Characterization of Volatile Organic Compounds from Human Analogue Decomposition Using Thermal Desorption Coupled to Comprehensive Two-Dimensional Gas Chromatography–Time-of-Flight Mass Spectrometry

Development and validation of a classification model for boar taint detection in pork fat samples

This study aims to characterize a complete volatile organic compound profile of pork neck fat for boar taint prediction. The objectives are to identify specific compounds related to boar taint and to develop a classification model. In addition to the well-known androstenone, skatole and indole, 10 other features were found to be discriminant according to untargeted volatolomic analyses were conducted on 129 samples using HS-SPME-GC×GC-TOFMS. To select the odor-positive samples among the 129 analyzed, the selection was made by combining human nose evaluations with the skatole and androstenone concentrations determined using UHPLC-MS/MS. A comparison of the data of the two populations was performed and a statistical model analysis was built on 70 samples out of the total of 129 samples fully positive or fully negative through these two orthogonal methods for tainted prediction. Then, the model was applied to the 59 remaining samples. Finally, 7 samples were classified as tainted.

Lime and odour: A preliminary investigation into the effect of hydrated lime on the volatiles emitted from human remains

The location of human remains is performed primarily through the aid of cadaver detection dogs, which rely on the malodour produced through decomposition of decaying bodies. Malefactors will attempt to conceal these putrefactive odours through chemical additions such as lime, which is also wrongly believed to accelerate decomposition and prevent the identification of the victim. Despite the frequency of lime in forensic applications, to date no research has been performed to determine its effect on the volatile organic compounds (VOCs) released during human decomposition. This research was therefore conducted to ascertain the effects of hydrated lime on the VOC profile of human remains. Two human donors were used in a field trial at the Australian Facility for Taphonomic Experimental Research (AFTER): one donor was covered with hydrated lime, and the other had no chemical additions acting as a control. VOC samples were collected over a period of 100 days and analysed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). The volatile samples were accompanied by visual observations of how decomposition progressed. The results showed that lime application decreased the rate of decomposition and decreased total carrion insect activity. Lime increased the abundance of VOCs during the fresh and bloat stages of decay, however the abundance of compounds plateaued during active and advanced decomposition and were much lower than those detected from the control donor. Despite this suppression of VOCs, the study found that dimethyl disulfide and dimethyl trisulfide, key sulfur-containing compounds, were still produced in high quantities, and can thus still be used to locate chemically altered human remains. Knowledge of the effects of lime on human decomposition can inform the training of cadaver detection dogs, and ensure a greater chance at locating victims of crimes or mass disasters.

Catch me if you can-emission patterns of human bodies in relation to postmortem changes

The present study examines for the first time the emission patterns and olfactory signatures of 9 complete human corpses of different stages of decomposition. Air sampling was performed inside the body bags with solid sorbents and analysed by coupled gas chromatography-mass spectrometry after thermal desorption (TD-GC-MS). Furthermore, odour-related substances were detected by gas chromatography-olfactometry (GC-O). Sulfurous compounds (mainly dimethyl di- and trisulfide) were identified as most important to the odour perception. Around 350 individual organic substances were detected by TD-GC-MS, notably sulfurous and nitrogenous substances as well as branched alkanes, aldehydes, ketones, alcohols, carboxylic acids, carboxylic acid esters and ethers. A range of terpenes was detected for the first time in a characteristic emission pattern over all decomposition stages. Concentrations of the substances varied greatly, and no correlation between the emission patterns, the stage of decomposition and the cause of death could be found. While previous studies often analysed pig cadavers or only parts of human tissue, the present study shows the importance of analysing complete human corpses over a range of decomposition stages. Moreover, it is shown that using body bags as a kind of "emission test chamber" is a very promising approach, also because it is a realistic application considering the usual transport and store of a body before autopsy.

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.

