Hand Odor Research Articles

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS.

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects' hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study

The adaptable nature of the SARS-CoV-2 virus has led to the emergence of multiple viral variants of concern. This research builds upon a previous demonstration of sampling human hand odor to distinguish SARS-CoV-2 infection status in order to incorporate considerations of the disease variants. This study demonstrates the ability of human odor expression to be implemented as a non-invasive medium for the differentiation of SARS-CoV-2 variants. Volatile organic compounds (VOCs) were extracted from SARS-CoV-2-positive samples using solid phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC–MS). Sparse partial least squares discriminant analysis (sPLS-DA) modeling revealed that supervised machine learning could be used to predict the variant identity of a sample using VOC expression alone. The class discrimination of Delta and Omicron BA.5 variant samples was performed with 95.2% (±0.4) accuracy. Omicron BA.2 and Omicron BA.5 variants were correctly classified with 78.5% (±0.8) accuracy. Lastly, Delta and Omicron BA.2 samples were assigned with 71.2% (±1.0) accuracy. This work builds upon the framework of non-invasive techniques producing diagnostics through the analysis of human odor expression, all in support of public health monitoring.

Investigating the Use of SARS-CoV-2 (COVID-19) Odor Expression as a Non-Invasive Diagnostic Tool-Pilot Study.

Since the beginning of the COVID-19 pandemic, there has been enormous interest in the development of measures that would allow for the swift detection of the disease. The rapid screening and preliminary diagnosis of SARS-CoV-2 infection allow for the instant identification of possibly infected individuals and the subsequent mitigation of the disease spread. Herein, the detection of SARS-CoV-2-infected individuals was explored using noninvasive sampling and low-preparatory-work analytical instrumentation. Hand odor samples were obtained from SARS-CoV-2-positive and -negative individuals. The volatile organic compounds (VOCs) were extracted from the collected hand odor samples using solid phase microextraction (SPME) and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Sparse partial least squares discriminant analysis (sPLS-DA) was used to develop predictive models using the suspected variant sample subsets. The developed sPLS-DA models performed moderately (75.8% (±0.4) accuracy, 81.8% sensitivity, 69.7% specificity) at distinguishing between SARS-CoV-2-positive and negative -individuals based on the VOC signatures alone. Potential markers for distinguishing between infection statuses were preliminarily acquired using this multivariate data analysis. This work highlights the potential of using odor signatures as a diagnostic tool and sets the groundwork for the optimization of other rapid screening sensors such as e-noses or detection canines.

The influence of intra-personal variations in human hand odor on the determination of sample donor

A dual effort investigation was conducted to study (a) the naturally occurring variation in same-donor human hand odor samples over time and (b) the accuracy in associating same-donor human hand odor samples. Hand odor samples were collected from 8 donors throughout 5 sampling sessions; samples were collected in triplicate and analyzed by HS-SPME-GC-MS at each sampling session. The resulting human hand odor profiles were analyzed to investigate (a) the variability of human hand odor profiles as a function of time and (b) the ability to determine the source origin of human hand odor samples, determining samples to be from the same source or different sources. The researchers observed greater variation in 2-dimensional human scent profile patterning schemes among inter-day, inter-subject samples and less variation in inter-day, intra-subject samples. Although intra-subject samples revealed less variation than inter-subject samples, there was still notable variability among inter-day, intra-subject human scent profiles, with an observed time dependency. Two proof of concept models for the source determination of human hand odor samples were developed with maximum performance measuring TPR = 0.817/ FPR = 0.308 and TPR = 1.000/ FPR = 0.206 for models one and two, respectively. The study quantified same-donor human hand odor profile variation over time displayed within a larger goal of determining sample source origin.

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 534subjects and their 1690 odor traces show the significant superiority of the indirect methods, especially without dimensionality reduction, be it with or without feature selection.

