A particularly challenging subject in the investigation of forensic human biological traces is analyzing samples containing mixtures of body fluids from multiple donors. Ideally, researchers want to identify each type of body fluid present. However, traditional methods, like mRNA and DNA profiling, often struggle with sensitivity, specificity, and efficiency, especially in complex mixtures. This proof-of-concept study has two primary aims: first, to classify body fluids within a mixture using discriminatory protein markers, and second, to evaluate the feasibility of using single amino acid variants (SAAVs) to trace the source of specific body fluids back to individual donors. To achieve this, we employed proteomic analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-independent acquisition (DIA) mode, developing a reliable approach for accurate body fluid classification. Through comprehensive proteomic profiling, we characterized a diverse array of discriminatory proteins present in peripheral blood, semen, saliva, urine, and vaginal fluid. Using advanced data analysis techniques, including t-distributed stochastic neighbor embedding (t-SNE), we demonstrated that these proteins could reliably distinguish between different body fluids, even in mixed samples. Additionally, our findings reveal that SAAVs within certain proteins, such as those in saliva, hold promise for source attribution in a forensic context. Challenges, including contamination and limited sample sizes, highlighted the need for strict quality controls and further large-scale studies. With these improvements, proteomic analysis could greatly enhance body fluid identification, classification, and source attribution in forensic investigations, improving both accuracy and reliability in forensic science.

Forensic DNA analysis is well established for phenotyping, providing valuable investigative leads. Proteomics, the large-scale study of proteins, is emerging as a complementary tool to DNA analysis, particularly for enhancing the evidential value of traces. This study explores the potential of proteomics to extract phenotypic traits from whole blood, using the estimation of biological sex as a starting point. Using LC–MS/MS, proteomes from 100 whole blood samples of known sex were analyzed to develop a biological sex classifier. Cross-validation of the model highlighted the model’s ability to achieve accurate classification, identifying key peptides, such as those from pregnancy zone protein and ceruloplasmin, as critical markers. To test real-world applicability, mock case samples were generated, bringing attention to the need for model robustness. Overall, our results suggest that using proteomics to infer phenotypic traits from whole blood samples in the context of forensics, while feasible, is hindered by hard-to-overcome technical challenges. We therefore recommend that future forensic proteomics research be directed toward areas where it can be most informative, such as source attribution and estimating the timeline of events, rather than focusing on extracting phenotypic traits for donor profiling.

Surface analysis techniques have rapidly evolved in the last decade. Some of these are already routinely used in forensics, such as for the detection of gunshot residue or for glass analysis. Some surface analysis approaches are attractive for their portability to the crime scene. Others can be very helpful in forensic laboratories owing to their high spatial resolution, analyte coverage, speed, and specificity. Despite this, many proposed applications of the techniques have not yet led to operational deployment. Here, we explore the application of these techniques to the most important traces commonly found in forensic casework. We highlight where there is potential to add value and outline the progress that is needed to achieve operational deployment. We consider within the scope of this review surface mass spectrometry, surface spectroscopy, and surface X-ray spectrometry. We show how these tools show great promise for the analysis of fingerprints, hair, drugs, explosives, and microtraces.

Bayesian networks have shown to be a useful tool for the evaluation of forensic findings given activity level propositions. In this paper, we demonstrate how case specific experiments can be used to assign probabilities to the states of the nodes of a Bayesian network for the evaluation of fingermarks given activity level propositions. The transfer, persistence and recovery of fingermarks on knives is studied in experiments where a knife is either used to stab a victim or to cut food, representing the activities that were disputed in the case of the murder of Meredith Kercher. Two Bayesian networks are constructed, exploring the effect of different uses of the experimental data by assigning the probabilities based on the results of the experiments. The evaluation of the findings using the Bayesian networks demonstrates the potential for fingermarks in addressing activity level propositions.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was applied to detect traces of amphetamine on fingerprints. In the present study, three different lift tapes and latent powder fingerprints were tested. The obtained results show that it is possible to identify traces of a drug as well as its distribution over the tested fingerprint after its transfer from the primary base onto an adhesive lifter (secondary base). Moreover, images obtained by the TOF-SIMS technique enable the observation of very small areas of the analysed fingerprint as well as the identification of micro-objects (residues of a contaminant) that were left on the fingerprint. The use of the black latent fingerprint powder did not interfere with the TOF-SIMS analysis, which makes it possible to effectively use this technique to study the traces of substances on the revealed fingerprints.

Fingermarks are traditionally used for individualisation purposes in a criminal investigation and as evidence in the courts. In the past few decades we have seen a wide variety of novel visualisation methods being explored. Moreover, there has been an enormous increase in the technological possibilities for the chemical analysis and imaging of fingermarks. These developments have a profound implication for criminal investigations. In this chapter the highlights of the challenges in detection of fingermarks will be discussed.

