluated and showed satisfactory results. Spot homogeneity was also satisfactory, with less than 15% of deviation from nominal concentration. Spot volume did not influence accuracy when less than 100 μL of the sample was applied to the spot. The validation of the proposed method suggests a potential application in different scenarios in toxicology.     

Are reality monitoring differences between truthful and deceptive autobiographical accounts affected by standardisation for word-count and the presence of others?

Although a number of methods have been proposed to control for word-count differences between truthful and deceptive accounts, there is no uniformity amongst researchers using the Reality Monitoring (RM) criteria as to when, why or how to standardise for word-count differences. Another factor that also has received little attention in the literature is whether the number of others present when a person is providing an account alters the lexical profile of accounts such that RM scores are affected. To investigate these issues, 62 autobiographical statements, 31 truthful and 31 deceptive, were generated under 3 conditions, no person present, 1 and 2 persons present, and were analysed before and after standardisation for word-count and duration. Results showed that the criteria successfully discriminated between truthful and deceptive accounts when no attempt to control for word-count was made and, to a lesser extent, when accounts were standardised for duration; however, they failed to discriminate after accounts had been standardised for length. The presence of others did not affect the ability to distinguish between truthful and deceptive accounts. The results highlight the difficulties involved in developing normative standardisation criteria which could be used in the field to classify individual or small numbers of cases.     

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.     

Forensic Analysis of Stains on Fabric Using Direct Analysis in Real‐time Ionization with High‐Resolution Accurate Mass‐Mass Spectrometry

A rapid technique using direct analysis in real ‐ time (DART) ambient ionization coupled to a high ‐ resolution accurate mass‐mass spectrometer (HRAM‐MS) was employed to analyze stains on an individual's pants suspected to have been involved in a violent crime. The victim was consuming chocolate ice cream at the time of the attack, and investigators recovered the suspect's pants exhibiting splatter stains. Liquid chromatography with mass spectral detection (LC‐MS) and stereoscopic light microscopy (SLM) were also utilized in this analysis. It was determined that the stains on the pants contained theobromine and caffeine, known components of chocolate. A shard from the ceramic bowl that contained the victim's ice cream and a control chocolate ice cream sample were also found to contain caffeine and theobromine. The use of DART‐HRAM‐MS was useful in this case due to its rapid analysis capability and because of the limited amount of sample present as a stain.     

Stress and Deception in Speech: Evaluating Layered Voice Analysis*

Abstract: This study was designed to evaluate commonly used voice stress analyzers—in this case the layered voice analysis (LVA) system. The research protocol involved the use of a speech database containing materials recorded while highly controlled deception and stress levels were systematically varied. Subjects were 24 each males/females (age range 18–63 years) drawn from a diverse population. All held strong views about some issue; they were required to make intense contradictory statements while believing that they would be heard/seen by peers. The LVA system was then evaluated by means of a double blind study using two types of examiners: a pair of scientists trained and certified by the manufacturer in the proper use of the system and two highly experienced LVA instructors provided by this same firm. The results showed that the “true positive” (or hit) rates for all examiners averaged near chance (42–56%) for all conditions, types of materials (e.g., stress vs. unstressed, truth vs. deception), and examiners (scientists vs. manufacturers). Most importantly, the false positive rate was very high, ranging from 40% to 65%. Sensitivity statistics confirmed that the LVA system operated at about chance levels in the detection of truth, deception, and the presence of high and low vocal stress states.     

A LDR‐PCR Approach for Multiplex Polymorphisms Genotyping of Severely Degraded DNA with Fragment Sizes <100 bp*

Abstract: Reducing amplicon sizes has become a major strategy for analyzing degraded DNA typical of forensic samples. However, amplicon sizes in current mini‐short tandem repeat‐polymerase chain reaction (PCR) and mini‐sequencing assays are still not suitable for analysis of severely degraded DNA. In this study, we present a multiplex typing method that couples ligase detection reaction with PCR that can be used to identify single nucleotide polymorphisms and small‐scale insertion/deletions in a sample of severely fragmented DNA. This method adopts thermostable ligation for allele discrimination and subsequent PCR for signal enhancement. In this study, four polymorphic loci were used to assess the ability of this technique to discriminate alleles in an artificially degraded sample of DNA with fragment sizes <100 bp. Our results showed clear allelic discrimination of single or multiple loci, suggesting that this method might aid in the analysis of extremely degraded samples in which allelic drop out of larger fragments is observed.