human body-odor components and their determination

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Human body-odor componentsand their determinationSudhir Kumar Pandey, Ki-Hyun Kim

We review methodological approaches commonly employed for the determination of human-body-odor (BO) components. As a

first step, we evaluate the common types of BO and the sampling and/or preconcentration strategies for them. We also emphasize

the potential for odor components as a health-diagnosis tool.

We critically evaluate gas chromatography-based detection methods in terms of their feasibility for human-BO monitoring. We

also discuss recently developed on-line detection methods, especially sensor-based techniques to assess their potential in the

determination of odorant components released by the human body. Finally, we highlight the limitations and the future prospects

for investigation of human BO.

ª 2011 Elsevier Ltd. All rights reserved.

Keywords: Body odor; Breath volatile; Disease diagnosis; Gas chromatography; Odor component; Odor sensor; Preconcentration; Sampling; Sweat;

Urinary volatile

1. Introduction

Human olfaction is the most ancient formof our distal senses, providing informationof different chemicals from distant sourcesin real time [1]. Humans emit a complexarray of non-volatile and volatile mole-cules, depending on their genetics, diet,stress, and immune status. Numerousvolatile compounds may be emitted fromseveral areas of the body that are prone toodor production (e.g., scalp, axillae, feet,groin, and oral cavity) [2]. However,according to the common criteriaextractable from the literature survey,human-body-odor (BO) research can besubdivided in to three categories:(1) perspiration (skin odor);(2) the odor released from the oral cavity

(e.g., exhaled breath); and,(3) odor released from human excreta

(e.g., urine).Individual BOs of humans are charac-

terized by several factors that are eitherstable over time (genetic factors) or varywith environmental or internal condi-tions. However, it is yet disputable to jus-tify how the human body creates diverseodorants with their own distinctive prop-erties.

BO is the unpleasant smell caused bythe mixing of perspiration (or sweat) andbacteria on the skin [3]. The idea that

human scent is produced through bacte-rial activities on dead skin cells andsecretions is the most common depictionof the creation of human BO [3]. Sweat isgenerally an odorless body secretion.However, as bacteria multiply on the skinand break down these secretions, theresulting by-products may contain astrong, disagreeable odor. The quick for-mation of odor was also proposed to sup-port the idea that such a mechanismproceeds via simple bond cleavage as op-posed to a complex bacterial action [4].

The present review is a compilation ofup-to-date knowledge on the determina-tion of human BO components withemphasis on volatile organic compounds(VOCs). Moreover, we also discuss brieflyother gaseous compounds that are rou-tinely monitored as BO components [e.g.,reduced sulfur compounds (RSCs), organicacids, carbonyls, and nitrogenous com-pounds (ammonia and trimethyl amine)][e.g., 1,5–10].

In this study, we evaluate the analyticalmethodologies commonly used for thedetermination of the odor componentsemanating from the human body. We alsohighlight the advantages and the limita-tions of these methods in order to directfuture human BO research in an accurate,quantifiable manner. Fig 1 depicts a gen-eral schematic of the common analytical

Sudhir Kumar Pandey,

Ki-Hyun Kim*,

Atmospheric Environment

Laboratory, Department of

Environment and Energy,

Sejong University,

98 Goon Ja Dong,

Gwang Jin Goo,

Seoul 143-747,

Republic of Korea

*Corresponding author.

Tel.: +82 2 499 9151;

Fax: +82 2 3408 4320;

E-mail: [email protected],

[email protected]

Trends Trends in Analytical Chemistry, Vol. 30, No. 5, 2011

784 0165-9936/$ - see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.trac.2010.12.005


protocols used for human-BO components released fromdifferent parts of the human body.

1.1. Skin odorUntil recently, there have been numerous types oftechnological limitations narrowing researchers� effortsto conduct qualitative and/or quantitative analysis of thechemical components released as human BO. As a result,the demand for such information has grown from vari-ous fields of study. For example, there is a pressing needto explain the mechanism of how the VOCs are producedfrom the body and act as the major constituents of hu-man BO. The composition of human-sweat samples hasalready been studied extensively through the years toelucidate the underlying interactions between the hu-man body and odorants [11–14]. Despite progressachieved in the analysis of human BO components,information about human sweat or excretions may notnecessarily be represented by the volatile compoundspresent in the headspace (HS) above the sweat samplesthat are apt to be collected and detected instrumentally.