Investigating analyte breakthrough under non-linear isotherm conditions during solid phase extraction facilitated by non-targeted analysis with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Solid phase extraction (SPE) sample preparation for the analysis of complex organic mixtures is often applied assuming all analytes of interest will preconcentrate on the stationary phase. This assumption ignores the reality that extraction is a dynamic interactive process and a diverse range of affinities for the stationary phase will result in equally diverse breakthrough volumes due to competitive sorption processes. To study this dynamic interactive process, and further to take advantage of it, we extracted a JP-8 jet fuel spiked with 40 ppm of a polar compound mix with silica and alumina SPE cartridges and analyzed sequential extracted fractions of the fuel to both assess the shifting chemical landscape present in the extraction and the impact of both SPE stationary phases on this process. Tile-based 1v1 comparative analysis (a recently reported extension of tile-based Fisher ratio analysis) was used to discover the (polar) compounds whose concentrations change between extracted fractions, discovering 21 compounds extracted with silica and 27 compounds extracted with alumina with at least a 2-fold change in concentration from the neat sample relative to the first 1 mL pass fraction sample. These compounds were quantified in each fraction to construct concentration ratio profiles, defined as the concentration ratio for a given SPE fraction per analyte compound relative to the analyte concentration in the neat fuel, for which the extraction behavior for each analyte could be assessed. These analyte compounds were found to breakthrough at different rates, with some analytes remaining on the column indefinitely (until extracted with a subsequent polar solvent) and other analytes eluting before the extraction is complete. Furthermore, in a comparison of the effect of selected stationary phase, alumina was found to retain oxygen-containing phenolic compounds to a greater extent than silica. Principal component analysis (PCA) was used to analyze the concentration ratio profiles of the various trace analytes in the JP8 fuel (phenols, indoles, etc.) in the context of their stationary phase affinity (silica or alumina) and competitive sorption behavior.

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.

Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis

Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δ_r>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, α_e,2D, and within-class relative standard deviation in peak area, RSD_wc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δ_r>, α_e,2D, and RSD_wc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δ_r>, α_e,2D, and RSD_wc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.

A headspace collection chamber for whole body volatilomics

The human body secretes a complex blend of volatile organic compounds (VOCs) via the skin, breath and bodily fluids. In this study, we have developed a headspace collection chamber for whole body volatilome profiling.

Class comparison enabled mass spectrum purification for comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry

Tile-based Fisher ratio (F-ratio) analysis is emerging as a versatile data analysis tool for supervised discovery-based experimentation using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). None the less, analyte identification can often be marred by poor 2D resolution and low analyte abundance relative to overlapping compounds. Linear algebra-based chemometric methods, in particular multivariate curve resolution alternating least squares (MCR-ALS), parallel factor analysis (PARAFAC) and PARAFAC2, are often applied in an effort to address this situation. However, these chemometric methods can fail to produce an accurate spectrum when the analyte is at low 2D resolution and/or in low relative abundance. To address this challenge, we introduce class comparison enabled mass spectrum purification (CCE-MSP), a method that utilizes the underlying requirement for signal consistency of the background interference compounds between the two classes in the F-ratio analysis to purify the mass spectrum of the analyte hits. CCE-MSP is validated using a dataset obtained for a neat JP-8 jet fuel spiked with 14 sulfur containing compounds at two levels (15 ppm and 30 ppm), using the p-value and lack-of-fit (LOF) for each analyte hit as consistency metrics. A purified mass spectrum was produced for each spiked analyte hit and their mass spectrum match value (MV) was compared to the MV obtained by MCR-ALS, PARAFAC, and PARAFAC2. The resulting MV for CCE-MSP were found to be as good or better than these chemometric methods, eg., for 2-butyl-5-ethylthiophene with an analyte-to-interference relative signal abundance of 1:87 and a 2D resolution of 0.2, CCE-MSP produced a MV of 831, compared to 476 for MCR-ALS, 403 for PARAFAC, and 336 for PARAFAC2. CCE-MSP is also extended to obtain the purified spectrum for more than one analyte, eg., two analyte hits in overlapping hit locations. The spectra produced by CCE-MSP can also be utilized as estimates to facilitate quantitative signal decomposition using MCR-ALS.

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.

Exhaled breath analysis in disease detection

Investigating the use of exhaled breath analysis to diagnose and monitor different diseases has attracted much interest in recent years. This review introduces conventionally used methods and some emerging technologies aimed at breath analysis and their relevance to lung disease, airway inflammation, gastrointestinal disorders, metabolic disorders and kidney diseases. One section correlates breath components and specific diseases, whereas the other discusses some unique ideas, strategies, and devices to analyze exhaled breath for the diagnosis of some common diseases. This review aims to briefly introduce the potential application of exhaled breath analysis for the diagnosis and screening of various diseases, thereby providing a new avenue for the detection of non-invasive diseases.