Are human symptoms and Zebra dove behaviors temporally related to odor perception in a newly industrialized region of Southern Thailand? A time-series study

Odor emission from a large gas separation plant in Songkhla, Thailand, has been claimed by local residents to disturb their health and to have adverse effects on their Zebra doves, which are reared for cooing competition and commerce, although there is no proof that odor emission is the real cause. This study was designed to verify if any association exists among odor perception, human health symptoms, and Zebra dove behaviors. Data were collected from daily reports of three resident groups including odor perception (n = 40), human health symptoms (n = 40), and Zebra dove behaviors (n = 39). Enrolled subjects comprised residents of location-matched triplets comprising one individual of each group. Mixed-effects random-intercept regression analyses among these measures over time (90 days) within triplet showed that lag 0–lag 3 gas odor was significantly positively associated with reported atypical Zebra dove behaviors (OR range: 1.84–3.85). On the other hand, oil, rotting food, and sulfide odor were significantly associated with reported human health symptoms (OR range: 1.83–13.80). The claims of adverse effect of gas odor on the health and behavior of residents’ doves and that of oil odor on human health are supported, although, evidence is lacking on the definitive sources of these odors.

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

A new method for identifying people by their odor is proposed. In this approach, subjects are characterized by a GC × GC–MS chromatogram of a sample of their hand odor. The method is based on the definition of a distance between odor chromatograms and the application of Bayesian hypothesis testing. Using a calibration panel of subjects for whom several odor chromatograms are available, the densities of the distance between chromatograms of the same person, and between chromatograms of different persons are estimated. Given the distance between a reference and a query chromatogram, the Bayesian framework provides an estimate of the probability that the corresponding two odor samples come from the same person. We tested the method on a panel that is fully independent from the calibration panel, with promising results for forensic applications.

Human odor and forensics. Optimization of a comprehensive two-dimensional gas chromatography method based on orthogonality: How not to choose between criteria

The use of comprehensive two-dimensional gas chromatography coupled with mass spectrometry would be a real asset for the forensic profiling of human hand odor. This paper focuses on the optimization of a comprehensive gas chromatography method using a synthetic mixture of 80 compounds representative of human hand odor composition. In order to rank the candidate column sets, instead of using a unique criterion, we used a chemometric tool called desirability which is based on Derringer functions and enables to consider several criteria simultaneously and hence to get the best compromise. Nine criteria including six orthogonality criteria were used to evaluate the quality and the efficiency of the separation. The desirability analysis lead to a straightforward ranking and an accurate overview of the results in two situations, with an objective of routine analysis and without. In both cases, the DB-1MS×DB-1701 set was found to be best suited for the separation of the considered mixture, however with different gradients.

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

Forensic profiling of human odor is challenging and would be useful to support information provided by dogs in courts of justice. Analyses of volatile compounds constitutive of human odor are commonly performed with gas chromatography coupled with mass spectrometry. All developed methods and sampling prototypes have to be easy to use in the field by crime scene investigators. This paper will focus on techniques for human hand odor sampling prior to analysis by a thermodesorption device coupled with gas chromatography and mass spectrometry. Thermodesorption and gas chromatography methods were developed using a sorbent phase spiked with a mixture of 80 compounds representative of human hand odor. Then, the crucial sampling step was performed indirectly with a homemade device based on air suction and trapping on a sorbent. This indirect sampling device was evaluated with the same synthetic mixture for optimization. An innovative polymer sorbent called Sorb-Star® was compared to classic Tenax TA® packed tubes. Sorb-Star® provided similar recovery to Tenax TA® packed tubes and a smaller pooled coefficient of variation (6 vs 13%). Thus, it appeared to be fully suited to the indirect sampling of human hand odor. The developed methods were successfully applied to real samples, the ultimate aim being the comparison of a suspect's sample to a sample collected from a crime scene.

Determination of VOC marker combinations for the classification of individuals by gender and race/ethnicity

Human scent has been demonstrated to allow for the differentiation of individuals. The extent of this differentiation may allow scent to be used as a characteristic, capable of revealing information on an individual’s traits. The ability to use scent as a biometric human feature has enabled the use of scent as forensic evidence. However, its reliability, value, and admissibility in courts of law have sometimes been challenged as a result of limited scientific validation. Full acceptance of human scent evidence has been hindered by the limited availability of peer reviewed information that provides greater understanding of how human scent originates and how it may be used. The objective of this study is to report new data that further enhances our understanding of the value of human scent evidence and its acceptance in courts of law. In order to fulfill this objective, volatile organic compound combinations were identified as markers for race/ethnicity and gender from hand odor samples from 105 individuals. The accuracy of human classification by race/ethnicity and gender, on the basis of these markers, was determined and validated. Overall accuracies of 80% and 72% were obtained for the classification of subjects by gender and race/ethnicity, respectively. The results obtained demonstrate that the identified VOC marker combinations represent a viable resource for the classification and/or differentiation of persons of interest and, as such, may be considered a valuable forensic tool for subject identification.

