Trace detection of endogenous human volatile organic compounds for search, rescue and emergency applications

Trends in Analytical Chemistry 66 (2015) 158–175

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Trends in Analytical Chemistry j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / t r a c

Trace detection of endogenous human volatile organic compounds for search, rescue and emergency applications Agapios Agapiou a,*, Anton Amann b,c, Pawel Mochalski b, Milt Statheropoulos d, C.L.P. Thomas e a

Department of Chemistry, University of Cyprus, P.O. Box 20537, Nicosia 1678, Cyprus Breath Research Institute of the University of Innsbruck, Rathausplatz 4, Dornbirn A-6850, Austria c Univ.-Clinic for Anesthesia and Intensive Care, Innsbruck Medical University, Anichstr, 35, Innsbruck A-6020, Austria d School of Chemical Engineering, National Technical University of Athens (NTUA), Field Analytical Chemistry and Technology Unit, 9 Iroon Polytechniou Str., Athens 157 73, Greece e Department of Chemistry, Centre for Analytical Science, Loughborough University, LE11 3TU, UK b

A R T I C L E

I N F O

Keywords: Blood Breath Chemical pattern Emergency Human VOCs Odor Skin Trace detection Urine Volatile organic compound

A B S T R A C T

Since Pauling’s paper in the 1970s, interest has increased in volatile organic compounds (VOCs) released from different bio-fluids, such as blood and urine. A number of VOCs reflect internal biochemical pathways occurring in the human body and their chemical pattern may serve as the chemical platform for tracing human VOCs. Monitoring endogenous human VOCs is proposed as an alternative method to the use of canines for search, rescue and emergency applications. Tracing human VOCs requires robust, rapid, reliable and sensitive analytical instruments. Instrumentation currently used to study human VOC biomarkers (e.g. GC-MS, PTR-MS, SIFT-MS, MCC-IMS, FAIMS and sensor based systems) has significant clinical potential, but has yet to receive widespread consideration for emergency search applications. © 2014 Elsevier B.V. All rights reserved.

Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Introduction ........................................................................................................................................................................................................................................................ Trapped human experiment ......................................................................................................................................................................................................................... Breath .................................................................................................................................................................................................................................................................... Urine ...................................................................................................................................................................................................................................................................... Blood ..................................................................................................................................................................................................................................................................... Skin and sweat ................................................................................................................................................................................................................................................... Emergency-medicine applications .............................................................................................................................................................................................................. Analytical instrumentation ............................................................................................................................................................................................................................ Future work ......................................................................................................................................................................................................................................................... Conclusions ......................................................................................................................................................................................................................................................... Acknowledgements .......................................................................................................................................................................................................................................... References ............................................................................................................................................................................................................................................................

1. Introduction Humans are thought to have their own distinctive odor, derived from a mixture of many low-molecular-weight molecules (18–300 amu) with associated high vapor pressures and, with the exception

* Corresponding author. Tel: +357 22 895432; Fax: +357 22 895466. E-mail address: [email protected] (A. Agapiou). http://dx.doi.org/10.1016/j.trac.2014.11.018 0165-9936/© 2014 Elsevier B.V. All rights reserved.

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of ammonia, carbon dioxide, water and NO, odor may be considered a mixture of volatile organic compounds (VOCs). Humans emit hundreds of VOCs associated with their metabolic processes. At the same time, other VOCs are formed in the human body through metabolism of food, beverages, drugs [1] or exposure to environmental VOCs [2–5]. Human VOC profiles are a combination of the VOCs associated with breath, urine, blood and skin; note VOCs from skin are in part produced by cutaneous microorganisms from apocrine secretion [6]. The tracking of VOCs is interesting for social [7,8], survival, medical [9–13], forensics [14] and security applications [15–17].

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The detection and the identification of VOCs in exhaled breath has attracted most interest for non-invasive, diagnostic applications. Exploitation of the results of significant effort and progress in this field is impeded because many studies report large numbers of candidate VOCs, are based on small numbers of participants, and use different analytical technologies, and standardized approaches to sampling data, normalization and validation have yet to be adopted [18]. The utility of adopting miniaturized, or even handheld, sensor-based devices [19–23] is a matter of debate and many are skeptical about the feasibility of this approach, given the current lack of unique or specific markers for certain diseases. Improvements in the information received from natural and/or man-made disasters have fostered reviews of the state of the art in urban search and rescue (USaR) operations [24–26]. The main factors that affect USaR operations were reported [27]. Currently, acoustic and optical systems have been widely used to locate casualties trapped in collapsed buildings. Endoscopes using fiber optics, infrared and visible cameras have been combined with geophones, seismic sensors, and microphones tuned to acoustic signals, such as voices, breathing, and heartbeats. In comparison, the use of chemical sensors is limited to carbon dioxide sensing and trained rescue dogs [28]. The excellent sensing capabilities of canines need to be balanced against their limitations. Dogs may work for a limited time only, are easily injured, respond poorly to human distress, become distressed themselves and require time-consuming training [29]. To improve and to enhance the training of canines, a dynamic vapor generator that simulates transient odor emissions of victims entrapped in the voids of collapsed buildings was presented [30]. Although numerous medical data from disasters are recorded in the literature, information on profiles that could contribute to casualty detection has been limited [31]. Recently, research addressed this area (www.sgl-eu.org), and the simulated “trapped human experiment” indicated that a plume of human metabolites, mainly carbon dioxide, ammonia, acetone and isoprene, capable of travelling through building debris [32], was formed over a period of up to 6 h. Multi-capillary column ionmobility spectrometry (MCC-IMS) applied in the same study, subsequently, led to 12 human metabolites being proposed as candidate signs-of-life markers [33]. In a related study in an attempt to understand the physiology and biochemistry of human subjects during entrapment, the injury profile of entrapped victims was correlated to the VOCs released and their biological source [17]. Three types of entrapment were proposed [17], according to victim status: A. people in great anxiety, hyper-alert in panic after the event, with or without minor injuries; B. persons in intense stress with multiple injuries; and, C. dead victims.

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modelling approaches developed [36]. Combustion VOCs were evaluated for their potential to mask human VOCs, and the feasibility of using them for the remote detection of hidden fires for first responders considered [35,36]. The gold standard for trace-VOC investigations is gas chromatography-mass spectrometry (GC-MS). Alternative techniques include proton-transfer reaction quadrupole or timeof-flight MS (PTR-Q-MS, PTR-TOF-MS) [37], selected ion flow tubeMS (SIFT-MS) [38] and MCC-IMS [16,39]. Portable and field approaches include differential IMS (d-IMS, also known as field asymmetric IMS, FAIMS) [40,41] and linear IMS, both of which have been successfully coupled to GC. VOCs are selectively ionized on the basis of the formation thermodynamics of their ions [e.g., through reactions with the hydronium ion (H3O+), the nitrosonium ion (NO+) or the dioxygenyl ion (O2+)] and the resulting product ions may identified through their mass or ion mobility. In PTR-MS, compounds may be identified through specific ions without requiring pre-separation, allowing for real-time measurement of the analytes. However, each method presents specific strengths and weaknesses. PTR-MS and SIFT-MS provide highly-sensitive, specific data in real time, providing the enthalpy of formation of analyte ions is higher than that of the reactant ions used. GC-MS provides comprehensive VOC profiles, albeit slowly [12]. None of these MS approaches comes close to passing field-operability tests. The field use of IMS has been well established and is acknowledged to be useful in safeguarding search and rescue dogs and personnel against exposure to toxic chemical agents [42]. Further, IMS has the potential for miniaturization but, importantly, it is limited by the selectivity of the ionization chemistry noted above. Other significant technologies, greatly used in the field, are novel sensor approaches, such as chemoresistive sensors, piezoelectric sensors, metal-oxide sensors, and quartz-crystal microbalance (QCM) sensors [19–22]. The analytical task for emergency-medicine and USaR operations may be characterization of VOCs and gases that uniquely signify human presence in debris, and specification of the instrumentation to take on USaR deployment. Note that not all VOCs associated with humans are candidates {e.g., reduced or low penetration capacity [43,44], time (aging) effects [15], confounding factors related to the USaR environment (waste materials, decomposing bodies, combustion products, rodent or insect infestation of the debris field, and even emergency medicine and injuries (severe or not)}. It should be stressed that the nature of this specific field and the relevant ethical restrictions limit research to laboratory-based pilot studies, including a small number of volunteers (small sample size) and limited quantitative information (concentration range). This review evaluates the proposition that current analytical instrumentation may be used in support of USaR operations. 2. Trapped human experiment