Most of the relevant scientific research pertaining tohuman scent has noticed the significance of axillary(armpit) and plantar (foot) sweat [15,16]. Compoundspresent in pH-adjusted axillary secretion extracts havebeen isolated from both male [4] and female [17] sub-

jects and identified through gas chromatographic (GC)methods [e.g., GC-mass spectrometry (MS) and GC-Fourier transform infrared spectroscopy (FTIR)]. Theanalysis showed the presence of several C6–C10 straightchains, branched, and unsaturated acids. Essential odorcontributors were identified as terminally unsaturatedacids, 2-methyl C6–C10 acids, and 4-ethyl C5–C11 acids,along with (E)-3-methyl-2-hexenoic acid, which weresaid to be the major odor-causing compounds. Short-chain fatty acids have also been extracted from sweatsamples obtained from feet [18]. Extracts from sockswere obtained through a 6-h Soxhlet extraction withdiethyl ether and analyzed by GC-MS. Short-chain fattyacids like isovaleric acid were found in all of the samples,in especially greater quantities from persons whoclaimed to have strong foot odor.

Human skin-odor samples were subject to the identi-fication of age-specific compounds with the aid of HS-GC-MS [19]. These authors noticed certain compoundclasses (e.g., hydrocarbons, alcohols, acids, ketones, andaldehydes) present in human-skin odor. They also rec-ognized that 2-nonenal was the key odor component forindividuals over 40 years of age. 2-nonenal, as well asother aldehydes, was seen to be produced throughoxidative degradation of monosaturated fatty acids(e.g., palmitoleic acid and vaccenic acid) [19].

Body odor type

GC-FPD

GC-MS

Skin odor(axillary secretions)

Mouth odor

Urine odor

GC-FIDSorption on

sorbent

API-MS

Pt-In 2O3 sensor

CRDS

MESI-GC

HPLC-ESTMS

GC-DMS

SIFT-MS

SamplingPreconcentration/

ExtractionTransfer for final

determinationDetection

EquilibriumHead-space

SPME

Online b,c

Liquid phase Extraction

Cryofocusing in TD system

Solvent-based extraction

Automated TD system

Direct injection

Thermal desorption in injector

Body odor type

GC-FPD

GC-MS

Skin odor(axillary secretions)

Mouth odor

Urine odor

GC-FIDSorption on

sorbent

API-MS

2 3

CRDS

MESI-GC

HPLC-ESTMS

GC-DMS

SIFT-MS

SamplingPreconcentration/

ExtractionTransfer for final

determinationDetection

EquilibriumHead-space

SPME

Online b,c

Liquid phase Extraction a

Cryofocusing in TD system

Solvent-based extraction

Automated TD system

Direct injection

Thermal desorption in injector

a From pad or sponges; b Air from oral cavity; c Head-space air from urine. and Abbreviations: TD, Thermal desorption; SPME, Solid-phase microextraction; API-MS, Atmospheric pressure ionization mass spectrometry; CRDS, Cavity ringdown spectroscopy; MESI-GC, Membrane-extraction technique by combining a sorbent interface with GC; ESTMS; Electrospray tandem mass spectrometry; GC-DMS , GC-differential mobility spectrometry; SIFT-MS, Selected ion flow tube-MS; GC-FPD, GC-flame photometric detector; and

MS, Gas chromatography with mass spectrometryGC-

Figure 1. Analytical protocols practiced for the determination of different types of human-body odor.

Trends in Analytical Chemistry, Vol. 30, No. 5, 2011 Trends

http://www.elsevier.com/locate/trac 785


Volatile components from the female skin were alsoinvestigated [20]. These authors were able to group alldetectable compounds into several classes, includingshort- and long-chain hydrocarbons, short-chain alde-hydes, and a branched ketone using HS-GC-MS. As ex-plained above, some subjects exhibited specific volatiles(e.g., 6-methyl-5-hepten-2-one) and hydrocarbons ofshorter-chain lengths, including decane, although theabundances of these compounds varied to a large extentamong individuals.

A number of investigations were also carried out toexamine the interactions between human odor and in-sect responses using the yellow-fever mosquito [21–23].These authors collected sweat samples on glass beadsrolled between the fingers to determine the compoundsthat attracted the yellow-fever mosquito.