Transformable fluorescent nanoparticles (TFNs) of amphiphilic block copolymers for visual detection of aromatic amines in water

A kind of novel transformable fluorescent nanoparticle made of block copolymers is constructed for the sensitive detection of aromatic amines in water.

Volatile compounds reveal age: a study of volatile organic compounds released by Chrysomya rufifacies immatures

Age determination of insects collected from vertebrate remains is an essential step in estimating time since colonization as related to the post-mortem interval. Long-established methods for making such estimates rely on determining age related to stage of development at the time of collection in relation to conditions experienced. However, such estimates are based on the completion of a stage of development. Methods allowing for more precise estimates of age (i.e., within a stage of development) are sorely needed. This study examined the potential of volatile organic compounds emitted by blow fly, Ch. rufifacies (Macquart), immatures to determine stage of development, which could potentially be used to estimate the age. Volatile organic compounds (VOCs) from the larval and pupal stages of Ch. rufifacies were collected by headspace solid-phase micro-extraction followed by gas chromatography-mass spectrometry (GC-MS). Analyses indicated 37 compounds shift quantitatively, as well as qualitatively, as the larvae and pupae age. Furthermore, compounds, such as 2-ethyl-1-hexanol, phenol, butanoic acid, hexadecanoic acid, octadecanoic acid, 2-methyl propanamide, and 2-methyl butanoic acid, serve as indicator compounds of specific stages within Ch. rufifacies development. This information could be important to determine if these compounds can be used in the field to predict the presence of certain developmental stages, in order to determine the potential of using volatile markers to estimate time of colonization.

Statistical inference of mass channel purity from Fisher ratio analysis using comprehensive two-dimensional gas chromatography with time of flight mass spectrometry data

Tile-based Fisher ratio (F-ratio) analysis has recently been developed and validated for discovery-based studies of highly complex data collected using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). In previous studies, interpretation and utilization of F-ratio hit lists has relied upon manual decomposition and quantification performed by chemometric methods such as parallel factor analysis (PARAFAC), or via manual translation of the F-ratio hit list information to peak table quantitative information provided by the instrument software (ChromaTOF). Both of these quantification approaches are bottlenecks in the overall workflow. In order to address this issue, a more automatable approach to provide accurate relative quantification for F-ratio analyses was investigated, based upon the mass spectral selectivity provided via the F-ratio spectral output. Diesel fuel spiked with 15 analytes at four concentration levels (80, 40, 20, and 10 ppm) produced three sets of two class comparisons that were submitted to tile-based F-ratio analysis to obtain three hit lists, with an F-ratio spectrum for each hit. A novel algorithm which calculates the signal ratio (S-ratio) between two classes (eg., 80 ppm versus 40 ppm) was applied to all mass channels (m/z) in the F-ratio spectrum for each hit. A lack of fit (LOF) metric was utilized as a measure of peak purity and combined with F-ratio and p-values to study the relationship of each of these metrics with m/z purity. Application of a LOF threshold coupled with a p-value threshold yielded a subset of the most pure m/z for each of the 15 spiked analytes, evident by the low deviations (< 5%) in S-ratio relative to the true concentration ratio. A key outcome of this study was to demonstrate the isolation of pure m/z without the need for higher level signal decomposition algorithms.

Translation of a One-Dimensional to a Comprehensive Two-Dimensional Gas Chromatography Method with Dual-Channel Detection for Volatile Organic Compound Measurement in Forensic Applications

After its introduction in the early 1990s, comprehensive two-dimensional gas chromatography (GC×GC) has evolved from a separation science research tool to the central component of many industries. Despite the maturity of the technique, some fields remain reluctant to its use in routine applications. In the case of forensic science, some constraints are the strict requirements enforced in forensic laboratories and the time and effort that must be invested for intralaboratory method validation. Concerns may also arise about whether information could be lost when transitioning to a new technique. This study reports on a method translation from conventional one-dimensional (1D) GC to GC×GC, ensuring the integrity of data as conversion is made. The GC was retrofitted with a reverse fill/flush (RFF) flow modulator and equipped with dual-channel detection using a quadrupole mass spectrometer (qMS) and a flame ionization detector (FID). The parallel use of two detectors, where qMS was applied for qualitative identification and FID for quantification, allowed higher flows and slightly wider peaks to be exploited for the analysis of a volatile organic compound (VOC) reference mixture relevant to forensic VOC profiling. Peak quality assessment and calibration curves using GC-qMS and GC×GC-qMS/FID document the transfer and adaptation of the original method without a loss in data quality. Furthermore, the preprocessing and the data analysis processing steps, including calibration and peak quality assessment for each of the three data sets, are explained in detail. This information provides benchmark data for routine laboratories that want to implement a GC×GC approach into routine workflows.