These categories were further subdivided into: A1, B1. less than 24h of entrapment and, A2, B2. more than 24h. Tests with a handheld aspiration-type IMS indicated breath VOCs (e.g., propanal, pentanal, acetone, 2-butanone, 2-pentanone 4-heptanone, 3-methyl-2-butanone, ethanol, dimethyl disulfide, hexanal and octanal) could be detected, indicating potential applications for human detection within the debris field [34]. Studies on potential confounding factors addressed the presence of smoldering fires within the debris by combining audio, video and chemicaldata streams; direct imaging was augmented with the analysis of reflected images from metallic surfaces [35]. Further, the chemical profile of burning patterns of ubiquitous materials (e.g., textile, paper, and wood) was examined and data-processing algorithms and

Entrapment under the ruins of collapsed buildings is a severely stressful and painful situation. The victims are entombed in confined spaces, under collapsed structure voids, fighting against time for their survival; crush injuries, crush syndrome, acute kidney failure and renal damage are the most common medical implications. Triage in situ saves time, resources and lives; however, USaR resources are limited. According to the “rule of four”, a victim can survive 4 min without air, 4 days without water and 4 weeks without food, so USaR operations usually end after 72 h. Nevertheless, several reports have shown that entrapped victims can survive for longer periods of time [26]. Simulation of this condition is very difficult, if not impossible. Interesting devices capable of mimicking conditions similar to the entrapment scene are body-plethysmography chambers (Ganshorn,

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methylated and aromatic hydrocarbons, aldehydes, ketones, sulfur and nitrogen compounds) [45,46]. Breathing releases hundreds of endogenous and exogenous VOCs, and biomarkers in exhaled breath have been proposed for disease diagnosis [18,45–49]. Breath gases include inorganic gases, e.g.:

• • • • • • •

Fig. 1. Body-plethysmography chamber for the simultaneous monitoring of breath and skin emanations. {Reproduced with permission from [31]}.

Niederlauer, Germany) shown in Fig. 1. Properly adjusted, they can facilitate study of the changes in human chemical patterns during entrapment. Moreover, VOCs originating from different sources (e.g., breath, and skin) can be easily separated and analyzed, as can the physiological parameters of the individual under study. Aa an alternative, a controlled environmental chamber was designed and built at the University of Loughborough (United Kingdom), enabling precise control of ventilation temperature and humidity. The developed chamber consisted of three main parts: the void simulator; the collapsed building simulator; and, the environmental chamber (Fig. 2). The prototype was tested under real conditions with human volunteers remaining enclosed for 6 h, whilst the nature and the levels of volatile metabolic markers released by entrapped individuals into collapsed structure voids were monitored and detected; the experiment was named “Trapped Human Experiment” (THE) and it was an on-going 24 h per day in a fiveday experiment [32,33]. 3. Breath Hippocrates, (ca. 400 BC) noted that the breath aroma of his patients was different to that of normal individuals, and, since the advent of GC, the origin of the perceived changes in breath odor have been assigned to numerous trace VOCs (i.e., straight-chain,

CO2 (respiration); NO (catalyzed by nitric-oxide synthases; involved in vasodilation or neurotransmission); NH3 (protein metabolism); CH4 (gut metabolism of carbohydrates); H2 (gut bacterial metabolism of carbohydrates); H2S (bacterial metabolism of thiol proteins); and, hundreds of VOCs emitted in the low ppbv to pptv region, e.g.: ○ acetone (fatty acid catabolism); ○ isoprene (cholesterol biosynthesis); ○ ethanol (gut bacterial metabolism of sugars); ○ methanol (intestinal bacteria chlora); ○ 2-propanol (product of an enzyme-mediated reduction of acetone); ○ acetaldehyde (ethanol metabolism, lipid peroxidation); ○ ethane (lipid peroxidation); ○ methanethiol (methionine metabolism); ○ methylamine (protein metabolism); and, ○ n-pentane (lipid peroxidation).

Nevertheless, the biochemical pathway of the majority of VOCs is still a matter in dispute. Breath is anticipated to be the most prominent source of volatiles in the USaR context due to its continuous nature. Bearing in mind that entrapped victims may drift in and out of sleep or consciousness over hours and days, it appears reasonable that studies of breath VOCs during sleep are of particular significance to USaR operations [50]. Real-time measurements are of paramount importance in USaR applications. Mimicking on-site capabilities of canines supposes spatiotemporal dynamic detection and identification of the released VOCs, so plume monitoring is a real scientific challenge. A step towards understanding the field dispersion of VOCs involved the real-time monitoring of selected VOCs using PTR-TOF-MS. As shown in Fig. 3, the plume of acetone standards over quartz gravels was monitored in time and space. In the same context, the body-plethysmography chamber (Fig. 1) offers the potential of direct, simultaneous detection of breath and skin metabolites of enclosed volunteers. Since breathing is a dynamic, continuous physical process, direct monitoring of human-oriented VOCs under daily natural activities (e.g., sleeping and exercising) in association with human vital signs opens a unique window for future on-site medical applications. This is especially helpful when an entrapped victim is detected under the ruins but extrication efforts require several hours. Acetone and isoprene are the most abundant VOCs in human breath and they have been the subject of numerous studies including mathematical modelling of their dynamics [51–53]. In the same context, isothermal (same temperature) rebreathing was applied as an experimental technique for estimating the alveolar levels of hydrophilic VOCs using as prototypic test compounds acetone and methanol [54]. Victims of severe hydration status were approached through continuous exercise in an ergometer and their specific VOCs were monitored over time (i.e., methyl acetate, butane, dimethyl sulfide and 2-pentanone alongside acetone and isoprene) [55]. These revealed associated characteristic rest-to-work transitions in response to variations in ventilation or perfusion. However, the dynamic determination of VOCs introduced a new interesting window – online measurements using powerful analytical instruments, such as PTR-MS. As a result, isoprene concentrations were shown to increase

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Fig. 2. The trapped human experiment, showing the environmental chamber, (bottom), feeding air to the void simulator and hence to the collapsed building simulator. A – Air supply; H – Humidifier; E – In-line Environics air-quality monitoring; T – In-line temperature and humidity monitoring; P – Thermoelectric heat pump; G – CO2, CO and O2 gas monitoring; S – Sampling point; V – Vital signs monitoring; And, F – Flow control. 1a – 4b: Sampling point locations within the collapsed building simulator. Airflow through the experiment is indicated by arrows. {Reproduced with permission from [32]}.

by a factor of 4–5 during moderate effort (e.g., exercising on a stationary bicycle); probably, isoprene is re-synthesized in the body on a time-scale of about 1–2 h for replenishment of peripheral pools (e.g., in the arms and legs) [56]. Breath ammonia is also widely emitted in the breath of human individuals in the concentration range 50–2000 ppbv. In parallel, it has been associated with various medical processes, including kidney, liver, and bacterial infection of either the stomach or mouth (e.g., hemodialysis, asthma, hepatic encephalopathy, detection of Helicobacter pylori, and halitosis) [57,58]. It is important to stress that all breath acetone and isoprene studies were conducted in sterile environments and have many difficulties in “real world” real-time applications. In particular, in such entrapment cases, which are highly influenced by the surroundings of the crash site of the building, measuring must be narrowly confined, allowing the victim to be pinpointed and avoiding confounding factors, such as the high emission rates of isoprene from vegetation into the atmosphere. Sensor-based systems are also considered significant in breath testing. In particular, they were used for:

• • •

detection of H. pylori infection, as a major cause for gastric cancer (GC) and peptic ulcer disease (PUD) [59]; distinguishing gastric cancer from benign gastric conditions [60]; and, detection of digestive cancer [61].

