Latent fingerprints (LFPs) are predominantly used for personal identification, but in recent years research has shown their potential for drug screening. Despite this there is no standardised collection method to allow accurate drug test interpretation. We sought to help address this by characterising different variables related to sweat deposition in LFPs as the knowledge is limited. A series of experiments were conducted firstly to validate a novel tool called the Ridgeway (Intelligent Fingerprint Ltd. UK) to quantify the amount of sweat deposited from a LFP using the refractive index (RI). A significant positive correlation was observed between the Ridgeway score (Rs) and LFP mass [r = 0.868, p < 0.01]. The Rs was used as means to investigate optimal sampling to characterise sample deposition for drug screening purposes. It was found with a consistent disposition pressure (300 - 400 g) and surface (glass slide) no significant difference was observed between the left and right index finger [left: p = 0.938; right: p = 0.838]. Significantly higher Rs [p<0.01] were obtained when 10 cumulative LFPs were deposited compared to a single LFP, suggesting a larger sweat quantity. We also wanted to investigate optimal eccrine sweat sampling to confirm drug ingestion over drug contamination of the fingerprint. We found that wearing gloves did not significantly improve mean difference in Rs when compared to no gloves [p = 0.239]. To produce eccrine only LFPs, external contamination (e.g. sebaceous sweat) needs to be removed. Soap with lint free tissue was significantly better for this compared to antibacterial hand gel [p<0.01]. Our findings showed that the Ridgeway tool effectively quantified LFPs at the point of deposition using a refractive index and enabled us to establish conditions for consistent LFP sampling.

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), have provided excellent figures of merit for analyzing high molecular-weight compounds. However, it can be challenging to analyze low molecular-weight compounds using MALDI-MS owing to potential interference produced by the organic matrices in the low molecular-weight region, which can impede the detection of small molecules (m/z < 700 Da). Alternately, surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved the production of ions involved in this method. The advancement of MALDI-MS and SALDI-MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI-MS and SALDI-MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework.

The analysis of fingerprint chemical composition is a meaningful way to excavate the multidimensional information of fingerprint, including the donor profiling information and the age of a fingerprint, which broadens the evidential values of fingerprint, especially for the partial and distorted fingerprint. But the research remains still in the pilot phases or is ongoing. Amino acids are the dominant organic substances in latent sweat fingerprint and influenced by many donor factors. Hence, their content reflects personal information of donors. Forensic science will be revolutionized if suspects can be individualized by their amino acid content. The diverse nature, distinct physicochemical properties, and ultra-micro levels of amino acids present in fingerprints make it hard to detect. A high sensitivity method for detecting and quantifying multiple amino acid components is required. UHPLC-QqQ MS/MS offers high sensitivity, high separation, simultaneous multicomponents detection, and no derivatization, making it an ideal method for detecting and analyzing amino acids in fingerprints. Therefore, in this study, we propose and validate an efficient UHPLC-QqQ MS/MS method for the extraction and analysis of 13 amino acids from fingerprint. We compared the results of amino acids of 10 different substrates and found that the inherent amino acids in most porous substrates would have been extracted along with the fingerprint amino acids, making them unsuitable for quantitative amino acid analysis. Instead, plastic sheets are ideal substrates for laboratory studies. Then, extensive experiments were conducted among 30 donors for multidimensional information analysis. The type of samples analyzed were eccrine-rich fingerprints. A Binary Logistic Regression (BLR) model was developed, and the female and male donors were successfully differentiated by amino acids in fingerprints. Two other mathematical models were also developed to verify the accuracy, and all three different mathematical models were able to identify donors of different genders with over 90% accuracy. This demonstrates that amino acids have the potential to provide more information for donors as metabolic markers. In the future, we will conduct a series of experiments to analyze more multidimensional information for individual identification by amino acid content in the fingerprint.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been shown to enhance fingermark recovery compared to standard processes used by police forces, but there is no data to show how generally applicable the improvement is. Additionally, ToF-SIMS can be run in either positive or negative ion mode (or both), and there is no data on which mode of operation is most effective at revealing fingerprints. This study aims to fill these gaps by using ToF-SIMS to image fingerprints deposited on two common exhibit-type surfaces (polyethylene and stainless steel) using 10 donors and ageing fingerprints in either ambient, rainwater, or underground for 1 and 5 months. In all, 120 fingerprints were imaged using ToF-SIMS, and each was run in positive and negative modes. A fingerprint expert compared the fingerprint ridge detail produced by the standard process to the ToF-SIMS images. In over 50% of the samples, ToF-SIMS was shown to improve fingerprint ridge detail visualised by the respective standard process for all surfaces tested. In over 90% of the samples, the ridge detail produced by ToF-SIMS was equivalent to standard development across all different ageing and exposure conditions. The data shows that there is a benefit to running the ToF-SIMS in both positive and negative modes, even if no ridge detail was seen in one mode.