Studies pertaining to residual axillary odor in clothingalso showed the absence of organic acids and the pres-ence of other odiferous compound classes (e.g., laundrysoiled with human sweat was washed with a colordetergent). These samples were analyzed for the presenceof residual human BO, and several compounds (e.g.,esters, ketones, and aldehydes) were detected as primaryodorants in the swatches post-washing [5]. However,organic acids commonly considered the dominantodorants in human axillary sweat were not identified inthe extracts of residual odor.

1.2. Odor from oral cavityIn recent years, interest in breath analysis for clinicalpurposes has increased. Approximately 90% of oralmalodor is thought to originate from the oral cavity,with the remaining 10% originating from distal points inthe digestive and respiratory systems [24]. Evaluation ofbreath samples is therefore not only important as thesource of nuisance but also proposed as the least invasivemethod for clinical diagnosis, disease-state monitoring,and assessment of environmental exposure [1].

The composition of VOCs in breath varies widely fromperson to person in both qualitative and quantitativesenses [25]. The bulk matrix of breath is a mixture ofnitrogen, oxygen, CO2, H2O, and inert gases. Theremaining small fraction comprises more than 1000trace quantities. The endogenous compounds detected inhuman breath and its condensate are found to includeVOCs (e.g., isoprene, ethane, pentane, and acetone) andnon-volatile substances (e.g., isoprostanes, peroxynitrite,or cytokines) [26]. These VOCs may be generated in thebody (endogenous) or absorbed as contaminants fromthe environment (exogenous). The concentrations ofthose VOCs generally appear to fall in the range of partsper trillion (pptv) to parts per million (ppmv) by volume[26–29]. Although the number of VOCs identified inhuman breath to date is more than 1000, some of themare commonly recognized from most humans. As thesecommon VOCs include isoprene, acetone, ethane, and

methanol, such information is useful enough to extendthe methodological applicability of human BO study,especially in the field of clinical diagnostics [27]. Asmany pieces of information can be derived from suchapplications in both qualitative and quantitative terms,they have been drawing a great deal of attention in thediagnosis of possible disease states [1]. However, if dif-ferent exogenous molecules, particularly halogenatedorganic compounds are examined thoroughly, suchinformation can also be used to assess recent exposure todrugs or environmental pollutants [30].

Despite many advantages of breath analysis, there arealso many limitations in odor and clinical research. Mostimportantly, researchers have experienced lack of stan-dardization in their analytical approaches. As a result, itis not uncommon to see a wide variation in experimentalresults between different studies of breath analysis [31].In light of the fact that concentrations of most sub-stances in exhaled breath cover two to three orders ofmagnitude (e.g., pptv–ppbv), the use of proper tech-niques for collection and preconcentration of samplesremains an indispensable step for their analyses. Pre-concentration of these breath volatiles can be achievedby adsorption on sorbent traps, coated fibers [solid-phasemicroextraction (SPME)], or by direct cryofocusing.However, the high water content of breath samples isalso found to affect the efficiency of preconcentration,separation, and detection of individual compounds.

1.3. Odor from human excretionsThe analysis of liquid-phase urine samples has been avery common practice in clinical diagnostics for manydiseases. The odor profile of human urine, although notas potent as such traditional analysis, is also likely tooffer a diagnostic measure for many types of healthdisorder. Nonetheless, urine samples have scarcely beenevaluated with respect to gas-phase volatile odorants.Reports on the volatile profile of urine date back to asearly as the 1970s. For example, Zlatkis et al. [32]studied some volatile metabolites present in human ur-ine and marked 2-butanone, 2-pentanone, 4-heptanone,dimethyl disulfide (DMDS), alkyl furans, pyrrole, andcarvone as the characteristic volatile constituents innormal urine. These authors also found some com-pounds at very high levels (i.e. pyrazines, cyclohexa-none, lower aliphatic alcohols, and octanols) in urinesamples of diabetes-mellitus patients undergoing insulintreatment. In an analogue to this effort, a number oflow-molecular-weight metabolites (e.g., aliphatic alco-hols, ethanol, n-propanol, isobutanol, n-butanol, iso-pentanol, 4-heptanone, and cyclohexanone) weresuggested as marker components of metabolic disordersrelated to diabetes mellitus because of their presence atabnormally high concentrations [33].