Furthermore, nanomaterial-based sensors were applied to monitoring the effect of hemodialysis on exhaled breath VOCs [62]. Finally, there was an assessment of the exhalation kinetics of VOCs linked with cancer [63]. 4. Urine Human urine is considered one of the best characterized matrices for human biomarkers [64,65] and medical diagnosis [66].

Numerous studies have dealt with urine VOCs in medical, toxicological, forensic, work-place and environmental exposure applications. VOCs in human urine originate from three main sources: dietary, systemic and exogenous (e.g., environmental exposure or smoking). Chemical human signatures of VOCs mainly belong to the first two categories, although the presence of exogenous compounds cannot be ignored. The principal analytical technologies used for headspace analysis of urine VOCs include GC-MS (with or without derivatization) [65,67], SIFT-MS [68], solid-phase extraction with subsequent thermal desorption [69] and IMS [70]. The majority of urine-analysis papers focused on clinical applications, and, as such, the state of the art in urine diagnosis relies on non-field devices with significant sample work-up for targeted-compound analysis. More than 200 different VOCs have been reported in urine by various authors [18,71], and, recently, the urine metabolome was presented [72]. However, such studies have treated urine to enhance the VOC-extraction process, mainly by salt addition, heating, agitation or pH adjustment [71]. Such extensive work is of limited application for USaR applications, where the primary aim is to detect and to identify signs of life, and only spontaneously emitted urineborne VOCs can be considered as potential markers of human presence. Moreover, within the entrapment environment in the debris field in confined spaces, temperature and humidity are expected to affect urination cycles and quantities, as well as volatilization processes. Also, dehydration and nutrition strongly affects human physiology. Consequently, VOCs described in clinical studies will not necessarily translate to a USaR context (e.g., many organic acids do not appear in high concentrations in the headspace of urine due to their low pKa value). Aside from water, urine mainly consists of urea, which therefore serves as the best detectable chemical, as other organic chemicals vary substantially and might be harder to relate in such cases. In a preliminary study, headspace solid-phase microextraction GC-MS (HS-SPME-GC-MS) was employed to create a panel of spontaneously emitted urinary signs of life [15]. Some 20 healthy

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Fig. 3. Spatiotemporal measurements of acetone standards over quartz gravels using proton-transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) (plume detection and monitoring). {The right panel of the figure is reproduced with permission from [31]}.

volunteers (9 female, 11 male, mean age 29.5, no special dietary regimes) provided samples of mid-stream urine that subsequently had their headspaces sampled over a period of four days while they were stored at room temperature. The inclusion criteria were a detectable level in 80% of the samples, and 33 VOCs were detected [15]. Among these compounds, ketones (represented by 10 ubiquitous compounds) and aldehydes (7) were the most common chemical classes. Volatile sulfur compounds (VSCs) were also detected. Some 17 VOCs were present in all samples. Methyl mercaptane, dimethyl disulfide and dimethyl trisulfide were the species showing the greatest increase in concentration [15]. Moreover, another study went on to characterize the quantitative permeation profiles of urine VOCs that were determined over a 24-h period through common building materials, such as brick and concrete [43]. Some 22 volatiles (based on NIST-standard libraries and retention-time data from standard reference materials) were proposed as potential human-urine indicators from the urine of four volunteers; acetone, 2-butanone, 2-pentanone, 4-heptanone, pyrrole and dimethyl sulfide were found in all cases, so they were indicated to be the most promising biomarkers. The VOC concentrations were generally below 10 ppb, with the prominent exception of acetone (300–600 ppb). Building materials appeared to affect the permeation profiles of the analytes under study. The more persistent compounds were those with good solubility in urine (aldehydes and ketones). However, furans

and sulfur compounds presented short residence times, so their usefulness is limited in the context of UsaR operations. Concrete had a greater effect than brick, drawing out the residence-time profiles to provide clear maxima and strong tailing. The maximum concentrations of the aldehydes were relatively unaffected by brick or concrete and their residence profiles also showed strong tailing. Whilst SPME-GC-MS is effective for building VOC libraries of human indicators, it is not a viable approach for USaR operations, so MCC-IMS [16,44] was used in a follow-up study involving 30 participants. Two samples were taken, one after fasting and another during a spontaneous urinating event [16], and 23 VOCs were isolated from the original panel of 33 VOCs. These 23 compounds served as a reference library for the MCC-IMS urine studies. Of these potential indicators, 11 were identified ubiquitously (using the same 80% threshold), of which acetone, 3-methyl-2-butanone, 2-heptanone and octanal were present in all samples. Moreover, quantitative aspects of the IMS characterization of urine VOCs were also addressed using 2-heptanone and n-octanal as prototypic VOCs. Their evolution profile was mapped through a filling chamber filled with varying grain sizes (2–8 mm) and layers (4– 12 cm) of quartz [44]. Permeation profiles of 2-heptanone exhibited exponential growth and subsequent exponential decay in the headspace of the chamber. For n-octanal experiments, only exponential growth was identified over the full experimental run time

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(12 h), probably because of the limited experimental time, but the work indicated that 2-heptanone permeated four times faster than n-octanal. The 2-fold and 3-fold increases of quartz-sand thickness lengthened the permeation times on average 3 and 7 times for n-octanal and 3 and 5 times for 2-heptanone, respectively [44].

5. Blood Headspace analysis of blood VOCs has been studied less than urine or breath analysis, due to the nature of the sample and individual response. Most of these studies were mainly performed for toxicological and environmental purposes. However, the close physiological link between blood and breath enables small volatile molecules to pass the alveolar-blood capillary membrane and vice versa. Having in mind the wide presence of isoprene and other hydrocarbons (i.e., n-alkanes and methylated alkanes) in human breath, their blood/air partition coefficients were studied to improve knowledge of their exhalation behavior [73]. It was shown that partition-coefficient values change exponentially with boiling point, molecular weight and increasing number of carbon atoms. Moreover, isoprene solubility in water, human blood and plasma was determined, filling the relevant gap and offering the opportunity to model the fate of isoprene in environmental and biological systems [74]. Furthermore, simultaneous measurements of blood and breathborne VOCs were performed in healthy volunteers, enabling endogenous compounds to be distinguished from exogenous compounds [46]. Fig. 4 shows the concentration patterns of selected VOCs omnipresent in blood and breath. The colors (or grey-scale patterns) correspond to the chemical classes of compounds. Note that the VOCs in Fig. 4 are a small set of the compounds found in either of the two matrices (breath and blood).

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6. Skin and sweat Skin, next to breath, is a principal source of human VOC constituents, as skin is the largest human organ, accounting for almost 15% of body weight. Contrary to temporal sources (i.e., blood or urine), skin VOCs are released continuously; however, glandular secretion and skin bacteria may differ considerably between individuals, giving rise to highly disparate VOC profiles [75]. A variety of analytical instruments have been employed for the determination of skin or sweat emanations, providing on-line measurements, such as secondary electrospray ionization atmospheric pressure MS (SESI-API-MS) [76], PTR-MS [77], SIFT-MS [78], MCCIMS [39], and off-line analytical determinations, including SPMEGC-MS [79,80] and thermally desorbed membranes using TD-GCMS [81]. Nevertheless, sweat-odor research has mainly focused on studying certain parts of the body (e.g., mainly axillae, hands and feet) for their emitted volatiles, while, in parallel, there was wide interest in deodorants, perfumes and chemical attractants of mosquitoes [82]. Although human-skin odors are produced in small amounts, they present high variability due to diet, disease and other factors. Nevertheless, in a relevant review, there is a list of the 25 most frequently identified VOCs in skin literature [83]. The majority of skin VOCs usually comprises oxygenated species, including aldehydes, alcohols, ketones, acids and esters. Particularly interesting compounds are aldehydes and ketones, which are thought to be related to oxidative degradation and oxidative stress. Fig. 5 shows exemplary measurements of skin-acetone emission from volunteers closed in the body-plethysmography chamber. Another very promising volatile skin compound is NH3 [84], which is released in normal and abnormal conditions (e.g., liver or kidney weakness, protein breakdown, anidrosia, and low sodium/potassium ratio). All these medical and metabolic implications are also found in

Fig. 4. Concentration patterns of selected omnipresent volatile compounds in blood and breath. The colors correspond to the different chemical classes of compounds: Green, Ketones; Red, Sulfides (DMS = Dimethyl sulfide, MPS = Methyl propyl sulfide); Orange, Terpenoids; Cyan, Hydrocarbons; and, Dark green, Miscellaneous.