Interest in the human microbiome has grown in recent years because of increasing applications to biomedicine and forensic science. However, the potential for dating evidence at a crime scene based upon time-dependent changes in microbial signatures has not been established, despite a relatively straightforward scientific process for isolating the microbiome. We hypothesize that modifications in microbial diversity, abundance, and succession can provide estimates of the time a surface was touched for investigative purposes. In this proof-of-concept research, the sequencing and analysis of the 16 S rRNA gene from microbes present in fresh and aged latent fingerprints deposited by three donors with pre- and post-washed hands is reported. The stability of major microbial phyla is confirmed while the dynamics of less abundant groups is described up to 21 days post-deposition. Most importantly, a phylum is suggested as the source for possible biological markers to date fingerprints: Deinococcus-Thermus.

The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.

Overlapping fingermark images are sometimes discarded because fingermark collation for the individual fingermarks is difficult. Fluorescence hyperspectral data (HSD) measured using the models of double overlapping fingermarks obtained under the excitation of a high-power, continuous wave, green laser is suitable for obtaining individual fingermark images. However, there are limitations such as the problems on each spectrum of the individual fingermark and the forensic value of the obtained images. In this study, independent component analysis (ICA) was applied to the fluorescence HSD obtained from the models of doubly overlapping fingermarks, to obtain independent component (IC) spectra and the corresponding IC images. Forensic value of the obtained IC images was examined, considering the possibility of fingermark collation in masked fashion to the model sample information. The IC images obtained from the HSD had enough potential to enable extracting twelve minutiae required for fingermark collation if the image quality was good.

Simple and efficient detection and mapping method based on a strong turn-on fluorescent pigment was developed for fingerprint analysis. We present a phosphor powder characterized by strong emission which is useful to achieve better fingerprint detection on multicolored or photoluminescent surfaces, such as currency notes characterized by optically changeable inks and highly fluorescent positions, because it offers better contrast and reduce the difficulty of background interference. Novel photochromic ink was prepared to establish a fingerprinted colorless film onto cellulose documents with green emission for anticounterfeiting applications as illustrated by photoluminescence spectra. Inorganic/organic nanoscale composite ink was prepared from rare-earth doped aluminate phosphor nanoparticles (PNPs; 27-49 nm) dispersed in a polyacrylic acid binding agent. PNPs were dispersed efficiently in polyacrylic acid to generate a colorless mark. The produced photochromic inks were spray-coated onto off-white paper sheets enclosing invisible fingermarks, and then exposed to thermofixation. Photochromic film was detected on paper surface presenting a transparent appearance under visible daylight and switchable to green under UV light. The CIE Lab parameters and photoluminescence spectra were studied under visible light and ultraviolet irradiation. The fingerprinted sheets showed fluorescence band at 517 nm upon excitation at 366 nm, showing a bathochromic shift and reversible photochromism without fatigue. The morphologies of pigment phosphor particles and fingerprinted sheets were inspected. The rheological properties of ink and mechanical behavior of the fingerprinted paper samples were explored. HIGHLIGHTS: Novel smart ink with alkaline-earth aluminate and polyacrylic acid was developed. Dual-mode fluorescent photochromism was presented for latent fingerprint analysis. Off-white fingerprinted films under daylight showed color change to green under UV. Fluorescence band monitored at 517 nm upon excitation at 366 nm. Fluorescent fingermark on paper sheets demonstrated good photostability.

Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.

Overlapping fingerprints are often found at crime scenes, but only individual fingerprints separated from each other are admissible as evidence in court. Fingerprint components differ slightly among individuals, and thus their fluorescence spectra also differ from each other. Therefore, the separation of overlapping fingerprints using the difference of the fluorescence spectrum was performed with a hyperspectral imager. Hyperspectral data (HSD) of overlapping fingerprints were recorded under UV LED excitation. Principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) were applied to the HSD to determine the optimal method for obtaining high-contrast images of individual fingerprints. The results suggested that MCR-ALS combined with PCA-based initialization is capable of separating overlapping fingerprints into individual fingerprints. In this study, a method for separating overlapping fingerprints without initial parameters was proposed.