Urinary volatiles were also treated separately aseffective targets to diagnose metabolic disorders such as


786 http://www.elsevier.com/locate/trac


phenylketonuria (PKU), maple-syrup-urine disease(MSUD), isovaleric acidemia, or trimethylaminuria (fish-odor syndrome) [34]. Zhang et al. [35] suggested thepossibility that routine quantification of volatile meth-ylamines and stable trimethylamine (TMA) N-oxidepresent in human urine can be used as an incisive tool todiagnose fish-odor syndrome. In continuation of thiseffort, Mills et al. [36] were able to determine TMA fromurine samples of patients suffering from fish-odorsyndrome. Wahl et al. [37] were also able to detect 34VOCs including TMA and 4-heptanone as metabolites ofdiagnostic relevance.

In continuation of these efforts, the amounts of volatilesubstances (acids, ammonia, sulfur, and nitrogen com-pounds) responsible for the malodor of human wastes(feces and urine) were obtained from a storage tank at acommunity wastewater-treatment plant [38]. Nonethe-less, relatively little has yet been achieved in character-izing urine odor as a health diagnostic tool or ascomponents of BO.

2. Sampling and preconcentration strategies

2.1. The basic principles of BO sampling and precon-centrationFor the quantitative (and/or qualitative) determinationof human BO components, it often requires a suitablesampling and/or preconcentration step to enrich thetarget components sufficiently to fit in the detectionrange of the common analytical systems. As depicted inFig. 1, if sorbent-based sampling (by means of sorbentmaterial) is employed for the collection of BO compo-nents, the collected analytes then need to be desorbedwith the aid of solvent or thermal desorption (TD) for theanalysis. The SPME-HS method is also one of the mostcommonly applied techniques that rely on partitioningand equilibrium of analytes between different matrices(liquid, gas, and solid phases) [6]. The HS can be usedwith or without the combination of TD, whereas SPMEgenerally requires HS for extracting and transferring theanalytes to the next stage. These techniques are wellknown for their reliability in quantifying a wide array ofodor components from various types of matrix. None-theless, they can also suffer from inherent or proceduralinterferences. For example, SPME can be subject to ma-trix effect, low storage stability, relative-humidity (RH)dependence of extraction efficiency, and competitivesorption [39].

The collection of odor samples on solid sorbents (bysorption) and their subsequent desorption (or re-extrac-tion) in a suitable solvent has been a common practicebefore final determination (e.g., addition of an analyte-transfer stage prior to detection) [40]. Although thesesolvent-based methods have been successful in deter-mining odor components at sufficiently low concentra-

tions, they are also subject to experimental biases due tothe multi-step treatment procedure (or contaminationfrom solvent).

In recent years, TD has emerged as a potent option forthe conventional solvent-based method, as it eliminatesuse of organic solvents [40]. The cryogenic trapping (orcryofocusing) technique has further promoted thecapacity of sorptive materials to extend the collectionefficiency for ultra-low-concentration samples [41].More specifically, a Peltier cooling (PC) device (e.g., anelectrically-cooled focusing trap) has proved to be aconvenient tool for cryogenic preconcentration and itcan be interfaced with an automated TD system [42].This PC-TD-based analysis has demonstrated a greatadvantage in terms of applicability to the determinationof odorous compounds at very low-concentration levelsfrom complex matrices, while still maintaining excellentquality assurance (QA). However, management of water(e.g., for breath samples) has become a crucial issue, as itcan hamper the analytical efficiency of these samplingand preconcentration techniques (e.g., clogging cryo-genic traps). It is thus important to eliminate waterselectively from the air streams before they are trans-ferred from the focusing device to the detection system.To overcome this problem, in-line permeable membranedryers (e.g., the Nafion dryer) can be used [43]. How-ever, use of these drying or scrubbing agents can also beproblematic, as they can eliminate some of target ana-lytes (e.g., polar compounds). In the following sections,we describe the applicability of these sampling and pre-concentration methods to analysis of human BO.