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Fig. 5. Acetone emitted by skin of volunteers in a body-plethysmography chamber, exhaling through a tube leading outside the chamber, not contributing to the concentration within the chamber. Different colors refer to different volunteers. The volunteers remained within the chamber for ~60 min. Acetone was measured using protontransfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) (Ionicon Analytik, Austria) with NO+ as a precursor ion.

entrapped victims, and contribute to higher production of skin NH3. Ammonia and acetone, were identified and detected in the THE [32,33]. The successful coupling of MCC with PTR-TOF-MS resulted in real-time monitoring of volatile emanations (aldehydes) from breath and skin [85]. In the same context, selective reagent ionizationtimeof-flight MS with NO+ as the reagent ion [SRI-TOF-MS (NO+) was applied for near real-time monitoring of selected skin-borne compounds. The majority of the detected compounds were aldehydes (n-propanal, n-hexanal, n-heptanal, n-octanal, n-nonanal, and 2 methyl 2-propenal), ketones (acetone, 2-butanone, 3-buten-2one, and 6-methyl-5-hepten-2-one), a hydrocarbon (2-methyl 2-pentene) and a terpene (DL-limonene). The observed median emission rates were in the range 0.28–44.8 nmol × person−1 × min−1 (16 − 1530 fmol × cm−2 × min−1) [86]. Furthermore, SPME-GC-MS analysis was applied to monitor the emission rates of selected VOCs from the skin of healthy volunteers. The observed median emission rates were in the range 0.55– 4790 fmol × cm−2 × min−1, whereas, acetone, 6-methyl-5-hepten-2one, and acetaldehyde presented high emission rates exceeding 100 fmol × cm−2 × min−1 [87]. In another study, human-skin and breath volatiles were simultaneously detected using GC-MS and sensorbased systems [88].

7. Emergency-medicine applications Patients in emergency-medicine applications (i.e., Intensive Care Units, ICUs) somehow resemble entrapped victims, as they usually present severe injuries, are multi-fractured and need oxygen supply. Studies definitely require ethical-protocol approval and are performed with a limited number of volunteers (small sample size), so the potential and the limitations of such studies were reviewed [9]. In such applications, the most preferred targeted source is expired air, and, more specifically, mechanically ventilated patients [89]. In such an application, an ion-molecule-reaction-MS (IMR-MS) was used for targeted monitoring of acetaldehyde, acetone, ethanol and isoprene [90]. A further step was the breath monitoring of five anaesthetized patients, whilst laparoscopic surgery was taking place in the operating theater [91]. SIFT-MS results showed that breath acetone remained almost at a constant level, but the long surgery time resulted in a slightly raised level because of lipolysis. However, a clear increase in breath isoprene was observed following abdomen inflation with CO2. The intravenously injected propofol was also detected in patients’ exhaled breath, but it remained constant during the whole perioperative period. Associated work was also carried out in pulmonary diseases. A recent review, which included data from 73 studies, highlighted the

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Table 1 Applied analytical methods widely used in the determination of volatile organic compounds (VOCs) Analytical instrumentation Gas Chromatography-Mass Spectrometry (GC-MS) Proton Transfer Reaction-Mass Spectrometry (PTR-MS) Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry (PTR-TOF-MS) Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) Laser Spectrometry Ion Mobility Spectrometry coupled to a multi-capillary column (with retention time ~1 min) (MCC-IMS) Field Asymmetric Ion Mobility Spectrometry (FAIMS) chip Sensors array

Identification

Fast?

Small?

Gold standard; reliable identification and quantification of a wide range of substances Limited identification. Fragmentation of compounds. Humidity influence. Insensitive to some VOCs (e.g., alkanes) High mass resolution Δm/m ~ 1/5000 often allows separation of isobars. Coupling to a MCC with retention time ~1 min further improves the possibility to separate compounds, while keeps near-real time capability Differentiates and identifies substances at the same molecular mass. Less sensitive than PTR-MS Detects specific small molecules (e.g., NO, CO, CO2, NH3, carbonyl sulfide, ethane). Not a portable technique Library of compounds still to be created, insensitive to some VOCs (e.g., alkanes)

✗ ✓

✗ ✗

✓

✗

✓

✗

✓

✗

✓/ ✗

✓

✓

✓

✓

✓

The varying voltage in FAIMS improves the identification of compounds even without coupling to an MCC Multivariate statistical techniques are usually applied to analyze the produced patterns “smell print”. Often the sensitivity does not (yet) reach down to concentrations at 1 ppb.

VOCs that were correlated with various diseases, such as asthma, chronic obstructive pulmonary disease, cystic fibrosis, thoracic oncology (e.g., lung cancer), and acute respiratory distress syndrome [92]. In the same context, the VOCs that are produced by the six most abundant and pathogenic bacteria in sepsis (Staphylococcus aureus, Streptococcus pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella pneumoniae and Escherichia coli) were identified and detected [93–96]. Sepsis is considered the most common cause of death after crush syndrome. It should be stressed that ICU patients present many different causes that do not necessarily relate to outdoor measurements of entrapment victims and could span a wide scope of injuries or conditions. However, other emergency applications, such as acute kidney injury, seem closer to the medical conditions of entrapped victims.

Other container-based approaches that have been developed for higher concentrations (ppm and above) are straightforward to apply but introduce complexity, instability and difficult-to-characterize artifacts into measurements, and such approaches would include glass syringes and polymer bags. Methods developed for monitoring VOC exposure in industrial-hygiene applications are more robust (BioVOC) and combine containers with adsorbent traps [99]. Sample reproducibility and storage contamination may be addressed through on-line analytical methods often directly interfaced to a mass spectrometer (e.g., PTR-MS, and SIFT-MS) due to the complexity of the sample, while others (e.g., MCC-IMS, FAIMS, and sensor arrays) are considered more compound-targeted but with high potential for miniaturization.

8. Analytical instrumentation Detection of humans in the field though their VOC profiles is mostly performed by trained canines, often in the context of security applications, criminal investigations, location of missing humans and/or dead bodies and rescue operations. Rescue dogs have been trained and deployed in search and rescue operations to locate casualties trapped within debris following major structural collapses. The apparent speed and fidelity of canine olfaction in these situations often obscures the laborious, dangerous and costly nature of such interventions. Furthermore, the chemical nature and the identities of the human markers perceived by dogs remain unknown, as does the true sensitivity and selectivity data (Receiver Operating Characteristics, ROC curve: the graphic interpretation of the sensitivity and selectivity that needs to relate to a determined threshold). Humans are only aware of the “finds”, as the false negatives remain unknown. An operational cycle time of 30 min followed by 5-h recovery accompanied by exhaustion and injury is the common way of working of a rescue dog. GC-MS is a well-established, reliable, standardized analytical method widely applied for the analysis of volatile substances; unfortunately, the most important aspect of the workflow, namely sampling, has yet to achieve the same levels of standardized performance. The most effective approaches use adsorbent-based materials {i.e., thermo-desorption tubes [45], needle traps [97] or SPME [46]}, and, with care, significant enrichment (>104) may be achieved, and miniaturization and integration of such approaches into portable analytical systems has been demonstrated for remote environmental applications [98] (e.g., International Space Station).

