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

Are We Ready for It? A Review of Forensic Applications and Readiness for Comprehensive Two-Dimensional Gas Chromatography in Routine Forensic Analysis

Comprehensive two-dimensional gas chromatography (GC×GC) has been explored in forensic research to provide advanced chromatographic separation for forensic evidence, including illicit drugs, fingerprint residue, chemical, biological, nuclear, and radioactive (CBNR) substances, toxicological evidence, odor decomposition, and petroleum analysis for arson investigations and oil spill tracing. In GC×GC, the separation and analysis of analytes is similar to one-dimensional GC, but the primary column is connected to a secondary column via a modulator to provide two independent separation mechanisms, thus increasing the peak capacity of the analysis. The goal of implementing GC×GC in forensic studies is often to increase the separation and detectability of analytes and has most often been applied in nontargeted forensic applications where a wide range of analytes must be analyzed simultaneously. To date, there has been no summary of the current state of forensic research that evaluates both analytical and legal readiness for routine use. For these analytical methods to be adopted into forensic laboratories and be used in evidence analysis, they must meet rigorous analytical standards. In addition, new analytical methods for evidence analysis must adhere to standards laid out by the legal system, including the Frye Standard, Daubert Standard, and Federal Rule of Evidence 702 in the United States and the Mohan Criteria in Canada. Current research on GC×GC use for forensic applications was summarized and reviewed for analytical advances and technology readiness to provide a comprehensive view of GC×GC use for future routine implementation. A technology readiness scale, with levels from 1 to 4, was used to characterize the advancement of research in each individual application area. Seven forensic chemistry applications are discussed related to courtroom criteria and categorized into technology readiness levels based on current literature as of 2024. Future directions for all applications should place a focus on increased intra- and inter-laboratory validation, error rate analysis, and standardization.

Analysis of the Human Scent on Fired Cartridge Cases from a Simulated Crime Scene

Fired cartridge cases are often found at crime scenes connected with a shooting, and their prompt analysis can be very useful for the police investigation. In addition to dactyloscopy (fingerprints) that tends to be more or less damaged on the cartridges and often are not adequate for individual identification, there are also scent traces on the fired cartridges that are not fully destroyed by the gun's being fired. In this pilot study, we compare the human scent remaining on cartridge cases after firing with scent samples from different volunteers to find out who loaded the gun before the gun was shot. In this experiment, a simulated crime scene was prepared, and one of our volunteers loaded the weapon. Analysis of the scent remaining on cartridge cases was carried out using two different methods, namely, olfactronics and olfactorics.

Method development of comprehensive two-dimensional liquid chromatography: A new metric for preliminary column selection

To elevate the separation performance, two-dimensional liquid chromatography (2D-LC) uses two chromatographic columns with different stationary phases to diversify solute interactions with the resin, hence providing a second "dimension" to solute-specific separation. Developing methods for 2D-LC starts therefore with preliminary column selection. Selecting columns that yield (metaphorically) orthogonal dimensions is of utmost importance, but remains challenging. Although several metrics exist to quantify column orthogonality, currently there is no established methodology, and none of the existing methods accounts for the non-homogeneity of peak band broadening across each separation dimension. In this work, we propose a new approach to select columns a priori. This approach is based on critical resolution distribution statistics and implicitly accounts for local peak crowding and peak band broadening. Furthermore, we assess the importance of preliminary column selection during in-silico method development and multi-objective optimization of comprehensive 2D-LC. The comparison of the multi-objective Pareto fronts revealed that column pairs selected with our approach provide better separation quality and reduce analysis time compared to column selections via the most established metrics in the literature. Our results prove the importance of preliminary column selection for method development and optimization of 2D-LC systems, and they also show that choosing the right orthogonality metric (such as that proposed here) is crucial.

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.

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.

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.

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

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.

Non-invasive sorptive extraction for the separation of human skin surface chemicals using comprehensive gas chromatography coupled to time-of-flight mass spectrometry: A mosquito-host biting site investigation

Variation in inter-human attractiveness to mosquitoes, and the preference of mosquitoes to bite certain regions on the human host, are possible avenues for identifying lead compounds as potential mosquito attractants or repellents. We report a practical, non-invasive method for the separation and detection of skin surface chemical compounds and comparison of skin chemical profiles between the ankle and wrist skin surface area sampled over a 5-day period of a human volunteer using comprehensive gas chromatography coupled to time-of-flight mass spectrometry. An in-house made polydimethylsiloxane passive mini-sampler, worn as an anklet or a bracelet, was used to concentrate skin volatiles and semi-volatiles prior to thermal desorption directly in the gas chromatography. A novel method for the addition of an internal standard to sorptive samplers was introduced through solvent modification. This approach enabled a more reliable comparison of human skin surface chemical profiles. Compounds that were closely associated with the wrist included 6-methyl-1-heptanol, 3-(4-isopropylphenyl)-2-methylpropionaldehyde, 2-phenoxyethyl isobutyrate, and 2,4,6-trimethyl-pyridine. Conversely, compounds only detected on the ankle region included 2-butoxyethanol phosphate, 2-heptanone, and p-menthan-8-ol. In addition to known human skin compounds we report two compounds, carvone and (E)-2-decenal, not previously reported. Limits of detection ranged from 1 pg (carvone) to 362 pg (indole).

Chemical profiling of the human skin surface for malaria vector control via a non-invasive sorptive sampler with GC×GC-TOFMS

Volatile organic compounds (VOCs) and semi-VOCs detected on the human skin surface are of great interest to researchers in the fields of metabolomics, diagnostics, and skin microbiota and in the study of anthropophilic vector mosquitoes. Mosquitoes use chemical cues to find their host, and humans can be ranked for attractiveness to mosquitoes based on their skin chemical profile. Additionally, mosquitoes show a preference to bite certain regions on the human host. In this study, the chemical differences in the skin surface profiles of 20 human volunteers were compared based on inter-human attractiveness to mosquitoes, as well as inter- and intra-human mosquito biting site preference. A passive, non-invasive approach was followed to sample the wrist and ankle skin surface region. An in-house developed polydimethylsiloxane (PDMS) passive sampler was used to concentrate skin VOCs and semi-VOCs prior to thermal desorption directly in the GC inlet with comprehensive gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). Compounds from a broad range of chemical classes were detected and identified as contributing to the differences in the surface skin chemical profiles. 5-Ethyl-1,2,3,4-tetrahydronaphthalene, 1,1'-oxybisoctane, 2-(dodecyloxy)ethanol, α,α-dimethylbenzene methanol, methyl salicylate, 2,6,10,14-tetramethylhexadecane, 1,2-benzenedicarboxylic acid, bis(2-methylpropyl) ester, 4-methylbenzaldehyde, 2,6-diisopropylnaphthalene, n-hexadecanoic acid, and γ-oxobenzenebutanoic acid ethyl ester were closely associated with individuals who perceived themselves as attractive for mosquitoes. Additionally, biological lead compounds as potential attractants or repellants in vector control strategies were tentatively identified. Results augment current knowledge on human skin chemical profiles and show the potential of using a non-invasive sampling approach to investigate anthropophilic mosquito-host interactions. Graphical abstract.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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.