Profiling Volatilomes: A Novel Forensic Method for Identification of Confiscated Illegal Wildlife Items

Globally, the rapid decline in wildlife species has many causes. The illegal trafficking of fauna and flora is a major contributor to species decline and continues to grow at an alarming rate. To enable the prosecution of those involved in the trafficking of illegal wildlife, accurate and reliable identification is paramount. Traditionally, morphology and DNA amplification are used. This paper investigates a novel application of volatilome profiling using comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry for wildlife sample detection. Known samples of elephant-derived ivory, other dentine samples, and bone (a common ivory substitute) were used as reference samples for volatilome profiling. Subsequently, specimens that were suspected ivory from border control seizures were obtained and analysed. Confirmatory DNA analyses were conducted on seized samples to establish the reliability parameters of volatilome profiling. The volatilome method correctly identified six of the eight seized samples as elephant ivory, which was confirmed through DNA analysis. There was also clear distinction of African elephant ivory parts from the bone and dentine samples from other species, as shown through PCA and discriminant analyses. These preliminary results establish volatilome profiling through GC×GC-TOFMS as a novel screening method used for the identification of unknown wildlife contraband.

Self-Amplified Fluorescent Nanoparticles for Rapid and Visual Detection of Xylene in Aqueous Media

Pollutant detection is of great importance for quality control of drinking water and environmental protection. The common methods of pollutant detection suffer from time-consuming procedures, bulky and expensive instruments, and complicated sample pretreatment. Herein, a type of conceptually new self-amplified fluorescent nanoparticle (SAFN) is constructed based on aggregation-induced emission (AIE) luminogens for rapid and visual detection of xylene in aqueous media. AIE luminogens are self-assembled into SAFNs in aqueous media, which emit efficiently due to the aggregation of luminogen molecules. The SAFNs of AIE luminogens stick xylene molecules from aqueous media through multiple interactions including hydrophobic and π-π interactions. Upon capturing xylene, SAFNs swell, which quench the fluorescence of the whole SAFNs, showing the self-amplification effect. Such a self-amplification effect is entirely different from that of conjugated polymers in the literature. Importantly, fluorescence quenching of SAFNs by xylene can be readily observed by the naked eye, which enables visual xylene sensing. The SAFNs enable rapid and visual detection of xylene in aqueous media with a low detection limit (5 μg/L) in the order of seconds. Given high sensitivity, rapid response, simple and easy operation, and low cost, SAFNs of AIE luminogens present a promising platform for visual detection of organic pollutants in aqueous media.

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.

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.

Sensitive and rapid detection of aliphatic amines in water using self-stabilized micelles of fluorescent block copolymers

A kind of self-stabilized micelles of fluorescent block copolymers are constructed for rapid and sensitive detection of aliphatic amines in water based on capture-report strategy. An amphiphilic triblock copolymer functionalized with aggregation induced emission (AIE) chromophores self assembles into micelles with core-shell structures in aqueous solution. Hydrophobic AIE chromophores organize into cores, where hydrophobic interaction among the AIE chromophores inhibits the micelles from disassembling. The cores of AIE chromophores are surrounded by a corona of water-soluble polymer segments, endowing the micelles with superior dispersibility in water. Water-soluble polymer segments capture organic amines in water due to preferential hydrophobic interactions between them. The enriched amines in the corona subsequently diffuse into hydrophobic cores of micelles, quenching fluorescence of the AIE chromophores. The fluorescent micelles allow rapid detection of aliphatic amines in the order of seconds at a concentration as low as 8 μg/L.

Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains

Cadaver dogs are routinely used internationally by police and civilian search organisations to locate human remains on land and in water, yet little is currently known about the volatile organic compounds (VOCs) that are released by a cadaver underwater; how this compares to those given off by a cadaver deposited on land; and ultimately, how this affects the detection of drowned victims by dogs. The aim of this study was to identify the VOCs released by whole porcine (Sus scrofa domesticus) cadavers deposited on the surface and submerged in water using solid phase microextraction gas chromatography mass spectrometry (SPME GC-MS) to ascertain if there are notable differences in decomposition odour depending on the deposition location. For the first time in the UK, the volatile organic compounds (VOCs) from the headspace of decomposing porcine cadavers deposited in both terrestrial and water environments have been detected and identified using SPME-GCMS, including thirteen new VOCs not previously detected from porcine cadavers. Distinct differences were found between the VOCs emitted by porcine cadavers in terrestrial and water environments. In total, seventy-four VOCs were identified from a variety of different chemical classes; carboxylic acids, alcohols, aromatics, aldehydes, ketones, hydrocarbons, esters, ethers, nitrogen compounds and sulphur compounds. Only forty-one VOCs were detected in the headspace of the submerged pigs with seventy detected in the headspace of the surface-deposited pigs. These deposition-dependent differences have important implications for the training of cadaver dogs in the UK. If dog training does not account for these depositional differences, there is potential for human remains to be missed. Whilst the specific odours that elicit a trained response from cadaver dogs remain unknown, this research means that recommendations can be made for the training of cadaver dogs to incorporate different depositions, to account for odour differences and mitigate the possibility of missed human remains operationally.

GC-MS application in determination of volatile profiles emitted by infected and uninfected human tissue

Volatile organic compounds (VOCs) released into the headspace air over human tissues infected with different bacteria were investigated in this work. The above-mentioned VOCs result both from bacterial metabolic processes (pathogen-specific signals) and from the matrix (tissue samples themselves). The objective of this study was to investigate whether one could reliably identify various microorganism strains that exist inside infected tissue samples by direct monitoring of the headspace atmosphere above their cultures. Headspace samples were directly interrogated using a GC-MS system, which produced distinct profiles for samples contaminated with single bacterial strains or with multiple strains (mixed infections). Principal component analysis (PCA) and predictive analysis based on receiver operating characteristics curves (ROC) were the statistical procedures utilized for differentiating between infected and uninfected samples, while network analysis and heat-mapping were used to highlight the connections between emitted volatiles and infectious pathogens. By using ROC curves, obtained results demonstrated that the area under the ROC (95% probability interval) was 0.86 in case of infected samples and 0.48 for uninfected samples. On the other hand, PCA highlighted separation between components coming from infected and uninfected patients, where 67% of variance was described from the first 2 principal components. The biomarker chemicals documented from this work, as well as the developed methodology may ultimately be applied to identify bacterial infections by analyzing exhaled breath.

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.

Comprehensive comparative compositional study of the vapour phase of cigarette mainstream tobacco smoke and tobacco heating product aerosol

A simple direct sample collection/dilution and introduction method was developed using quartz wool and Tenax/sulficarb sorbents for thermal desorption and comprehensive two-dimensional gas chromatography (TD-GC × GC) analyses of volatile organic compounds from vapour phase (VP) fractions of aerosol produced by tobacco heating products (THP1.0) and 3R4F mainstream tobacco smoke (MTS). Analyses were carried out using flame ionisation detection (FID) for semi-quantification and both low and high resolution time-of-flight mass spectrometry (LR/HR-TOFMS) for qualitative comparison and peak assignment. Qualitative analysis was carried out by combining identification data based on linear retention indices (LRIs) with a match window of ±10 index units, mass spectral forward and reverse library searches (from LR and HRTOFMS spectra) with a match factor threshold of >700 (both forward and reverse), and accurate mass values of ± 3 ppm for increased confidence in peak identification. Using this comprehensive approach of data mining, a total of 79 out of 85 compounds and a total of 198 out of 202 compounds were identified in THP1.0 aerosol and in 3R4F MTS, respectively. Among the identified analytes, a set of 35 compounds was found in both VP sample types. Semi-quantitative analyses were carried out using a chemical class-based external calibration method. Acyclic, alicyclic, aromatic hydrocarbons and ketones appeared to be prominent in 3R4F MTS VP, whereas larger amounts of aldehydes, ketones, heterocyclic hydrocarbons and esters were present in THP1.0 aerosol VP. The results demontsrate the capability and versatility of the method for the characterization and comparison of complex aerosol samples and highlighted the relative chemical simplicity of THP1.0 aerosol in comparison to MTS.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold.

