GC × GC–TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures

Volabolomic Fingerprinting for Post-Mortem Interval Estimation: A Novel Physiological Approach

Death is a multifaceted process wherein each individual cell and tissue has a metabolic homeostasis and a time of functional cessation defined by the dying process as well as by intrinsic and extrinsic factors. Decomposition is physiologically associated with the release of different types of volatile organic compounds (VOCs), and these form volaboloma mortis. The main purpose of this study was to record the volabolomic fingerprint produced by volatile molecules during the physiological decomposition process of human tissue and muscle cells. The volatile chemical signature has important implications for an open issue in forensics and pathology, namely the estimation of the postmortem interval (PMI), which decreases in accuracy with the passage of time. Volatile metabolites emitted from human tissues and muscle cells at 0, 24, 48, and 72 h were recorded in real time with an electronic nose sensor device. The key findings were the continuous sampling of VOCs emitted from tissues and cells. These showed a common behavior as time progressed; particularly, after 48 h the distributions became dispersed, and after 72 h they became more variable. Volabolomic fingerprinting associated with time progression relevant to the study of PMIs was reconstructed. Additionally, there may be broader applications, such as in dog training procedures for detecting human remains, and perhaps even for studying scavenger and insect attractants.

The smell of death. State-of-the-art and future research directions

The decomposition of a body is inseparably associated with the release of several types of odors. This phenomenon has been used in the training of sniffer dogs for decades. The odor profile associated with decomposition consists of a range of volatile organic compounds (VOCs), chemical composition of which varies over time, temperature, environmental conditions, and the type of microorganisms, and insects colonizing the carcass. Mercaptans are responsible for the bad smell associated with corpses; however, there are no unified recommendations for conducting forensic analysis based on the detectable odor of revealed corpses and previous research on VOCs shows differing results. The aim of this review is to systematize the current knowledge on the type of volatile organic compounds related to the decomposition process, depending on a few variables. This knowledge will improve the methods of VOCs detection and analysis to be used in modern forensic diagnostics and improve the methods of training dogs for forensic applications.

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical "-omics" Data with Multiple Explanatory Variables

Biological volatilome analysis is inherently complex due to the considerable number of compounds (i.e., dimensions) and differences in peak areas by orders of magnitude, between and within compounds found within datasets. Traditional volatilome analysis relies on dimensionality reduction techniques which aid in the selection of compounds that are considered relevant to respective research questions prior to further analysis. Currently, compounds of interest are identified using either supervised or unsupervised statistical methods which assume the data residuals are normally distributed and exhibit linearity. However, biological data often violate the statistical assumptions of these models related to normality and the presence of multiple explanatory variables which are innate to biological samples. In an attempt to address deviations from normality, volatilome data can be log transformed. However, whether the effects of each assessed variable are additive or multiplicative should be considered prior to transformation, as this will impact the effect of each variable on the data. If assumptions of normality and variable effects are not investigated prior to dimensionality reduction, ineffective or erroneous compound dimensionality reduction can impact downstream analyses. It is the aim of this manuscript to assess the impact of single and multivariable statistical models with and without the log transformation to volatilome dimensionality reduction prior to any supervised or unsupervised classification analysis. As a proof of concept, Shingleback lizard (Tiliqua rugosa) volatilomes were collected across their species distribution and from captivity and were assessed. Shingleback volatilomes are suspected to be influenced by multiple explanatory variables related to habitat (Bioregion), sex, parasite presence, total body volume, and captive status. This work determined that the exclusion of relevant multiple explanatory variables from analysis overestimates the effect of Bioregion and the identification of significant compounds. The log transformation increased the number of compounds that were identified as significant, as did analyses that assumed that residuals were normally distributed. Among the methods considered in this work, the most conservative form of dimensionality reduction was achieved through analyzing untransformed data using Monte Carlo tests with multiple explanatory variables.