2.2. Skin odorMost studies directed toward determination of human-skin odor collected human axillary secretions in one wayor another. For example, Bernier et al. [22] collectedhuman-sweat secretions on glass beads rolled betweenthe fingers. Compounds were evaporated from the glasssurface via thermal desorption of sample glass beads andcryofocused on the head of the column with liquidnitrogen prior to GC separation. The application of GC-MS combined with above analytical protocols resulted inthe detection of at least 346 distinct peaks, 303 of whichwere directly identified by standard or tentatively iden-tified by library and spectral interpretation, leaving 43 ofthem unidentified. Because 26 out of the 303 com-pounds were of background origin, the remaining 277compounds were suspected of being candidate attrac-tants for mosquitoes (Aedes aegypti).

Natsch et al. [13] used cotton pads for the collection ofaxillary secretions. These authors extracted volatilecarboxylic acids by hydrolysis and treated the liquidextract with N-acyl-glutamine aminoacylase (N-AGA).Using GC-MS analysis, these authors were able toconfirm more than 28 volatile carboxylic acids releasedfrom human sweat; they designated them the main




odor components of human skin. Moreover, they alsorecognized that some of these compounds (i.e. severalaliphatic 3-hydroxy acids with 4-Me branches, 3,4-unsaturated, 4-Et-branched aliphatic acids, and a varietyof degradation products of amino acids) had not previ-ously been identified from natural sources.

In another report by Gallagher et al. [44], two com-plementary sampling techniques (viz SPME and solventextraction) were used to build up comprehensive VOCprofiles from human skin. Analyses were performedusing both GC-MS and GC with flame photometricdetection (FPD). These researchers applied a glass funnel[internal diameter = 10 cm (for back) and 7.5 cm (forthe forearm)] over the skin to hold a SPME fiber (2 cm,50/30-lm divinylbenzene (DVB)/carboxen (CAR)/poly-dimethylsiloxane (PDMS) Stableflex fibers) in place for anexposure duration of 30 min. In addition to SPME sam-pling, the skin secretions were also extracted with asolvent (50/50 mixture of ethanol/hexane) for directinjection (into GC injector) and detection by both FPDand MS. The application of both complementary sam-pling techniques (SPME and solvent extraction) yielded atotal of 92 individual compounds from the skin-emana-tion samples. However, there were differences in themaximum number of identifiable compounds – SPME(58) and solvent extracts (49) – which signified thatSPME can be superior in the collection of such volatiles.

As another example of the SPME technique in skin-odor analysis, Curran et al. [6] collected sweat sampleson sterile, 2 · 2, 8-ply, gauze sponges first and storedthem in 10-mL glass vials. The DVB-CAR on PDMSSPME fiber (50/30 lm) was used to extract the VOCsfrom the HS of the vials containing the gauze. Theseauthors were able to detect 54 VOCs through a com-bined application of HS-SPME and GC-MS.

In an analogue to this effort, HS-SPME was also ap-plied successfully for the determination of foot malodor[7]. These researchers collected the odor components ina 20-mL glass vial by washing the feet of subjects with aphysiological solution. The washing aliquots wereincubated at 36�C for subsequent HS-SPME extraction.On the basis of the combined application of HS-SPMEand GC-MS, a number of fatty acids, including acetic,butyric, isobutyric, and isovaleric acids, were measuredqualitatively as the main foot-odor components alongwith propionic, valeric, and isocaproic acids.

2.3. Mouth odorBecause of the delicacy involved in the collection ofgaseous samples, sample processing remains one of thecrucial issues in breath analysis. Dilution and contami-nation of samples with dead-space gas and loss of ana-lytes during the sampling procedure are suspected to bethe main causes of disagreement between differentstudies [1]. At present, there are two basic approachesfor the collection of breath samples:

(1) mixed expiratory (ME) sampling (total breathincluding dead-space air); and,

(2) alveolar sampling (pure alveolar gas).ME sampling has been used most frequently, as it is

easy to perform with spontaneously breathing subjectswithout additional equipment.

Studies dealing with mouth odorants have focused onthe analysis of both salivary content and exhaled breath.For example, Whittle et al. [1] analyzed salivary TMAlevels thorough a combination of HS-SPME and GC-MS.These authors also analyzed the reduced sulfur com-pounds [RSCs: COS, H2S, CH3SH, and (CH3)2S] presentin the oral cavity of human subjects by pulling 10 mL ofthe subjects� mouth air via an atmospheric samplingloop using a gas-tight syringe. These RSCs, whendetermined by GC-FPD, were 1.08 ppb, 2.17 ppb, 1.97ppb, and 0.21 ppb, respectively.