In this work, we have simultaneously examined, electrochemically driven deposition of three proteins (haemoglobin, acid phosphatase, and α-amylase) and silica films at a polarized liquid–liquid interface. The interfacial adsorption of the proteins occurs efficiently within the acidic pH range (pH = 2–4). The interfacial charge transfer reactions recorded in the presence of fully positivity charged macromolecules were followed with cyclic voltammetry on the positive side of the potential window. Faradaic currents attributed to the presence of proteins in the aqueous phase appeared for concentrations equal to ca. 0.1 µM for haemoglobin and acid phosphatase and ca. 1 µM for the α-amylase. Concomitant deposition of silica films was achieved via the addition of tetraethoxysilane molecules to the organic phase (1,2-dichloroethane). The hydrolysis and condensation reactions of tetraethoxysilane were controlled via the interfacial transfer of H⁺ coinciding with the potential for protein adsorption. The effect of tetraethoxysilane concentration – up to 50% by volume – revealed significant shrinkage of the potential window (the region where capacitive currents are recorded). The optimized platform was then used to prepare silica-proteins co-deposits. These could be easily collected from the interface and further analyzed with infrared spectroscopy and transmission electron microscopy.

We report a flexible single-cell isolation method by imaging-assisted hydrogel formation. Our approach consists of imaging-aided selective capture of cells of interest by encasing them into a polymeric hydrogel, followed by removal of unwanted cells and subsequent release of isolated cells by enzymatic hydrogel degradation, thus offering an opportunity for further analysis or cultivation of selected cells. We achieved high sorting efficiency and observed excellent viability rates (>98%) for NIH/3T3 fibroblasts and A549 carcinoma cells isolated using this procedure. The method presented here offers a mask-free, cost-efficient and easy-to-use alternative to many currently existing surface-based cell-sorting techniques, and has the potential to impact the field of cell culturing and isolation, e.g. single cell genomics and proteomics, investigation of cellular heterogeneity and isolation of best performing mutants for developing new cell lines.

A previous paper published in this journal proposed a model for evaluating the location of fingermarks on two-dimensional items (de Ronde, van Aken, de Puit and de Poot (2019)). In this paper, we apply the proposed model to a dataset consisting of letters to test whether the activity of writing a letter can be distinguished from the alternative activity of reading a letter based on the location of the fingermarks on the letters. An experiment was conducted in which participants were asked to read a letter and write a letter as separate activities on A4- and A5-sized papers. The fingermarks on the letters were visualized, and the resulting images were transformed into grid representations. A binary classification model was used to classify the letters into the activities of reading and writing based on the location of the fingermarks in the grid representations. Furthermore, the limitations of the model were studied by testing the influence of the length of the letter, the right- or left-handedness of the donor and the size of the paper with an additional activity of folding the paper. The results show that the model can predict the activities of reading or writing a letter based on the fingermark locations on A4-sized letters of right-handed donors with 98 % accuracy. Additionally, the length of the written letter and the handedness of the donor did not influence the performance of the classification model. Changing the size of the letters and adding an activity of folding the paper after writing on it decreased the model's accuracy. Expanding the training set with part of this new set had a positive influence on the model's accuracy. The results demonstrate that the model proposed by de Ronde, van Aken, de Puit and de Poot (2019) can indeed be applied to other two-dimensional items on which the disputed activities would be expected to lead to different fingermark locations. Moreover, we show that the location of fingermarks on letters provides valuable information about the activity that is carried out.

Fingerprints found at a crime scene can be key in criminal investigations. A method to accurately determine the age of the fingerprint, potentially crucial to linking the fingerprint to the crime, is not available at the moment. In this paper, we show that the use of the enantiomeric ratio of d/l-serine in fingerprints could pose as interesting target for age estimation techniques. We developed a UPLC-MS/MS method to determine the enantiomer ratios of histidine, serine, threonine, alanine, proline, methionine and valine from fingerprint residue. We found a significant change only in the relative ratio of d-serine with increasing fingerprint age after analysis of fingerprints up to 6 months old.

The amino acid profile obtained from a fingerprint may provide valuable information on its donor. Unfortunately, the collection of chemicals from the fingerprint is often destructive to the fingerprint ridge detail. Herein we detail the use of cross-linkable solutions of dextran-methacrylate to form hydrogels capable of collecting amino acids from surfaces followed by extraction and quantification with UPLC-MS. This method allows for the amino acid profile analysis of fingerprints while allowing for their increased visualization at a later stage using the standard method of cyanoacrylate fuming followed by basic-yellow dyeing.