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Applicability of emanating volatile organic compounds from various forensic specimens for individual differentiation

Trace biological materials contain volatile profiles that have yet to be evaluated to determine their value in forensic investigations. The volatiles released by different biological specimens (hand odor, hair, fingernails and saliva) collected from twenty individuals were identified using a solid phase microextraction–gas chromatography–mass spectrometry method. The human scent compounds from each specimen, per subject, were evaluated using Spearman rank correlation to assess the applicability of these compounds for the differentiation of individuals. The volatile organic compounds from each specimen type were readily identified and discriminated. When conducting inter-subject discrimination within a single specimen type, greater than 98.9% of the samples, or individuals, were differentiated for all specimen types. When conducting inter-subject discrimination among the four specimen types 99.6% of the samples were differentiated, at the 0.9 correlation coefficient threshold. Additionally, the only occurrence of cross-correlation between specimen types was observed between hair and fingernails while there were no cross-correlations with hand odor or saliva thereby demonstrating the distinctiveness of these specimens.

Comparison of the Volatile Organic Compounds from Different Biological Specimens for Profiling Potential*

Previous work has demonstrated the ability to differentiate individuals based on the analysis of human scent hand odor chemicals. In this paper, a range of forensic biological specimens are shown to also have the ability to differentiate individuals based upon the volatile organic compounds (VOCs) present. Human VOC profiles from hand odor, oral fluid, breath, blood, and urine of 31 individuals were analyzed by solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) and combined methods of chromatogram comparison, Spearman rank correlation comparison, and principal component analysis. Intra-specimen comparisons demonstrated the distinguishability of individuals above 99%. Inter-specimen VOC profiles from the same individual were found to be too different to be used for scent-matching purposes, with Spearman rank coefficients below 0.15. A 6-month VOC profile monitoring of two individuals demonstrated the consistency of VOC profiles over time across specimens.

Do Trained Dogs Discriminate Individual Body Odors of Women Better than Those of Men?*

Scent identification lineups using dogs are a potentially valuable forensic tool, but have been dismissed by some critics because of cases where a false identification was shown to have occurred. It is not known, however, why dogs appear to make more false indications to the odors of some persons than of others. In this study, human genders were compared as to the degree their individual odors are distinguishable or "attractive" to dogs. Six dogs were trained to smell an individual's hand odor sample and then find the matching hand odor sample in a lineup of five odors. Using one-gender lineups and two-gender lineups with different gender ratios, it was found that dogs trained for the study identified individual women's hand odors more accurately than those of men. It is hypothesized that this is either because of differences in chemical compounds making discrimination of women's odors easier, or because of greater "odor attractiveness" of women's scents to dogs.

基于BP网络的手部气味识别<br>A Recognition Model of Hand Odor Based on BP Artificial Network

本文建立了一个基于BP人工神经网络的手部气味识别模型，并对网络参数进行了选择优化。人体手部气味经样品采集、浓缩后，用气相色谱–质谱联用仪对其进行分析获得手部气味轮廓图，并利用逐步判别分析法提取了手部气味轮廓图的47个特征变量(有机化合物)。在此基础上，构建了一个47 × 10 × 1的BP网络，其隐含层和输出层的传递函数分别为tansig和logsig，训练函数为trainrp。该网络能正确区分不同性别的手部气味。 A recognition model has been developed based on Back Propagation Artificial Network and its net parame- ters were optimized. Hand odors were sampled, concentrated and then analyzed with Chromatography-Mass Spectrometer, resulting in hand odor profiles whose feature variables (47 organic compounds) were extracted and reduced by means of stepwise discriminant analysis. In sequence, a BP network, in which the structure was 47 × 10 × 1, the transfer functions for hidden layer and output layer tansig and logsig, respectively, and the training function trainrp, was pro- posed. The experiment demonstrated that the network was able to specify whether an odor sample had been from man hand or woman hand.