In another study aiming to assess oral malodor dueto halitosis, mouth air (10 mL) was aspirated with agas-tight syringe; subsequently, samples were injectedonto the GC column at 70�C interfaced with FPD [8].The level of RSCs was then defined as the total RSCs[H2S, CH3SH, and (CH3)2S)]. These authors were ableto differentiate mean RSC concentrations of patientsbetween before (0.56 ppm) and after the treatment(0.10 ppm).

In continuation of this effort, Awano et al. [9] alsoassessed oral malodor by means of the GC-FPD method.These authors injected the sample of mouth air into GCthrough a multi-port injector with a loop by controllingthe sample volume. Based on the logistic regressionmodel and the organoleptic test, they suggested thatmethane thiol may be the predominant causative factorof oral malodor and a more important diagnostic marker(than H2S and total RSC) for the prediction of oralmalodor. Murata et al. [5] also analyzed oral airsamples from randomly selected volunteers with both GC-semiconductor sensor (SCS) and a GC-FPD. These authorswere able to find good correlations in both methods forthe determination of H2S, CH3SH, and (CH3)2S.

In an effort to provide a real-time profile of acidcomponents in breath samples, Martinez-Lozano and Dela Mora [45] tested an atmospheric pressure ionizationmass spectrometer (API-MS) by negatively charging ex-haled vapors via contact with an electrospray cloud.These authors were able to demonstrate real-timedetection for a wide array of deprotonated fatty acids,including saturated chains up to C14 along with unsat-urated fatty acids (C7–C10), ketomonocarboxylic acids(C6–C10), and a family of aldehydes.

2.4. Urine odorIn order to study the profiles of urinary volatiles frompatients with diabetes mellitus, Zlatkis et al. [32] mixedurine samples with (NH4)2 SO4 in a flask and heated it




with boiling water. These authors collected the urinaryvolatiles on Tenax GC adsorbent by passing helium gasover the urine, and the volatiles were finally determinedthrough GC-MS. The method was good enough to dis-tinguish between normal subjects and those sufferingfrom diabetes mellitus, as there were marked differencesin the concentration of certain compounds (i.e. relativelyenhanced levels of pyrazine, cyclohexanone, lower ali-phatic alcohols, and octanols in patients).

In another study aiming to determine 4-heptanoneand its precursors in urine, the urinary volatiles wereextracted with cyclohexane in a single step and analyzedby GC-mass fragmentography [46]. These authorsachieved a limit of detection (LOD) of 8 pg with areproducibility range of 1.2% and 10% (in terms of rel-ative standard deviation, RSD).

To acquire a volatile profile of urine constituents frompatients suffering from several metabolic disorders,Burke et al. [34] extracted VOCs from a urine solutionusing volatile traps (Chromosorb 105). By conductingGC-MS analysis of the volatile profile, these authorssuccessfully differentiated patients from normal subjects.For example, the differences in observed marker levels,(e.g., 4-heptanone, benzaldehyde, p-cresol, and phenol)were sufficient to identify PKU. However, they alsohighlighted a saturation problem of the adsorption trapwith some major components and masking of someminor components by GC.

In other research aiming to establish urine odor as adiagnostic tool, TMA was determined with a combina-tion of SPME technique and isotope-dilution GC-MS [36].Both PDMS and mixed CAR–PDMS SPME fibers weresuitable for the HS extraction of TMA from urine sam-ples. They found that the recovery of TMA fell in a rangeof 75–85.5% and that sensitivity of the method was atleast 14.9 lmol/L.

In a study aiming to establish the odor profile of hu-man waste (urine and feces), volatile components werecollected using an adsorption tube filled with Tenax-TA,and their concentrations were determined by TD-GC-MS[38]. About 90% of the malodor-causing substanceswere fatty acids [i.e. acetic acid (40–120 ppm), propionicacid (5.30–27 ppm) and butyric acid (0.41–1.60 ppm)].The concentration of ammonia was in the range 18–34 ppm. Moreover, other malodor-causing substances[e.g., indole (0.02–0.35 ppm), skatole (0.10–0.48 ppm),pyridine (0.03–0.23 ppm), pyrrole (0.01–0.02 ppm),hydrogen sulfide (19–50 ppm), and methyl mercaptan(0.70–1.10)] were also quantified.