Fisher ratio feature selection by manual peak area calculations on comprehensive two-dimensional gas chromatography data using standard mixtures with variable composition, storage, and interferences

Comprehensive two-dimensional gas chromatography (GC×GC) is becoming increasingly more common for non-targeted characterization of complex volatile mixtures. The information gained with higher peak capacity and sensitivity provides additional sample composition information when one-dimensional GC is not adequate. GC×GC generates complex multivariate data sets when using non-targeted analysis to discover analytes. Fisher ratio (FR) analysis is applied to discern class markers, limiting complex GC×GC profiles to the most discriminating compounds between classes. While many approaches for feature selection using FR analysis exist, FR can be calculated relatively easily directly on peak areas after any native software has performed peak detection. This study evaluated the success rates of manual FR calculation and comparison to a critical F-value for samples analyzed by GC×GC with defined concentration differences. Long-term storage of samples and other spiked interferences were also investigated to examine their impact on analyzing mixtures using this FR feature selection strategy. Success rates were generally high with mostly 90-100% success rates and some instances of percentages between 80 and 90%. There were rare cases of false positives present and a low occurrence of false negatives. When errors were made in the selection of a compound, it was typically due to chromatographic artifacts present in chromatograms and not from the FR approach itself. This work provides foundational experimental data on the use of manual FR calculations for feature selection from GC×GC data.

Top-Down Approach to Retention Time Prediction in Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry

In this contribution, we describe a novel modeling approach to predicting retention times (t_r) in comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-ToF-MS) with a particular emphasis on the second-dimension (²D) retention time predictions (²t_r). This approach is referred to as a "top-down" approach in that it breaks down the complete GC × GC separation into two independent one-dimensional gas chromatography separations (1D-GC). In this regard, both dimensions, that is, first dimension (¹D) and second dimension (²D) are treated separately, and the cryogenic modulator is simply considered as a second consecutive injection device. Separate 1D-GC t_r predictions are performed on both dimensions using the same flow rate as the one deployed in the conventional GC × GC system. The separate t_r predictions are then combined to account for the two-dimensional separation. This model was applied to 24 analytes from 2 standard mixtures (Grob Test Mix and Fragrance Materials Test Mix) and assessed across 9 GC × GC chromatographic conditions. The experimental and predicted chromatographic retention space occupations were assessed by using the convex hull approach defined by the Delaunay triangulation. The predicted percentage of space occupation corresponded favorably with the experimental values. Furthermore, the top-down approach enabled an accurate prediction of the ²t_r of all investigated analytes, providing an average ²t_r modeling error of 0.26 ± 0.01 s.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Detecting volatile organic compounds to locate human remains in a simulated collapsed building

The occurrence of mass disasters has increased worldwide due to changing environments from global warming and a heightened threat of terrorism acts. When these disasters strike, it is imperative to rapidly locate and recover human victims, both the living and deceased. While search and rescue dogs are used to locate the living, cadaver detection dogs are typically tasked with locating the dead. This can prove challenging because commingling of victims is likely to occur during disasters in populated areas which will impact the decomposition process and the resulting odour produced. To date, there has been no research to investigate the process of human decomposition in a mass disaster scenario or to understand which compounds are detectable by cadaver detection dogs. Hence, the current study investigated the human decomposition process and subsequent volatile organic compound (VOC) production in two simulated building collapse scenarios with six human donors placed in each scenario. The human remains were only recovered after a period of one month, during which time VOC samples were collected and analysed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. A considerable degree of differential decomposition was observed upon recovery of the human remains, which was carried out as a part of a police disaster victim recovery training exercise. The location of the bodies in the disaster area was found to impact the decomposition process. The VOC profile was found to correlate with the decomposition process. Fifteen days following the simulated disaster, the VOC profile changed showing that a detectable change in the decomposition process had occurred. Overall, the changing VOC profile can inform the training of cadaver detection dogs for these unique scenarios.