Laboratory evaluation of lactic acid on attraction of Culex spp. (Diptera: Culicidae)

The role of lactic acid was evaluated for attraction of Culex nigripalpus, Culex quinquefasciatus, Culex tarsalis, and Aedes aegypti in the laboratory using a dual-port olfactometer. When lactic acid was combined with chicken odor, attraction was increased for Cx. quinquefasciatus compared to chicken odor alone but not for Cx. nigripalpus, Cx. tarsalis, and Ae. aegypti. Lactic acid combined with hand odor did not change attraction of Cx. tarsalis and Ae. aegypti but decreased attraction of Cx. nigripalpus and Cx. quinquefasciatus. The addition of lactic acid to CO(2) increased attraction of Ae. aegypti and Cx. quinquefasciatus but reduced attraction of Cx. nigripalpus and Cx. tarsalis. Use of commercial lactic acid baits with CO(2) resulted in a similar trend except for Cx. nigripalpus which showed no difference. A blend of lactic acid, acetone, and dimethyl disulfide was attractive to Ae. aegypti (63.4%) but elicited low responses by all Culex spp. (1.3-26.8%). Addition of the blend to CO(2) increased attraction of Ae. aegypti and Cx. quinquefasciatus but reduced attraction of Cx. nigripalpus and Cx. tarsalis. The mixture of compounds plus CO(2) was as attractive as a hand for Cx. quinquefasciatus, Cx. tarsalis, and Ae. aegypti.

The Differentiation of the Volatile Organic Signatures of Individuals Through SPME‐GC/MS of Characteristic Human Scent Compounds

Human scent evidence is utilized as an investigative tool through canine scent discriminations based on the premise that human scent is an individualizing characteristic. This study describes the development of what is effectively a human scent barcode consisting of the relative ratios of an individual's "primary odor" compounds utilized to determine a reproducible and individualizing profile which can be stored in a searchable database for a proof of concept of human scent as a biometric measure. Triplicate hand odor samples were evaluated from 10 subjects utilizing solid phase micro-extraction gas chromatography/mass spectrometry (SPME-GC/MS) and compared via Spearman Rank Correlations. Narrowing the compounds considered for each subject to only those common in all three samples, or a subject's "primary odor constituents," produced a greater degree of both individualization and discrimination; at both correlation thresholds of 0.9 and 0.8, the individuals were correctly discriminated and identified in 99.54% of the cases.

The Stability of Collected Human Scent Under Various Environmental Conditions*,†

Human scent evidence collected from objects at a crime scene is used for scent discrimination with specially trained canines. Storage of the scent evidence is usually required yet no optimized storage protocol has been determined. Storage containers including glass, polyethylene, and aluminized pouches were evaluated to determine the optimal medium for storing human scent evidence of which glass was determined to be the optimal storage matrix. Hand odor samples were collected on three different sorbent materials, sealed in glass vials and subjected to different storage environments including room temperature, -80 degrees C conditions, dark storage, and UVA/UVB light exposure over a 7-week period. Volatile organic compounds (VOCs) in the headspace of the samples were extracted and identified using solid-phase micro-extraction-gas chromatography/mass spectrometry (SPME-GC/MS). Three-dimensional covariance mapping showed that glass containers subjected to minimal UVA/UVB light exposure provide the most stable environment for stored human scent samples.

The frequency of occurrence and discriminatory power of compounds found in human scent across a population determined by SPME-GC/MS

The composition of human scent collected from the hands is of interest to the medical community as a mechanism to diagnose disease and the forensic community as a means to investigate canine scent discriminations. An extensive survey of the volatile organic compounds (VOCs) identified in the headspace of hand odor samples utilizing solid phase micro-extraction gas chromatography/mass spectrometry (SPME-GC/MS) has been conducted to determine the constituents of the human base odor profile. Sixty-three compounds were extracted from the collected odor samples. The composition included acids, alcohols, aldehydes, hydrocarbons, esters, ketones and nitrogen-containing compounds. The majority of the compounds detected (79.4%) were present in less than one third of the individuals sampled. Spearman correlation coefficient comparisons at a match/no-match threshold of 0.9 produced a distinguish ability of 99.67% across the population.