As seen in many studies, urine odorants can also beused as an efficient means to diagnose certain healthdisorders. However, if we screen the available literature,most of the assessments of urine-derived odorants assuitable biomarkers for clinical purposes are in theirpreliminary stages with qualitative data sets.

3. Application of human BO in health diagnosis

The concurrent developments in medical and analyticaltechnologies can be nominated as the driving force toadvance monitoring approaches for BO and/or urinesamples toward clinical diagnostics. There is a consid-erable line of evidence that analyses of human BO (e.g.,breath volatiles) can be used as a potential tool forestablishing biomarkers of many internal diseases. Forexample, nitric oxide (NO) has been the most exten-sively exhaled marker for a host of respiratory ail-ments, including chronic obstructive pulmonarydisease (COPD), rhinitis, rhinorrheachronic cough(primary), asthma, cystic fibrosis, bronchiectasis, andlung cancer [47]. Moreover, numerous breath volatileshave also been studied as exhaled markers of diseases,including:

(1) acetone and other ketones [48,49] for diabetesmellitus;

(2) breath methylated alkane contour (BMAC) forheart-transplant rejection [50];

(3) leukotrienes for asthma [51–53];(4) 2-propanol, 2,3-dihydro-1-phenyl-4 (1H)-qui-

nazolinone, 1-phenyl-ethanone, and heptanalfor breast cancer [28,54];

(5) acetone, methylethylketone and n-propanol forlung carcinoma [55];

(6) aniline and o-toluidine for lung carcinoma [56];(7) alkanes, mono-methylated breath alkanes, and

alkenes for lung carcinoma [57];(8) hydrogen peroxide [58], nitrosothiols [7], and ni-

tric oxide [59] for COPD;(9) 8-isoprostane [60] and leukotriene B4, iterleu-

kin-8 [61] for cystic fibrosis; and,(10) hydrogen disulfide and limonene [62], ethaneth-

iol, dimethylsulfide [63], and dimethylamine andtrimethylamine [64–66] for liver diseases.

If some of the above applications are examined fur-ther, BMAC has the potential to be a biomarker of heart-transplant rejection [50]. These authors collected a totalof 1061 breath VOC samples from 539 heart-transplantrecipients before a scheduled endomyocardial biopsy.Based on GC-MS analysis of these breath samples, theyrevealed that transplant rejection can be accompaniedby oxidative stress, which degrades membrane polyun-saturated fatty acids, evolving alkanes and methylalk-anes; these components are excreted in the breath asVOCs.

In another study conducted by the same study group,it was also reported that certain VOCs (e.g., 2-propanol,2, 3-dihydro-1-phenyl-4(1H)-quinazolinone, 1-phenyl-ethanone, heptanal, and isopropyl myristate) can beregarded as potential biomarkers of breast cancer [28].However, they also indicated that the method should bevalidated further.




Tab

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Com

pil

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applied

toth

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type

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ass

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ds

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liquid

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nat

the

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dof

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colu

mn

Ther

mal

des

orp

tion

insi

de

the

inje

ctor

GC

-MS

[21]

3H

um

anbody

Agi

ng

effe

ct2-N

onen

al+

(hyd

roca

rbons,

alco

hols

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ids,

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)

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ecti

on

on

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ax-T

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action

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9]

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MS)

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lm/3

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upel

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SPM

Ein

toth

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ctor

GC

-MS

[6]

5A

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ary

secr

etio

ns

Vola

tile

carb

oxy

lic

acid

sC

ott

on

pad

sLi

quid

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eex

trac

tion

wit

hhyd

roly

sis

and

N-a

cyl-

gluta

min

eam

inoac

ylas

e

DI

GC

-MS

[13]

6H

um

ansk

in100

VO

Cs

2cm

,50-l

m/3

0-l

mdiv

inyl

ben

zene/

carb

oxe

n/

poly

dim

ethyl

silo

xane

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leflex

fiber

s(S

upel

coC

orp

.,B

elle

fonte

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.).

