Comparative Assessment of a Novel Photo‐Anthropometric Landmark‐Positioning Approach for the Analysis of Facial Structures on Two‐Dimensional Images

Positioning landmarks in facial photo‐anthropometry (FPA) applications remains today a highly variable procedure, as traditional cephalometric definitions are used as guidelines. Herein, a novel landmark‐positioning approach, specifically adapted for FPA applications, is introduced and, in particular, assessed against the conventional cephalometric definitions for the analysis of 16 landmarks on ten frontal images by two groups of examiners (with and without professional knowledge of anatomy). Results showed that positioning reproducibility was significantly better using the novel method. Indeed, in contrast to the classic approach, very low landmark dispersions were observed for both groups of examiners, which were usually below the strictest clinical standards (i.e., 0.575 mm). Furthermore, the comparison between the two groups of examiners highlighted higher dispersion consistencies, which supported a higher robustness. Thus, the use of an adapted landmark‐positioning approach proved to be highly advantageous in FPA analysis and future work in this field should consider adopting similar methodologies

Targeted and non-targeted forensic profiling of black powder substitutes and gunshot residue using gradient ion chromatography – high resolution mass spectrometry (IC-HRMS)

A novel and simplified gradient IC-HRMS approach is presented in this work for forensic profiling of ionic energetic material residues, including low-order explosives and gunshot residue (GSR). This new method incorporated ethanolic eluents to facilitate direct coupling of IC and HRMS without auxiliary post-column infusion pumps that are traditionally used to assist with gas phase transfer. Ethanolic eluents also enabled better integration with an in-service protocol for direct analysis of high-order organic explosives by ICHRMS, without requiring solvent exchange before injection. Excellent method performance was achieved, enabling both full scan qualitative and quantitative analysis, as required. In particular, linearity for 19 targeted compounds yielded R2 > 0.99 across several orders of magnitude, with trace analysis possible at the low-mid pg level. Reproducibility and mass accuracies were also excellent, with peak area %RSDs < 10 %, tR %RSDs < 0.4% and δm/z < 3 ppm. The method was applied to targeted analysis of latent fingermarks and swabbed hand sweat samples to determine contact with a black-powder substitute containing nitrate, benzoate and perchlorate. When combined with principal component analysis (PCA), the effect of time since handling on recorded signals could be interpreted further in order to support forensic investigations. In a second, non-targeted application, PCA using full scan IC-HRMS data enabled classification of GSR from three different types of ammunition. An additional 20 markers of GSR were tentatively identified in silico, in addition to the 15 anions detected during targeted analysis. This new approach therefore streamlines and adds consistency and flexibility to forensic analysis of ionic energetic material. Furthermore, it also has implications for targeted, non-targeted and suspect screening applications in other fields by expanding the separation space to low molecular weight inorganic and organic anions

DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models

The field of DNA intelligence focuses on retrieving information from DNA evidence that can help narrow down large groups of suspects or define target groups of interest. With recent breakthroughs on the estimation of geographical ancestry and physical appearance, the estimation of chronological age comes to complete this circle of information. Recent studies have identified methylation sites in the human genome that correlate strongly with age and can be used for the development of age-estimation algorithms. In this study, 110 whole blood samples from individuals aged 11-93 years were analysed using a DNA methylation quantification assay based on bisulphite conversion and massively parallel sequencing (Illumina MiSeq) of 12 CpG sites. Using this data, 17 different statistical modelling approaches were compared based on root mean square error (RMSE) and a Support Vector Machine with polynomial function (SVMp) model was selected for further testing. For the selected model (RMSE = 4.9 years) the mean average error (MAE) of the blind test (n = 33) was calculated at 4.1 years, with 52% of the samples predicting with less than 4 years of error and 86% with less than 7 years. Furthermore, the sensitivity of the method was assessed both in terms of methylation quantification accuracy and prediction accuracy in the first validation of this kind. The described method retained its accuracy down to 10 ng of initial DNA input or ~2 ng bisulphite PCR input. Finally, 34 saliva samples were analysed and following basic normalisation, the chronological age of the donors was predicted with less than 4 years of error for 50% of the samples and with less than 7 years of error for 70%