Fig. 6. An exemplary 3D chromatogram from headspace multi-capillary column ion mobility spectrometer (HS-MCC-IMS) analysis of volatile organic compounds (VOCs) in the headspace of human urine. {Reproduced with permission from [31]}.

166

Table 2 A panel of selected human-borne volatile organic compounds (VOCs) emitted from breath, urine, blood and skin or sweat. Preferentially, median concentrations have been considered from the existing literature, if available Compound name

Chemical class

Chemical formula

Tentative origin

124-38-9 10102-43-9 74–82-8

Carbon dioxide Nitric oxide Methane

Inorganic Inorganic Hydrocarbon

CO2 NO CH4

74–84-0

Ethane

Hydrocarbon

C2H6

109-66-0

Pentane

Hydrocarbon

C5H12

78–79-5

Isoprene

Hydrocarbon

C5H8

67-56-1

Methanol

Alcohol

CH4O

Blood-borne

64-17-5

Ethanol

Alcohol

C2H6O

Natural, diet, disinfectants, diet/bacteria

67-63-0

2-Propanol

Alcohol

C3H8O

Natural, disinfectants

104-76-7

2-Ethylhexanol

Alcohol

C8H18O

Contaminant from tubing material, skin-borne

50-00-0

Formaldehyde

Aldehyde

CH2O

75-07-0

Acetaldehyde

Aldehyde

C2H4O

Blood-borne Bacterial Natural or petrol, product of lipid peroxidation, blood-borne Natural, possibly petrol, product of lipid peroxidation, blood-borne or exogenous Blood-borne, mevalonate pathway – biosynthesis of cholesterol

Ethanol metabolism

Breath

Urine

Skin emanations

40000000 ppb healthy [57] || 38000000 ppb healthy [57] || 30000000 ppb healthy [57] || 6.7 ppb healthy [57] || 31 ppb healthy [57] || 20 ppb healthy [57] || mean concentration in healthy adult subjects 16.6 ppm and 15.2 ppm [102] || Mean 6.2 ppm [103] || 0.88 ± 0.09 ppb in healthy volunteers [92] || 0.10 (−0.25–0.44) ppb in healthy volunteers [92] || 0.82 ± 0.09 ppb healthy [92] || 1.9 (0–10.54) ppb in healthy volunteers [92] || 2.9 ± 1.0 pmol/dL healthy [92]

|| [83] ||

|| Mean 1.8 ppb healthy [46] || 0.21 (0.13–0.29) ppb healthy [92] || 0.83 (0.61–1.13) ng/L healthy [92] || median 268.0 (107.7–462.7) 10-12M healthy [92] || healthy volunteers 0.25–48.89 ppb, median 5.29 ppb [104] || median 0.12 (0.10–0.16) nmol/L healthy [92] || 0.57 ± 0.3 (mean ± SD) nmol/L [105] || healthy mean 40 and median 38 ppbv, 0.3 nmol/L (7 ppbv) for a healthy group, healthy populations 13 to 90 ppbv [106] || || Range (mean) 31–273 (131) ppb healthy [46] || 106 ppb healthy [57] || 58 (44–112) ppb [90] || 143 ppb healthy [92] || 105.2 ppb healthy [92] || 70.8 (19.5–200.5) ppb healthy [92] || 81.8 (56.1) ppb healthy [92] || 5.99 (3.53–8.45) nmol/L healthy [92] || median 21.8 (13.9– 41.4) nmol/m2 healthy [92] || 57.17–329.8 healthy volunteers || 40 to 300 ppb healthy, mean 212 ppb, SD 60 ppb [78] || 6 to 275 ppbv (mean: 99 ppbv) healthy [34] || mean (SD) 280 (143) ppb non-smokers healthy [107] || healthy volunteers 57.17–329.8 ppb, (median 104.55 ppb) || 7.05 ± 0.53 (mean ± SD) nmol/L [105] || range 55–121 ppb, mean 89.2 ppb [108] || mean 83 (22–234) ppb [108] || median 106 ppb, mean 83 ppb, mean 90 ppb, mean 146 ± 42 ppb [109] || median level for young cohort is 37 ppb, geometric standard deviation (GSD) 2.5, adult cohort of 106 ppb with a GSD of 1.65, mean (±SD) for pupils within age 7–10 years (28 ± 24 ppb), 10–13 years (40 ± 21 ppb), 13–16 years (60 ± 41 ppb) and 16–19 years (54 ± 31 ppb), mean 83 ppb (SD 45 ppb) and range 20–240 ppb [110] || mean 118 ppb (SD 68 ppb) and range 0–474 ppb, mean 83 ppb, without reported stress (mean 123 ppb) [111] || 461 ppb [57] || 142.0 ppb healthy [92] || mean 502 ppb (SD = 239) healthy, median 444 ppb healthy [78] || median value of 460 ppbv healthy [34] || mean (SD) 312 (159) nonsmokers healthy ppb [107] || median 461 ppb with geometric standard deviation 1.62 [109] || || (100–3358) ppb healthy [57] || 123 (108–185) ppb healthy [90] || healthy human breath (without consumption of alcohol) < 4.2 ppb [104] || mean 196 ppb (SD = 244), median 153 ppb healthy [78] || 188.5 (4.5–479.5) ppb healthy [92] || 100–200 ppb [103] || mean (SD) 130 (213) ppb non-smokers healthy [107] || range 27 ± 153 ppb, mean 86.6 ppb [108] || median 112 ppb with geometric standard deviation 3.24, mean 115 ppb; mean 90 ppb [109] || || 0–135 ppb healthy [57] || 3.21–4.17 ppb healthy [92] || median 94.1 (55.2) ppb healthy [92] || mean 22 ppb (SD = 17) healthy, median 94 ppb healthy [78] || 10 (5) ppb nonsmokers healthy [107] || median 18 ppb, mean 22 ppb [109] || || [33] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 2.69–13.1 (5.19) [87] ||

|| median 3.0 (1.9 ) ppb healthy [92] || healthy < 10 ppb [78] || healthy volunteers, smokers and lung cancer patients ranged in between 71 nmol/l (1,582 ppb) [112] || || 6–33 ppb healthy [57] || 63 (47–87) ppb healthy [90] || 20 ppb healthy [78] || mean (SD) 89 (134) ppb non-smokers healthy [107] || range 2 -5 ppb, mean 3.8 ppb [108] || median 22 ppb, range 2–5 ppb [109] ||

|| [15] || [16] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 0.99–17.7 (4.6) [87] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 683–42773 (2005) [87] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 506 [87] || || 0.70 ppb healthy [39] || >0.05 ppb healthy [33] || [83] ||

|| 33.3 (SD = 23.5) nmol healthy [77] || Emission rate range (median) [fmol × cm−2 × min−1] 164–3989 (244) [87] (continued on next page)

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

CAS-number

Table 2 (continued) CAS-number

Compound name

Chemical class

Chemical formula

Tentative origin

Propanal

Aldehyde

C3H6O

78–84-2

Aldehyde Propanal, 2-methyl- || Isobutyraldehyde

C4H8O

123-72-8

Butanol

Aldehyde

C4H8O

590-86-3

Butanol, 3-methyl- || Isovaleraldehyde 2-Butenal, 3-methyl-

Aldehyde

C5H10O

Natural or industrial waste product, diet, skinborne Skin-borne

Aldehyde

C5H8O

Skin-borne

107-86-8

Natural or industrial waste product, exogenous or skin-borne

Urine

Skin emanations

| Range (mean) ) 5–66 (18.3) ppb healthy [46] || 1.56–3.44 ppb healthy [92] || 6.9 (5.6–9.1) ppb healthy [92] ||

|| [15] || [16] || [31] ||

|| [113] ||

|| [15] || || [16] || [43] ||

|| 7.05 ( SD = 3.32) nmol healthy [77] || || [82] || Emission rate (median) [nmol × min-1 × person-1] 1.23 -19 (4.03) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 3.44–112 (12.4) [87] || [114] [82] || Emission rate range (median) [fmol × cm−2 × min−1] 5.48–17.7 (11.7) [87] || [114] || || Emission rate range (median) [fmol × cm−2 × min−1] 4.6–311 (12) [87] ||

|| 1.35–1.87 ppb healthy [92] || mean (SD) 9 (5) ppb non-smokers healthy [107] || healthy volunteers, smokers and lung cancer patients ranged in between 7 pmol/l (161 ppt) [112] || || 0.32 (0.00–1.40) nmol/L healthy [92] ||

