Human odor and forensics: Towards Bayesian suspect identification using GC × GC–MS characterization of hand odor

ABOV: A Novel System of Direct Headspace Skin Sampling to Study Human Body Odor

Chemicals emitted by the human body convey information about the individuals. However, our understanding of the chemical underpinnings of human chemical communication remains limited, partly due to methodological constraints. Here, we describe a novel sampling technique, named ABOV (Analysis of Body Odor Volatiles), for analyzing the chemical composition of human skin odor. The ABOV device was designed to be easy to use and comfortable, adaptable to different contexts and body parts, and to collect in a non-contact manner airborne chemicals potentially involved in chemical communication. Twenty participants were sampled with this technique in their right and left axillae and neck, and their chemical profiles were obtained through gas chromatography-mass spectrometry (GC-MS) analysis. We robustly showed higher similarity of odor profiles between left/right sides of a given individual than between his/her odor sources (axilla vs. neck) or - even more prominently - than between different individuals. Further, exploratory analyses (PLS-DA) confirmed that the axilla and neck significantly differ in their chemical profiles, and that differences between men's and women's body odor profiles are also present although less pronounced. Several compounds were identified as being more characteristic of one source or sex than the other, and we concluded that predicting sex based on skin volatile profiles has limited reliability (at best 34% error) while prediction reliability was rather good for odor source (11% error). Overall, the novel device ABOV may be used in the future for ecological body odor sampling, even on moving subjects during behavioral experiments, to further investigate the chemical bases of human odor diversity and chemical communication.

Human scent characterization: A review

Human scent has long been cited as a probable parameter that can be exploited as a biometric measure. Identifying the scent of individual persons using specially trained canines is a well-known forensic method which is frequently used in criminal investigations. To date there has been limited research on the chemical components present in human scent and their usefulness in distinguishing between people. This review delivers insight into studies which have dealt with human scent in forensics. Sample collection methods, sample preparation, instrumental analysis, compounds identified in human scent and data analysis techniques are discussed. Methods for sample collection and preparation are presented, but to date, there is no available validated method. Instrumental methods are presented and from the overview it is clear that gas chromatography combined with mass spectrometry is the method of choice. New developments such as two-dimensional gas chromatography offer exiting possibilities to collect more information. Given the amount and complexity of data, data processing is used to extract the relevant information to discriminate people. Finally, sensors offer new opportunities for the characterization of human scent.

An Identity Recognition Model Based on RF-RFE: Utilizing Eye-Movement Data

Can eyes tell the truth? Can the analysis of human eye-movement data reveal psychological activities and uncover hidden information? Lying is a prevalent phenomenon in human society, but research has shown that people's accuracy in identifying deceptive behavior is not significantly higher than chance-level probability. In this paper, simulated crime experiments were carried out to extract the eye-movement features of 83 participants while viewing crime-related pictures using an eye tracker, and the importance of eye-movement features through interpretable machine learning was analyzed. In the experiment, the participants were independently selected into three groups: innocent group, informed group, and crime group. In the test, the eye tracker was used to extract a total of five categories of eye-movement indexes within the area of interest (AOI), including the fixation time, fixation count, pupil diameter, saccade frequency, and blink frequency, and the differences in these indexes were analyzed. Building upon interpretable learning algorithms, further investigation was conducted to assess the contribution of these metrics. As a result, the RF-RFE suspect identification model was constructed, achieving a maximum accuracy rate of 91.7%. The experimental results further support the feasibility of utilizing eye-movement features to reveal inner psychological activities.

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.

Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

Development of a Method for the Measurement of Human Scent Samples Using Comprehensive Two-Dimensional Gas Chromatography with Mass Detection

Every human body is a source of a unique scent, which can be used for medical or forensic purposes. Human skin scent is a complex mixture of more or less volatile compounds with different chemical and physical properties, which often differ significantly in their concentrations. The most efficient technique for separating such complex samples is comprehensive two-dimensional gas chromatography (GC × GC). This work aimed to find the optimal arrangement of a two-dimensional chromatographic system and define a suitable chromatographic method for non-targeted analysis of human scent samples. Four different chromatographic columns (non-polar Rxi-5MS and TG-5HT, medium polar Rxi-17Sil MS and Rtx-200MS) and their different configurations were tested. The best system was the 30 m primary column Rtx-200MS (with the 2 m pre-column Rtx-200MS) and the 1 m secondary column TG-5HT in a reverse configuration. This system achieved the highest theoretical and conditional peak capacities, optimal resolution, and the lowest number of coelutions.

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.

On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS

This paper reports on-line mixture quantification with FAIMS. Ternary gas mixtures composed of acetone, ethanol, and diethyl ether were used for quantification. We succeeded in an on-line quantification of ppm-level concentration and even sub-ppm-level gases using the gradient descent method. It took 10 minutes to quantify the ternary mixture. However, it was too long, because we aim to track the temporal change of each component concentration in the mixture. Then, an algorithm based on feedback control was introduced to reduce the quantification time. The feedback method successfully tracked concentrations in three cases. The simulation result shows that the proposed method can reduce the quantification time.

The Blossoming of Technology for the Analysis of Complex Aroma Bouquets-A Review on Flavour and Odorant Multidimensional and Comprehensive Gas Chromatography Applications

Multidimensional approaches in gas chromatography have been established as potent tools to (almost) attain fully resolved analyses. Flavours and odours are important application fields for these techniques since they include complex matrices, and are of interest for both scientific study and to consumers. This article is a review of the main research studies in the above theme, discussing the achievements and challenges that demonstrate a maturing of analytical separation technology.