Traces of condom lubricants in fingerprints can be valuable information in cases of sexual assault. Ideally, not only confirmation of the presence of the condom but also determination of the type of condom brand used can be retrieved. Previous studies have shown to be able to retrieve information about the condom brand and type from fingerprints containing lubricants using various analytical techniques. However, in practice fingerprints often appear latent and need to be detected first, which is often achieved by cyanoacrylate fuming. In this study, we developed a desorption electrospray ionization mass spectrometry (DESI-MS) method which, combined with principal component analysis and linear discriminant analysis (PCA-LDA), allows for high accuracy classification of condom brands and types from fingerprints containing condom lubricant traces. The developed method is compatible with cyanoacrylate (CA) fuming. We collected and analyzed a representative dataset for the Netherlands comprising 32 different condoms. Distinctive lubricant components such as polyethylene glycol (PEG), polydimethylsiloxane (PDMS), octoxynol-9 and nonoxynol-9 were readily detected using the DESI-MS method. Based on the analysis of lubricant spots, a 99.0% classification accuracy was achieved. When analyzing lubricant containing fingerprints, an overall accuracy of 90.9% was obtained. Full chemical images could be generated from fingerprints, showing the distribution of lubricant components such as PEG and PDMS throughout the fingerprint, while still allowing for classification. The developed method shows potential for the development of DESI-MS based analyses of CA treated exogenous compounds from fingerprints for use in forensic science.

In this paper, we describe a promising method to evaluate the location of fingermarks on two-dimensional objects, which provides valuable information for the evaluation of fingermarks at activity level. For this purpose, an experiment with pillowcases was conducted at the Dutch music festival Lowlands, to test whether the activity ‘smothering’ can be distinguished from an alternative activity like ‘changing a pillowcase’ based on the touch traces on pillowcases left by the activities. Participants performed two activities with paint on their hands: smothering a victim with the use of a pillow and changing a pillowcase of a pillow. The pillowcases were photographed and translated into grid representations. A binary classification model was used to classify the pillowcases into one of the two classes of smothering and changing, based on the distance between the grid representations. After applying the fitted model to a test set, we obtained an accuracy of 98.8%. The model showed that the pillowcases could be well separated into the two classes of smothering and changing, based on the location of the fingermarks. The proposed method can be applied to fingermark traces on all two-dimensional items for which we expect that different activities will lead to different fingermark locations.

Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore the evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.

Fingerprints are widely used in forensic science for individualization purposes. However, not every fingermark found at a crime scene is suitable for comparison, for instance due to distortion of ridge detail, or when the reference fingerprint is not in the database. To still retrieve information from these fingermarks, several studies have been initiated into the chemical composition of fingermarks, which is believed to be influenced by several donor traits. Yet, it is still unclear what donor information can be retrieved from the composition of one's fingerprint, mainly because of limited sample sizes and the focus on analytical method development. It this paper, we analyzed the chemical composition of 1852 fingerprints, donated by 463 donors during the Dutch music festival Lowlands in 2016. In a targeted approach we compared amino acid and lipid profiles obtained from different types of fingerprints. We found a large inter-variability in both amino acid and lipid content, and significant differences in L-(iso)leucine, L-phenylalanine and palmitoleic acid levels between male and female donors. In an untargeted approach we used full-scan MS data to generate classification models to predict gender (77.9% accuracy) and smoking habit (90.4% accuracy) of fingerprint donors. In the latter, putatively, nicotine and cotinine are used as predictors.

In this review, we describe the importance and possible electrochemical screening methods for the illicit drug – cocaine. It covers the detection at bare and modified solid electrodes, soft electrified junctions and nanopore sensing. Emphasis is given on interfacial modification techniques and electroanalytical parameters for cocaine detection in different environments, covering the detection from both, model and real samples.

UPLC-MS is a commonly used technique to first separate complex samples and subsequently quantify molecules of interest. Herein we describe the use of UPLC-MS using an amide stationary phase to quantify non-derivatized amino acids extracted from fingerprints. As detector either a triple-quadrupole MS/MS or a TOF-MS detector was used. This method allows for a simple and fast sample preparation, which facilitates the analysis of large amounts of samples.

The behaviour of acid phosphatase at an electrified liquid–liquid interface was studied in this work. It was found that only the protonated form of the protein can undergo interfacial adsorption which is affected by the pH of the aqueous phase. With ion transfer voltammetry we could detect acid phosphatase in concentrations as low as 0.1 μM. We were able to co-deposit the protein and silica at the electrified liquid–liquid interface via controlled proton transfer to the organic phase where it catalyzed tetraethoxysilane hydrolysis, followed by polycondensation to silica.