110-62-3

Pentanal

Aldehyde

C5H10O

Natural, diet, skinborne, urine

|| 0.002 (0.000–0.011) nmol/L healthy [92] || 4 (2) ppb non-smokers healthy [107] ||

|| [15] || || [16] || [43] ||

66-25-1

Hexanal

Aldehyde

C6H12O

Natural, diet, skinborne

|| Range (mean) 0.63–0.67 (0.65) ppb healthy [46] ||1 (1) ppb non-smokers healthy [107] ||

|| [15] || || [43] || [16] ||

124-13-0

n-Octanal

Aldehyde

C8H16O

Natural or industrial waste product, diet, skinborne

|| 0.011 (0.004–0.028) nmol/L healthy [92] ||

|| [15] || || [43] || [16] || [43] || [44] ||

124-19-6

Nonanal

Aldehyde

C9H18O

Possibly natural, skin-borne

|| 0.033 (0.021–0.096) nmol/L healthy [92] ||

112-31-2

Decanal

Aldehyde

C10H20O

Skin-borne

|| Emission rate range (median) [fmol × cm−2 × min−1] 6.09–26.9 (13.4) [87] || [114] || [115] || || 0.95 ppb healthy [39] || Emission rate range (median) [fmol × cm−2 × min−1] 13.5–68.7 (28.3) [87] || || Emission rate range (median) [fmol × cm−2 × min−1] 3.74–14.9 (8.59) [87] || || > 0.30 ppb [33] || [83] || 4.9 ppb [85] || Emission rate (median) [nmol × min-1 × person-1] 1.06–6.33 (1.98) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 16.8–168 (41.9) [87] || [116] || || 1.38 ppb healthy [39] || || >0.10 ppb healthy [33] || [82] || [83] || 8.5 ppb [85] || Emission rate (median) [nmol × min-1 × person-1] 0.5–2.52 (0.99) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 22.5–150 (33.1) [87] || [116] || || 3.36 ppb healthy [39] || > ppb healthy [33] || [82] || [83] || 14.4 ppb [85] || Emission rate (median) [nmol × min-1 × person-1] 0.58– 5.22 (1.52) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 18.1–119 (58.9) [87] || [116] || || 3.17 ppb healthy [39] || >0.3ppb healthy [33] || || [82] || [83] || 29.9 ppb [85] || [116] ||

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

123-38-6

Breath

(continued on next page) 167

168

Table 2 (continued) CAS-number

Chemical class

Chemical formula

107-02-8

2-Propenal || Acrolein

Aldehyde

C3H4O

78–85-3

Aldehyde

C4H6O

100-52-7

2-Propenal, 2-methyl- || Methacrolein Benzaldehyde

Aldehyde

C7H6O

Exogenous, skinborne

|| Range (mean) 1–3.4 (1.8) ppb healthy [46] ||

64-19-7

Acetic acid

Acid

C2H4O2

Natural or industrial waste product, bloodborne

|| 68 (64) non-smokers healthy ppb [107] ||

27960-21-0

trans-3Methyl-2hexenoic acid 3-Hydroxy-3methylhexanoic acid 3-Methyl-3sulfanylhexan1-ol sec-Butyl acetate

Acid

C7H12O2

|| [119] || [120] || [83] || [121] || [115] || [122] || [6] || [123] ||

Acid

C7H14O3

|| [119] || [124] || || [125] || [119] || [83] || [122] ||

Sulfide

C7H16OS

|| [125] || [126] || [100] || [115] || [122] ||

Ester

C6H12O2

Ketone

C3H6O

|| 0.29 ppb healthy [39] || Emission rate range (median) [fmol × cm−2 × min−1] 659–8140 (4790) [87] ||| || > 2 ppb healthy [33] || Emission rate (median) [nmol × min-1 1 × person ] 13.2–168 (44.8) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 493–3680 (1100) [87] ||

58888-76-9

307964-23-4

105-46-4

67-64-1

Acetone

Tentative origin Exogenous

Breath

Urine

|| Range (mean) 2.9–19 (5.9) ppb healthy [46] || (5.10–9.57) ppb healthy [92] || mean (SD) 32 (64) ppb non-smokers healthy [107] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 6.37–45 (19.5) [87] || || Emission rate (median) [nmol × min-1 × person-1] 0.22– 0.98 (0.55) [86] || || 0.47 ppb healthy [39] || Emission rate range (median) [fmol × cm−2 × min−1] 62–238 (147) [87] || || [82] || 1 ppm healthy (foot odor) [83] || [117] || [118] ||

|| Range (mean) 0.4–2.9 (1.2) ppb healthy [46] ||

Blood-borne, fatty acid metabolism

|| Range (mean) 281–2525 (950) ppb healthy [46] || 200–2000 ppb healthy [57] || 504 (152–950) ppb healthy [90] || 627.5 ppb healthy [92] || (44.20–531.45) ppb healthy [92] || 225.7 (41.6–753.4) ppb healthy [92] || 33.2 (20.8–38.6) nmol/L healthy [92] || median 119 (52–270) nmol/m2 healthy [92] || (73.11–437.14) ppb in healthy volunteers [104] || median value of 600 ppbv healthy [34] || mouth (101.67 ppb), alveolar (199.19 ppb) [127] || median concentration (alveolar) 119.19 ppb and (mouth) 101.67 ppb [128] || median 212.25 ppb (healthy) [129] || median (mean) 347 (376) ppb healthy, median 327 ppb healthy, median 263 ppb healthy [103] || mean (SD) 1802 (984) ppb non-smokers healthy [107] ||73.11–437.14 ppb (median 145.58 ppb) in the control group [104] || range 293 ± 870 ppb, mean 487.4 ppb [108] || median 477 ppb with geometric standard deviation 1.58, mean 500 ppb; median 520 ppb in controls || young adults 17–18 years median 263 ppb and GSD = 1.61, adults 20–60 years median 477 ppb and GSD = 1.58, adults over 60 years median 440 ppb and GSD = 1.57 [130] || geometric mean 477 ppb (GSD 1.58) and range 148 - 2744 ppb, mean 329 ppb (SD 89) and median 318 ppb, mean 1130 ppb (SD 763) and median 803 ppb, type-2 diabetes patients greater than 1760 ppb whereas healthy controls lower than 800 ppb [131] ||

Skin emanations

|| [15] || [43] || || [16] ||

(continued on next page)

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

Compound name

Table 2 (continued) CAS-number

Compound name

Chemical class

Chemical formula

Tentative origin

Urine

Skin emanations

Diet, environmental contaminant, exogenous

|| Range (mean) 0.5–5 (2.2) ppb healthy [46] || (1.35–3.18) ppb healthy [92] || mouth (0.32 ppb), alveolar (0.24) [127] || median concentration (alveolar) 0.25 ppb and (mouth) 0.32 ppb [128] || median 0.38 ppb (healthy) [129] ||

|| Emission rate (median) [nmol × min-1 × person-1] 2.4–7.76 (3.94) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 3.7–16.6 (6.4) [87] ||

|| Range (mean) 0.1–2.1 (0.62) ppb healthy [46] || (1.80–4.11) ppb healthy || 4.8 (4.6–5.1) ppb healthy [92] || mouth (0.11 ppb), alveolar (0.38 ppb) [128] || median concentration (alveolar) 0.38 ppb and (mouth) 0.11 ppb [128] || median 0.38 ppb (healthy) [129] ||

|| [24] || [34] || [15] || [16] || [43] || [82] || || [24] || [43] || [15] || [16] || || [43] || [15] || [16] || [82] || || [82] ||