Direct analysis of volatile organic compounds in foods by headspace extraction atmospheric pressure chemical ionisation mass spectrometry

RATIONALE

The rapid screening of volatile organic compounds (VOCs) by direct analysis has potential applications in the areas of food and flavour science. Currently, the technique of choice for VOC analysis is gas chromatography/mass spectrometry (GC/MS). However, the long chromatographic run times and elaborate sample preparation associated with this technique have led a movement towards direct analysis techniques, such as selected ion flow tube mass spectrometry (SIFT-MS), proton transfer reaction mass spectrometry (PTR-MS) and electronic noses. The work presented here describes the design and construction of a Venturi jet-pump-based modification for a compact mass spectrometer which enables the direct introduction of volatiles for qualitative and quantitative analysis.

METHODS

Volatile organic compounds were extracted from the headspace of heated vials into the atmospheric pressure chemical ionization source of a quadrupole mass spectrometer using a Venturi pump. Samples were analysed directly with no prior sample preparation. Principal component analysis (PCA) was used to differentiate between different classes of samples.

RESULTS

The interface is shown to be able to routinely detect problem analytes such as fatty acids and biogenic amines without the requirement of a derivatisation step, and is shown to be able to discriminate between four different varieties of cheese with good intra and inter-day reproducibility using an unsupervised PCA model. Quantitative analysis is demonstrated using indole standards with limits of detection and quantification of 0.395 μg/mL and 1.316 μg/mL, respectively.

CONCLUSIONS

The described methodology can routinely detect highly reactive analytes such as volatile fatty acids and diamines without the need for a derivatisation step or lengthy chromatographic separations. The capability of the system was demonstrated by discriminating between different varieties of cheese and monitoring the spoilage of meats.

Thermal desorption comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry for vapour phase mainstream tobacco smoke analysis

A thermal desorption comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GC×GC-TOFMS) method has been developed for the analysis of mainstream tobacco smoke (MTS) vapour phase (VP). The selection process of the sample introduction approach involved comparing the results obtained from three different approaches: a) use of gas sampling bag followed by SPME (Tedlar®-SPME), b) gas sampling bag followed by TD (Tedlar®-TD), and c) sampling directly on TD sorbents (Direct-TD). Six different SPME fibers and six different TD sorbent beds were evaluated for the extraction capacities in terms of total number of peaks and related intensities or peak areas. The best results were obtained for the Direct-TD approach using Tenax TA/Carbograph1TD/Carboxen1003 sorbent tubes. The optimisation of TD tube desorption parameters was carried out using a face-centered central composite experimental design and resulted in the use of the Tenax TA/Carbograph 1TD/Carboxen 1003 sorbent with a 7.5min desorption time, a 60mL/min tube desorption flow, and a 250°C tube desorption temperature. The optimised method was applied to the separation of MTS-VP constituents, with 665 analytes detected. The method precision ranged from 1% to 15% for over 99% of identified peak areas and from 0% to 3% and 0% to 1% for both first (1tR) and second (2tR) dimension retention times, respectively. The method was applied to the analyses of two cigarette types differing in their filter construction. Principal component analysis (PCA) allowed a clear differentiation of the studied cigarette types (PC1 describing 94% of the explained variance). Supervised Fisher ratio analysis permitted the identification of compounds responsible for the chemical differences between the two sample types. A set of 91 most relevant compounds was selected by applying a Fisher ratio cut-off approach and most of them were selectively removed by one of the cigarette filter types.

Differentiation between decomposed remains of human origin and bigger mammals

This study is a follow-up study in the search for a human specific marker in the decomposition where the VOC-profile of decomposing human, pig, lamb and roe remains were analyzed using a thermal desorber combined with a gas chromatograph coupled to a mass spectrometer in a laboratory environment during 6 months. The combination of 8 previously identified human and pig specific compounds (ethyl propionate, propyl propionate, propyl butyrate, ethyl pentanoate, 3-methylthio-1-propanol, methyl(methylthio)ethyl disulfide, diethyl disulfide and pyridine) was also seen in these analyzed mammals. However, combined with 5 additional compounds (hexane, heptane, octane, N-(3-methylbutyl)- and N-(2-methylpropyl)acetamide) human remains could be separated from pig, lamb and roe remains. Based on a higher number of remains analyzed, as compared with the pilot study, it was no longer possible to rely on the 5 previously proposed esters to separate pig from human remains. From this follow-up study reported, it was found that pyridine is an interesting compound specific to human remains. Such a human specific marker can help in the training of cadaver dogs or in the development of devices to search for human remains. However, further investigations have to verify these results.

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.