Sampling method development and optimization in view of human hand odor analysis by thermal desorption coupled with gas chromatography and mass spectrometry

Screening of Breast Cancer from Sweat Samples Analyzed by 2-Dimensional Gas Chromatography-Mass Spectrometry: A Preliminary Study

Breast cancer (BC) remains one of the most commonly diagnosed malignancies in women. There is increasing interest in the development of non-invasive screening methods. Volatile organic compounds (VOCs) emitted through the metabolism of cancer cells are possible novel cancer biomarkers. This study aims to identify the existence of BC-specific VOCs in the sweat of BC patients. Sweat samples from the breast and hand area were collected from 21 BC participants before and after breast tumor ablation. Thermal desorption coupled with two-dimensional gas chromatography and mass spectrometry was used to analyze VOCs. A total of 761 volatiles from a homemade human odor library were screened on each chromatogram. From those 761 VOCs, a minimum of 77 VOCs were detected within the BC samples. Principal component analysis showed that VOCs differ between the pre- and post-surgery status of the BC patients. The Tree-based Pipeline Optimization Tool identified logistic regression as the best-performing machine learning model. Logistic regression modeling identified VOCs that distinguish the pre-and post-surgery state in BC patients on both the breast and hand area with sensitivities close to 1. Further, Shapley additive explanations and the probe variable method identified the most important and pertinent VOCs distinguishing pre- and post-operative status which are mostly of distinct origin for the hand and breast region. Results suggest the possibility to identify endogenous metabolites linked to BC, hence proposing this innovative pipeline as a stepstone to discovering potential BC biomarkers. Large-scale studies in a multi-centered VOC analysis setting must be carried out to validate obtained findings.

Evaluation of distance-based approaches for forensic comparison: Application to hand odor evidence

The issue of distinguishing between the same-source and different-source hypotheses based on various types of traces is a generic problem in forensic science. This problem is often tackled with Bayesian approaches, which are able to provide a likelihood ratio that quantifies the relative strengths of evidence supporting each of the two competing hypotheses. Here, we focus on distance-based approaches, whose robustness and specifically whose capacity to deal with high-dimensional evidence are very different, and need to be evaluated and optimized. A unified framework for direct methods based on estimating the likelihoods of the distance between traces under each of the two competing hypotheses, and indirect methods using logistic regression to discriminate between same-source and different-source distance distributions, is presented. Whilst direct methods are more flexible, indirect methods are more robust and quite natural in machine learning. Moreover, indirect methods also enable the use of a vectorial distance, thus preventing the severe information loss suffered by scalar distance approaches. Direct and indirect methods are compared in terms of sensitivity, specificity, and robustness, with and without dimensionality reduction, with and without feature selection, on the example of hand odor profiles, a novel and challenging type of evidence in the field of forensics. Empirical evaluations on a large panel of 534 subjects and their 1690 odor traces show the significant superiority of the indirect methods, especially without dimensionality reduction, be it with or without feature selection.

Scent lineups compared across eleven countries: Looking for the future of a controversial forensic technique

A scent lineup is generally a procedure whereby a dog's alerting behavior is used to establish that the dog detects two scents, one from a crime scene and one from a suspect, as deriving from the same person. The aim of this article is to compare methodologies of using dogs in scent lineups as a means of identifying perpetrators of crimes. It is hoped that this comparative approach, looking at countries where the method is currently or has in the past been used, will help determine what issues should be addressed in order to assure that the scent lineup will have a future as a forensic technique. Participants from eleven countries-Belgium, The Czech Republic, Finland, France, Germany, Hungary, Lithuania, The Netherlands, Poland, Russia, and the U.S.-completed a survey questionnaire regarding key aspects of the scent lineup procedures used by the police in their countries. Although there was broad overlap on certain matters, such as the use of control and zero trials, collection of decoy scents from individuals of similar gender and race as the suspect, materials for holding scent, frequency of cleaning and changing stations, and use and timing of rewards, there were significant differences in the degree of blindness required, who calls an alert (handler or experimenter), and whether handlers can work with more than one dog. The gap between recommendations and results available from the scientific literature and procedures used in police practice was greater for some countries than others, even taking into account that some scientific methodologies might be expensive or impractical given agency resources. The authors make recommendations about how to go forward if scent lineups are to remain a valid forensic technique.