78–93-3

2-Butanone

Ketone

C4H8O

563-80-4

2-Butanone, 3-methyl-

Ketone

C5H10O

107-87-9

2-Pentanone

Ketone

C5H10O

Blood-borne, natural, diet

591-78-6

2-Hexanone

Ketone

C6H12O

Industrial waste product

589-38-8

3-Hexanone

Ketone

C6H12O

110-43-0

2-Heptanone

Ketone

C7H14O

106-35-4

3-Heptanone

Ketone

C7H14O

123-19-3

4-Heptanone

Ketone

C7H14O

110-93-0

6-Methyl-hept5-en-2-one

Ketone

C8H14O

98-86-2 7783-06-4

Acetophenone Hydrogen sulfide

Ketone Sulfide

C8H8O H2S

Bacterial

75-18-3

Dimethylsulfide

Sulfide

C2H6S

Blood-borne

624-89-5

Sulfide, ethyl methyl Sulfide, methyl propyl

Sulfide

C3H8S

Blood-borne

|| 11.78 ppb [127] || 2 ppb (median) healthy [127] || (mean ± SD) 115 ± 192 ppb, median 39 ppb [132] || mean concentration 11.78 ppb [128] || median geometric mean/geometric SD mouth 27.5/1.6 ppb [133] || || Range (mean) 1.4–28 (5) ppb healthy [46] || 7.58 (5.73–9.43) healthy [92] || 0.30 (0.00–0.31) nmol/L healthy [92] || 9.3 (5.3–19.3) ppb healthy [92] || (0.28–8.09) ppb healthy volunteers [104] || 20.3 ppb [127] || 4.81 ppb (median) [127] || (mean ± SD) = 35 ± 45 ppb, median 20 ppb [132] || mean concentration 20.3 ppb [128] || median concentration (alveolar) 14.48 ppb and (mouth) 4.29 ppb [128] || median 13.79 ppb (healthy) [129] || mean (SD) 17 (10) non-smokers healthy ppb [107] || healthy volunteers 0.28–8.09 ppb, median 2.13 ppb [104] || median (geometric mean)/geometric SD ppb mouth 3/1.3 [133] || || Range (mean) 10.05–0.06 (0.06 ppb) healthy [46] ||

Sulfide

C4H10S

Blood-borne, diet

|| Range (mean) 0.05–39 (2.2) ppb healthy [46] ||

3877-15-4

|| Range (mean) 0.1 ppb healthy [46] ||

Natural, drugs, blood-borne Blood-borne

|| Range (mean) 0.02–0.05 (0.03) ppb healthy [46] ||

|| [15] || [43] || || [44] || [15] || [43] || [16] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 0.85–7.56 (1.94) [87] || || Emission rate range (median) [fmol × cm−2 × min−1] 1.74–3.55 (2.65) [87] ||

|| Emission rate range (median) [fmol × cm−2 × min−1] 9.02–10.3 (9.66) [87] ||

|| [32] || [43] ||

Squalene oxidation, skin-borne

|| 1.04 ppb healthy [39] || [83] || Emission rate (median) [nmol × min-1 × person-1] 0.43– 2.54 (0.66) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 14–918 (133) [87] || || > 0.02 ppb healthy [33] ||

|| [15] || [43] ||

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

Breath

|| Emission rate range (median) [fmol × cm−2 × min−1] 0.60–6.06 (2.52) [87] ||

(continued on next page)

169

170

Table 2 (continued) CAS-number

Compound name

Chemical class

C4H8S

Tentative origin Blood-borne

Breath

Sulfide, allylmethyl

624-92-0

Dimethyldisulfide Sulfide

C2H6S2

3658-80-8

Dimethyltrisulfide Sulfide

C2H6S3

74–93-1

Methanethiol || Methylsulfide

Sulfide

CH4S

109-97-7

Pyrrole

Heterocyclic

C4H5N

110-00-9

Furan

Heterocyclic

C4H4O

Smoking

534-22-5

Furan, 2-methylFuran, 3-methyl-

Heterocyclic

C5H6O

Smoking

|| Range (mean) 0.1–3.7 (0.55) ppb healthy [46] || 1 (1) non-smokers healthy ppb [107] ||

Heterocyclic

C5H6O

Smoking-related, blood-borne

|| Range (mean) 0.05–0.39 (0.18) ppb healthy [46] ||

Bacteria

|| Range (mean) 0.09–12.7 (1.6) ppb healthy [46] || mouth (0 ppb), alveolar (0.10) [127] || median concentration (alveolar) 0.10 ppb and (mouth) 0 ppb [128] || median 0.08 ppb (healthy) [129] || || 3920 ± 680 pptv healthy [92] || mouth (0.061 ppb), alveolar (0 ppb) [127] || 0.052 ppb (median) [127] || median concentration (alveolar) 0 ppb and (mouth) 0.061 ppb [128] || healthy median 0.38 ppb [129] || median geometric mean/geometric SD mouth 5.5/1.3 ppb [133] || || mouth and alveolar (0 ppb) [127] ||

|| (1.82–2.88) ppb healthy volunteers [104] || 9.7 ppb [127] || (mean ± SD) = 178 ± 193 ppb, median 102 ppb [132] || mean concentration 9.7 ppb [128] || healthy volunteers 1.82–2.88 ppb, median 2.35 ppb [104] || median (geometric mean)/geometric SD ppb: mouth 3.5/1.5 [133] || || Range (mean) 0.09–0.27 (0.17) ppb healthy [46] || 4 (2) non-smokers healthy ppb [107] || || Range (mean) 0.08–2.3 (0.42) ppb healthy [46] || 3.7 (3.0–5.3) ppb non-smokers healthy [92] ||5 (6) non-smokers healthy ppb [107] ||

625-86-5

2,5Dimethylfuran

Heterocyclic

C6H8O

Smoking

|| Range (mean) 0.62–2.78 (1.6) ppb healthy [46] || mean (SD) 1 (2) non-smokers healthy ppb [107] ||

7664-41-7

Ammonia

Inorganic

NH3

Blood-borne

|| 50–2000 healthy [57] || 559–639 healthy [57] || 425–1800 healthy [57] || | 200–2000 healthy [57] || 964.4 ± 402.4 ppb, 280 ± 120 ppb healthy subjects [58] || median 688 ppb for mouth-eNH3 healthy, 34 ppbv for nose-eNH3 healthy, and 21 ppbv for both mouthand nose-eNH3 healthy after an acidic mouth wash [84] || mean value 854 ppb healthy, median 830 ppb healthy, geometric mean 833 ppb healthy [78] || range 422–2389 ppb, mean 1015.4 ppb [108] || median 833 ppb with geometric standard deviation 1.62, mean 1000 ppb [109]| geometric mean value 833 ppb and the geometric standard deviation 1.62 [134] young adults 17–18 years median 317 ppb and GSD = 2.14, adults 20–60 years median 833 ppb and GSD = 1.62, adults over 60 years median 1080 ppb and GSD = 1.71 [130] ||

75-50-3

Trimethylamine

Amine

C3H9N

138-86-3

DL-Limonene

Terpene

C10H16

End stage renal failure Industrial waste (used in food flavorings and cosmetics), bloodborne

|| Range (mean) 0.27–7.42 (1.46) ppb healthy [46] ||

Urine

Skin emanations || Emission rate range (median) [fmol × cm−2 × min−1] 0.28–3.13 (1.73) [87] ||

|| [15] || [16] ||

|| [15] || [16] || [82] || || [16] ||

|| [15] || [43] || || [15] || [16] || [43] || || [43] || || [16] || [43] ||

|| [82] || || [82] || Emission rate range (median) [fmol × cm−2 × min−1] 0.44–4.15 (0.9) [87] || || Emission rate range (median) [fmol × cm−2 × min−1] 0.37–8.28 (0.55) [87] || || A median ammonia mixing ratio in the lower forearm skin gas of 3.4 ppbv [84] ||

|| < 0.50 ppb healthy [33] || [83] || Emission rate (median) [nmol × min-1 × person-1] 0.21– 2.39 (0.76) [86] || Emission rate range (median) [fmol × cm−2 × min−1] 0.88–377 (8.76) [87] ||

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

10152-76-8

930-27-8

Sulfide

Chemical formula

A. Agapiou et al./Trends in Analytical Chemistry 66 (2015) 158–175

171

Fig. 7. Typical mean/median concentrations of selected breath volatile marker compounds in logarithmic scale. Compounds with an asterisk are considered smokingrelated compounds (e.g., furan and its methyl derivatives).