A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

A non-targeted data processing workflow for volatile organic compound data acquired using comprehensive two-dimensional gas chromatography with dual channel detection

There has been an influx of technology for comprehensive two-dimensional gas chromatography analyses in recent years, calling for development of guided workflows and rigorous reporting of processes. This research focuses on the processing method for data collected on a dual channel detection system using flame ionization detection (FID) and quadrupole mass spectrometry (qMS) for the analysis of volatile organic compounds (VOCs). The samples analyzed were kava (Piper methysticum), which has a rich VOC profile that benefits substantially from a multidimensional approach due to enhanced peak capacity. The procedure which was customized here was the data processing workflow from a manual single-sample analysis to an integrated batch workflow that can be applied across studies.•Parameter choice for baseline correction and peak detection were defined when handling batch data.•Elution regions were defined using qMS data to automate compound identification.•Stencils were transformed onto FID data and sequenced for quantitative information.This dataset can be used as a training tool, as all details, methods and results for the workflow have been provided for users to compare with. The focus on data workflow reproducibility in the field of multidimensional chromatography will assist in adoption by users in new application areas.

Cadaver Dogs and the Deathly Hallows-A Survey and Literature Review on Selection and Training Procedure

Human remains detection dogs (HRDDs) are powerful police assets to locate a corpse. However, the methods used to select and train them are as diverse as the number of countries with such a canine brigade. First, a survey sent to human remains searching brigades (Ncountries = 10; NBrigades = 16; NHandlers = 50; Nquestions = 9), to collect their working habits confirmed the lack of optimized selection and training procedures. Second, a literature review was performed in order to outline the strengths and shortcomings of HRDDs training. A comparison between the scientific knowledge and the common practices used by HRDDs brigade was then conducted focusing on HRDDs selection and training procedures. We highlighted that HRDD handlers select their dogs by focusing on behavioral traits while neglecting anatomical features, which have been shown to be important. Most HRDD handlers reported to use a reward-based training, which is in accordance with training literature for dogs. Training aids should be representative of the odor target to allow a dog to reach optimal performances. The survey highlighted the wide diversity of homemade training aids, and the need to optimize their composition. In the present document, key research topics to improve HRDD works are also provided.

Comparison of the effect of chemically and biologically induced inflammation on the volatile metabolite production of lung epithelial cells by GC×GC-TOFMS

Exhaled breath analysis has a high potential for early non-invasive diagnosis of lung inflammatory diseases, such as asthma. The characterization and understanding of the inflammatory metabolic pathways involved into volatile organic compounds (VOCs) production could bring exhaled breath analysis into clinical practice and thus open new therapeutic routes for inflammatory diseases. In this study, lung inflammation was simulated in vitro using A549 epithelial cells. We compared the VOC production from A549 epithelial cells after a chemically induced oxidative stress in vitro, exposing the cells to H₂O₂, and a biological stress, exposing the cells to an inflammatory pool of sputum supernatants. Special attention was devoted to define proper negative and positive controls (8 different types) for our in vitro models, including healthy sputum co-culture. Sputum from 25 asthmatic and 8 healthy patients were collected to create each pool of supernatants. Each sample type was analyzed in 4 replicates using solid-phase microextraction (SPME) comprehensive two-dimensional gas chromatography hyphenated to time-of-flight mass spectrometry (GC×GC-TOFMS). This approach offers high resolving power for complex VOC mixtures. According to the type of inflammation induced, significantly different VOCs were produced by the epithelial cells compared to all controls. For both chemical and biological challenges, an increase of carbonyl compounds (54%) and hydrocarbons (31%) was observed. Interestingly, only the biological inflammation model showed a significant cell proliferation together with an increased VOC production linked to asthma airway inflammation. This study presents a complete GC×GC-TOFMS workflow for in vitro VOC analysis, and its potential to characterize complex lung inflammatory mechanisms.

Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis

The ability to discover minute differences between samples or sample classes for gas chromatography coupled to mass spectrometry (GC-MS) can be a challenging endeavor, especially when those differences are not a priori. Fisher ratio (F-ratio) analysis is an apt technique to probe the differences between GC-MS chromatograms. F-ratio analysis is a supervised, non-targeted, discovery-based method that compares two different samples (or sample classes) to reduce the GC-MS dataset into a hit list composed of class distinguishing compounds. Three different F-ratio techniques, peak table, tile, and pixel-based were used to "discover" nine non-native analytes that were spiked into gasoline at four different nominal concentrations of 250, 85, 25, 5 parts-per-million (ppm). For the tile and pixel-based F-ratio calculations, a novel methodology is introduced to improve the sensitivity of the F-ratio calculations while reducing false positives. Furthermore, we use a combinatorial technique using null class comparisons, termed null distribution analysis, to determine a statistical F-ratio cutoff for analysis of the hit lists. The pixel-based algorithm was the most sensitive method and was able to "discover" all nine spiked analytes at a nominal concentration of 250 ppm albeit with one false positive interspersed towards the bottom of the hit list. The pixel-based software was also able to "discover" more of the spiked analytes at the lower concentrations with seven of the spiked analytes "discovered" at 85 ppm, four of the spiked analytes "discovered" at 25 ppm, and one analyte "discovered" at 5 ppm.

Behavioral and Electrophysiological Responses of the Fringed Larder Beetle Dermestes frischii to the Smell of a Cadaver at Different Decomposition Stages

A cadaver is colonized by a wide diversity of necrophagous insects. It is well documented that Dipterans are attracted by the volatile organic compounds (VOCs) released by a corpse during the first minutes following death. Coleopterans are known to be attracted by highly decomposed cadavers, but have received less attention regarding the olfaction-based mechanisms underlying these interactions. In the present study, we impregnated gauzes with VOCs collected from each decomposition stage of dead rats: fresh, bloated, active, and advanced decay. We collected the VOCs released by the gauze and confirmed what was previously know from the literature: the decomposition stages are associated with contrasting chemical profiles. We exposed Dermestes frischii Kugelann (Coleoptera: Dermestidae) male and female antennae to the same gauzes and found that stronger electrical responses were recorded when using the smell of the advanced decay stage. Finally, we performed two choices behavioral assays. Females showed no preference for the four decomposition stages, while males were attracted by the smell associated with active and advanced decay stages. These results suggest that specific VOCs released by a decaying body guide necrophagous coleopterans to their feeding site. Whether D. frischii males release pheromones to attract females remains to be tested.

Volatile Organic Compound Profiling from Postmortem Microbes using Gas Chromatography-Mass Spectrometry

Volatile organic compounds (VOCs) are by-products of cadaveric decomposition and are responsible for the odor associated with decomposing remains. The direct link between VOC production and individual postmortem microbes has not been well characterized experimentally. The purpose of this study was to profile VOCs released from three postmortem bacterial isolates (Bacillus subtilis, Ignatzschineria indica, I. ureiclastica) using solid-phase microextraction arrow (SPME Arrow) and gas chromatography-mass spectrometry (GC-MS). Species were inoculated in headspace vials on Standard Nutrient Agar and monitored over 5 days at 24°C. Each species exhibited a different VOC profile that included common decomposition VOCs. VOCs exhibited upward or downward temporal trends over time. Ignatzschineria indica produced a large amount of dimethyldisulfide. Other compounds of interest included alcohols, aldehydes, aromatics, and ketones. This provides foundational data to link decomposition odor with specific postmortem microbes to improve understanding of underlying mechanisms for decomposition VOC production.

Comparison of Pre-Processing and Variable Selection Strategies in Group-Based GC×GC-TOFMS Analysis

Chemometric analysis of comprehensive two-dimensional chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) data has been reported with various workflows, yet little effort has been devoted to evaluating the impacts of workflow variation on study conclusions. The report presented herein aims to investigate the effects of different pre-processing and variable selection strategies on the scores’ plot outputs from GC×GC-TOFMS data acquired from lavender and tea tree essential oils. Our results suggest that pre-processing, such as applying log transformation to the data set, can result in significant differentiation of sample clustering when compared to only mean centering. Additionally, exploring differences between analysis of variance, Fisher-ratio, and partial least squares-discriminant analysis feature selection resulted in little variation in scores plots. This work highlights the effects different chemometric workflows can have on results to help facilitate harmonization efforts.

