Lenka Halámková

Human nails are a durable, non-invasively collected keratin matrix that records exposure over months. We assessed whether attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy of intact fingernail clippings, combined with machine learning, can discriminate self-reported smokers from non-smokers. Spectra (n = 3681) from 87 donors (42 smokers, 45 non-smokers) were second-derivative transformed, truncated to the informative regions and L2-normalized. PLS-DA and a radial-basis-function support-vector-machine discriminant analysis (SVM-DA) were developed with fully nested, donor-grouped tuning and evaluated over ten repeated 20-donor hold-outs at the spectrum and donor levels. Aggregating spectra per donor improved discrimination (+0.07–0.09 AUROC). SVM-DA on all nails reached a donor-level AUROC of 0.86 (95% CI 0.75–0.98; balanced accuracy 0.77, specificity 0.91, sensitivity 0.62); PLS-DA, logistic regression and random forest reached 0.79–0.84, with no statistically significant differences. Smokers were older and predominantly male: sex and age alone gave an AUROC of 0.79; adding the spectral score raised it to 0.89, an uncertain gain (95% CI −0.04 to 0.23), and SVM-DA retained an AUROC of 0.79 in male donors only. ATR FT-IR of nails carries a smoking-associated signal, but confirmation in a demographically matched, biochemically verified, independent cohort is required before forensic use.

Introduction: Species identification and sex determination have significant relevance in wildlife monitoring, conservation, and forensic investigations. This study explores the feasibility of using Raman spectroscopy coupled with Partial Least Squares Discriminant Analysis (PLS-DA) for sex determination of white-tailed deer ( Odocoileus virginianus ) based on serum and plasma samples. Methods: A total of 720 Raman spectra (360 serum, 360 plasma) were acquired from five male and five female deer. PLS-DA models were developed using full data and four calibration-to-validation splits (1:4, 2:3, 3:2, 4:1). Results: The serum-based PLS-DA model achieved predictive accuracies of 79.2% (1:4), 99.1% (2:3), 99.3% (3:2), and 98.6% (4:1), with balanced calibration-to-validation splits (≥2:3) demonstrating excellent robustness and generalizability exceeding 98%. The plasma-based model showed high internal accuracy (∼99.2%) but reduced external accuracy depending on the split: 85.8% (1:4), 82.4% (2:3), 86.1% (3:2), and 57.0% (4:1), reflecting greater variability and susceptibility to overfitting. Spectral comparison revealed consistent Raman bands in both matrices associated with proteins, lipids, and carbohydrates, with subtle but distinct sex-specific intensity differences. Males showed enhanced signals at ∼1,315 and ∼1,445 cm ^-1 (lipid regions), while females displayed stronger signals at ∼1,220 -1,280 and ∼1,540 -1,630 cm ^-1 (protein regions). Conclusion: These findings highlight the potential of Raman-PLSDA models —particularly with serum —as a reliable, rapid, and nondestructive method for sex classification in forensic wildlife contexts, especially when morphological features are absent or degraded. This approach may enable field-adaptable identification that enhance species conservation and forensic investigations involving incomplete or decomposed biological remains.

Butyrylcholinesterase (BChE) may serve as a scavenger enzyme protecting against various toxic compounds, but we still don't fully understand how it interacts with fentanyl analogues.

Human nails have recently become a sample of interest for toxicological purposes. Multiple studies have proven the ability to detect various analytes within the keratin matrix of the nail. The analyte of interest in this study is fentanyl, a highly dangerous and abused drug in recent decades. In this proof-of-concept study, ATR–FTIR was combined with machine learning methods, which are effective in detecting and differentiating fentanyl in samples, to explore whether nail samples are distinguishable from individuals who have used fentanyl and those who have not. PLS-DA and SVM-DA prediction models were created for this study and had an overall accuracy rate of 84.8% and 81.4%, respectively. Notably, when classification was considered at the donor level—i.e., determining whether the donor of the nail sample was using fentanyl—all donors were correctly classified. These results show that ATR–FTIR spectroscopy in combination with machine learning can effectively differentiate donors who have used fentanyl and those who have not and that human nails are a viable sample matrix for toxicology.

Raman spectroscopy and machine learning were combined in this proof-of-concept study for the race identification of a urine stain donor with 90% accuracy. This method offers a powerful new tool for immediate suspect profiling at the crime scene.

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor’s sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors’ fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

A novel method for metabolite monitoring is presented for diverse applications in biometric identification.

Fourier transform infrared (FT-IR) spectroscopy is used throughout forensic laboratories for many applications. FT-IR spectroscopy can be useful with ATR accessories in forensic analysis for several reasons. It provides excellent data quality combined with high reproducibility, with minimal user-induced variations and no sample preparation. Spectra from heterogeneous biological systems, including the integumentary system, can be associated with hundreds or thousands of biomolecules. The nail matrix of keratin possesses a complicated structure with captured circulating metabolites whose presence may vary in space and time depending on context and history. We developed a new approach by using machine-learning (ML) tools to leverage the potential and enhance the selectivity of the instrument, create classification models, and provide invaluable information saved in human nails with statistical confidence. Here, we report chemometric analysis of ATR FT-IR spectra for the classification and prediction of long-term alcohol consumption from nail clippings in 63 donors. A partial least squares discriminant analysis (PLS-DA) was used to create a classification model that was validated against an independent data set which resulted in 91% correctly classified spectra. However, when considering the prediction results at the donor level, 100% accuracy was achieved, and all donors were correctly classified. To the best of our knowledge, this proof-of-concept study demonstrates for the first time the ability of ATR FT-IR spectroscopy to discriminate donors who do not drink alcohol from those who drink alcohol on a regular basis.

Gunshot residue (GSR) is an important type of forensic trace evidence produced when a firearm is discharged. Currently, inorganic GSR particles are used for establishing the fact of shooting. The organic gunshot residue (OGSR) has been recently shown to have great potential for providing additional information vital for the crime scene investigation. Smokeless powder is the precursor to OGSR and one of its chemical components. In this study, Raman spectroscopy and chemometric modeling were used to analyze smokeless powder extracted from ammunition cartridge cases. The proposed hierarchical model demonstrated great potential for determining the manufacture and the bullet type based on the analysis of smokeless powder. Expanding the developed approach to the analysis of OGSR will be needed to make it a useful tool for law enforcement agencies.