172

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Table 1 presents and compares the most widely applied analytical technologies for their performance, on-line capabilities and trend towards miniaturization [31]. PTR-MS, PTR-TOF-MS, SIFT-MS, IMS, and FAIMS offer on-line monitoring capabilities for human VOCs. Nevertheless, the ion chemistry in the instrument determines the nature of analytes and sample treatment/separation that is needed; this is especially true for atmospheric pressure chemical-ionization mechanisms. The PTR-Q-MS, PTR-TOF-MS and SIFT-MS techniques have been demonstrated to provide rapid, sensitive measurements of VOCs in ambient air. MCC-IMS (with GC column) and FAIMS (without GC column) are sensitive instruments with great potential for on-site applications and near real-time capabilities; they are considered effective systems for gas detection of biological fluids and situational awareness monitoring. A number of studies have been proposed to assist USaR teams in locating entrapped victims under collapsed structures by using handheld IMS [16,32–34,43]. In this context, Fig. 6 shows an example of a 3D chromatogram of urine headspace analysis. In addition, the advantage of FAIMS is its small size (microfabricated), simplicity and compatibility with GC or other sampling inlets. FAIMS is considered much more powerful and informative than linear IMS, because of the simultaneous detection of positive and negative ions [100,101]. Table 2 shows a pool of selected human-borne VOCs, which have high potential as indicators of life; these were selected from the relevant literature with caution. Table 2 presents qualitatively and/ or quantitatively the mean or median concentrations of these VOCs originating from human breath, blood, urine and/or skin and sweat with the aim of finally visualizing the human-breath profile based on the mean/median value results given in the literature. In this context, Fig. 7 represents the typical mean/median concentrations values (from the literature) for selected breath volatile compounds in logarithmic scale; the “spine-shape” figure accumulates the selected breath volatiles in a single figure and minimizes the variations in concentrations of VOCs.

9. Future work A lot of work needs to be done to solve the puzzle of human VOCs in USaR and emergency applications. Besides the interactions with building materials, other similar interferences include the interaction with clothes and the effect of other building materials (e.g., soil, steel, and wood). Another important factor in VOC analysis is the prevailing surface chemistry in the building surfaces of confined spaces; this is believed to be mostly affected by temperature and humidity. Along with surface chemistry, the porosity of the material and the type of chemical mechanism (e.g., condensation, and chemical bond) per material is also of paramount importance. Also, an important problem is dust and particulate matter carried in the air, which might strongly affect data collection and interpretation in such sites of building collapse. Finally, VOCs evolved from household animals and plants also need to be taken into consideration. Since the levels and the types of VOCs tend to evolve depending on victim’s medical condition, the issue is still open with respect to identification of groups of individuals who resemble the status of entrapped victims (e.g., people under high stress, and fasting, crush-syndrome, liver-damage, kidney-failure and ICU patients). Potential breath markers of renal disorder were recently detected; trimethylamine (TMA) was measured directly in the breath of individuals next to aliphatic hydrocarbons and sulfur compounds [104,135]. Sensor-based systems, such as gold-nanoparticle sensors and sensor arrays based on nanoparticles, were also tested for the detection of breath VOCs from renal injury patients [136,137]. Also, FAIMS usage is extending to novel medical applications [138].

While PTR-MS and SIFT-MS are powerful state-of-the-art analytical technologies for the rapid, continuous detection of VOCs, their use is mostly beyond consideration in USaR and emergency situations; their employment for detecting human endogenous VOCs under debris is relatively unexplored. However, both instruments are able to perform on-site dynamic measurements. Moreover, MCCIMS and FAIMS allow near real-time detection and have great potential for miniaturization. 10. Conclusions VOCs are continuously and ubiquitously evolved from human metabolism in a variety of fluids, including expired air, sweat, urine, blood, and other biological liquids. These compounds are not necessarily unique to human life, as they may be released by other sources. Confined spaces are enriched by VOCs of entrapped victims due to breathing, urination, sweating and blood loss (if injured), enabling their identification after hours and days of entrapment. The survival within ruins of the metabolic plume of VOCs contains transient and dynamic characteristics so it can serve as a chemical sign of human presence. State-of-the-art analytical methods (e.g., PTRMS, SIFT-MS, and MCC-IMS) and novel sensor-based sensors providing rapid, real-time, sensitive measurements of VOCs in ambient air are considered promising tools for crucial applications in the field (e.g., detection and identification of entrapped victims), while portability, robustness and miniaturization (e.g., FAIMS) remain necessary demands for the success of future onsite operations. Acknowledgements The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/ 2007-13) under Grant Agreement No. 217967 (“SGL for UsaR” Project, Second Generation Locator for Urban Search and Rescue Operations, www.sgl-eu.org). Anton Amann and Pawel Mochalski appreciate funding from the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT/BMWA, Project 836308, KIRAS). We gratefully appreciate funding from the Oncotyrolproject 2.1.1. The Competence Centre Oncotyrol is funded within the scope of the COMET - Competence Centers for Excellent Technologies through BMVIT, BMWFJ, through the province of Salzburg and the Tiroler Zukunftsstiftung/Standortagentur Tirol. The COMET Program is conducted by the Austrian Research Promotion Agency (FFG). P.M. gratefully acknowledges support from the Austrian Science Fund (FWF) under Grant No. P24736-B23. A.A. and P.M. thank the Government of Vorarlberg (Austria) for its generous support. References [1] S. Erhart, A. Amann, E. Haberlandt, G. Edlinger, A. Schmid, W. Filipiak, et al., 3-Heptanone as a potential new marker for valproic acid therapy, J. Breath Res. 3 (2009) 016004. [2] J.D. Pleil, Influence of systems biology response and environmental exposure level on between-subject variability in breath and blood biomarkers, Biomarkers 14 (2009) 560. [3] J.R. Sobus, J.D. Pleil, M.C. Madden, W.E. Funk, H.F. Hubbard, S.M. Rappaport, Identification of surrogate measures of diesel exhaust exposure in a controlled chamber study, Environ. Sci. Technol. 42 (2008) 8822. [4] J.D. Pleil, Role of exhaled breath biomarkers in environmental health science, J. Toxicol. Environ. Health B Crit. Rev. 11 (2008) 613. [5] J.D. Pleil, D. Kim, J.D. Prah, S.M. Rappaport, Exposure reconstruction for reducing uncertainty in risk assessment: example using MTBE biomarkers and a simple pharmacokinetic model, Biomarkers 12 (2007) 331. [6] X.N. Zeng, J.J. Leyden, J.G. Brand, A.I. Spielman, K.J. McGinley, G. Preti, An investigation of human apocrine gland secretion for axillary odor precursors, J. Chem. Ecol. 18 (1992) 1039. [7] M. Rosenberg, The science of bad breath, Sci. Am. 286 (2002) 72. [8] I. Eli, H. Koriat, R. Baht, M. Rosenberg, Self-perception of breath odor: role of body image and psychopathologic traits, Percept. Mot. Skills 91 (2000) 1193.

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