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.

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.

Advanced method optimization for volatile aroma profiling of beer using two-dimensional gas chromatography time-of-flight mass spectrometry

The complex mixture of volatile organic compounds (VOCs) present in the headspace of Trappist and craft beers was studied to illustrate the efficiency of thermal desorption (TD) comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for highlighting subtle differences between highly complex mixtures of VOCs. Headspace solid-phase microextraction (HS-SPME), multiple (and classical) stir bar sorptive extraction (mSBSE), static headspace (SHS), and dynamic headspace (DHS) were compared for the extraction of a set of 21 representative flavor compounds of beer aroma. A Box-Behnken surface response methodology experimental design optimization (DOE) was used for convex hull calculation (Delaunay's triangulation algorithms) of peak dispersion in the chromatographic space. The predicted value of 0.5 for the ratio between the convex hull and the available space was 10% higher than the experimental value, demonstrating the usefulness of the approach to improve optimization of the GC×GC separation. Chemical variations amongst aligned chromatograms were studied by means of Fisher Ratio (FR) determination and F-distribution threshold filtration at different significance levels (α=0.05 and 0.01) and based on z-score normalized area for data reduction. Statistically significant compounds were highlighted following principal component analysis (PCA) and hierarchical cluster analysis (HCA). The dendrogram structure not only provided clear visual information about similarities between products but also permitted direct identification of the chemicals and their relative weight in clustering. The effective coupling of DHS-TD-GC×GC-TOFMS with PCA and HCA was able to highlight the differences and common typical VOC patterns among 24 samples of different Trappist and selected Canadian craft beers.

Present and foreseeable future of metabolomics in forensic analysis

The revulsive publications during the last years on the precariousness of forensic sciences worldwide have promoted the move of major steps towards improvement of this science. One of the steps (viz. a higher involvement of metabolomics in the new era of forensic analysis) deserves to be discussed under different angles. Thus, the characteristics of metabolomics that make it a useful tool in forensic analysis, the aspects in which this omics is so far implicit, but not mentioned in forensic analyses, and how typical forensic parameters such as the post-mortem interval or fingerprints take benefits from metabolomics are critically discussed in this review. The way in which the metabolomics-forensic binomial succeeds when either conventional or less frequent samples are used is highlighted here. Finally, the pillars that should support future developments involving metabolomics and forensic analysis, and the research required for a fruitful in-depth involvement of metabolomics in forensic analysis are critically discussed.

Establishing the volatile profile of pig carcasses as analogues for human decomposition during the early postmortem period

Following a mass disaster, it is important that victims are rapidly located as the chances of survival decrease greatly after approximately 48 h. Urban search and rescue (USAR) teams may use a range of tools to assist their efforts but detector dogs still remain one of the most effective search tools to locate victims of mass disasters. USAR teams can choose to deploy human scent dogs (trained to locate living victims) or human remains detection (HRD) dogs (trained to locate deceased victims). However, little is known about the variation between live human scent and postmortem human remains scent and the timeframe during which one type of scent transitions to the other. The aim of the current study was to measure the change in the scent profile of human decomposition analogues during the first 72 h postmortem by measuring the volatile organic compounds (VOCs) that comprise the odour. Three pig carcasses (Sus scrofa domesticus L.) were placed on a soil surface and allowed to decompose under natural conditions. Decomposition odour was sampled frequently up to 75 h postmortem and analysed using comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC×GC-TOFMS). A total of 105 postmortem VOCs were identified during the early postmortem period. The VOC profile during the early postmortem period was highly dynamic, changing both hourly and daily. A transition period was observed after 43 h postmortem, where the VOC profile appeared to shift from a distinct antemortem odour to a more generalised postmortem odour. These findings are important in informing USAR teams and their use of detector dogs for disaster victim recovery.