SPM

EG

C-M

San

dG

C-F

PD

[44]

7H

um

ansk

in100

VO

Cs

Extr

action

of

skin

secr

etio

ns

by

solv

ents

-50/5

0m

ixtu

reof

double

-dis

tilled

ethan

ol/

singl

e-dis

till

edhex

anes

DI

wit

hsy

ringe

GC

-MS

and

GC

-FPD

[44]

8Fo

ot

mal

odor

Ace

tic

acid

,buty

ric

acid

,is

obuty

ric

acid

,is

ova

leri

cac

id,

pro

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nic

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leri

cac

id,

and

isoca

pro

icac

id

SPM

ESP

ME

SPM

Ein

toth

eG

C-

inje

ctor

GC

-MS

[70]

9H

um

anm

ale

axil

lae

C6

toC

11st

raig

ht-

chai

n,

bra

nch

ed,

and

unsa

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ted

acid

s

GC

-MS

and

GC

-FTIR

[4]

10

Hum

anax

illa

rysw

eat

VO

Cs

Gau

zesp

onge

s(D

uka

lC

orp

ora

tion,

Syoss

et,

NY

,U

SA)

HS-

SPM

Eon

DV

B/C

AR

on

PD

MS)

50-l

m/3

0-

lm

fiber

s(S

upel

co)

SPM

Ein

toth

eG

C-

inje

ctor

GC

-MS

[6]



Author's personal copy[A

]G

C-b

ased

met

hods

Ord

erB

ody-

odor

type

Rem

arks

Tar

get

com

pounds

Sam

ple

coll

ecti

on

Pre

trea

tmen

t/ex

trac

tion

Sam

ple

tran

sfer

Det

ecti

on

met

hod

Ref

.

11

Hum

anhai

ran

dsc

alp

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anes

,al

kenes

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cohols

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amic

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1]

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Hum

anax

illa

rysw

eat

and

sebum

Anal

ysis

of

laundry

soil

edw

ith

hum

anax

illa

rysw

eat

and

sebum

Este

rs(e

thyl

-2-

met

hyl

pro

pan

oat

ean

det

hyl

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noat

e),

keto

nes

(1-

hex

en-3

-one

and

1-o

cten

-3-

one)

and,

par

ticu

larl

y,al

deh

ydes

[(Z

)-4-h

epte

nal

,oct

anal

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cten

al,

met

hio

nal

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)-2-n

onen

al,(E

,Z)-

2,6

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ienal

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,Z)-

2,4

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ienal

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,E)-

2,4

-dec

adie

nal

,an

d4

met

hoxy

ben

zald

ehyd

e]

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tile

swat

ches

(100%

cott

on

or

100%

poly

este

r,21

·14.8

cm)

Double

-dis

till

eddie

thyl

ether

DI

HR

GC

-MS

[71]

[B]

Online

met

hods

Ord

erB

ody-

odor

type

Rem

arks

Tar

get

com

pounds

Sam

ple

coll

ecti

on

Det

ecti

on

met

hod

Ref

.

1H

um

ansk

inA

cids,

carb

onyl

s,al

cohol,

ster

oid

sC

hem

ical

senso

rs[7

2]

2A

rmpit

secr

etio

ns

Effe

ctof

free

zing

the

sam

ple

sIn

term

sof

rate

dva

riab

les

(i.e

.ple

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tnes

s,at

trac

tive

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ascu

linit

y,an

din

tensi

ty)

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on

pad

s(E

bel

inco

smet

icpad

s,D

M-

dro

geri

emar

kt,

Ces

keB

udej

ovi

ce,

Cze

chR

epublic)

Odor

sense

dby

hum

anolf

acti

on

[15]

3H

um

anax

illa

rysw

eat

and

sebum

Anal

ysis

of

laundry

soil

edw

ith

hum

anax

illa

rysw

eat

and

sebum

Aro

ma

pro

file

by

pan

elof

9Tex

tile

swat

ches

(100%

cott

on

or

100%

poly

este

r,21

·14.8

cm)

Senso

ryev

aluat

ion

[71]

4H

um

an-s

kin

vapors

Han

dsk

inLa

ctic

acid

+sa

tura

ted

and

singl

yunsa

tura

ted

fatt

yac

ids

(C12–C

18)

Elec

trosp

rayc

ham

ber

Rea

l-ti

me

QTO

F-M

S[1

5]




Tab

le2.

Com

pil

atio

nof

met

hods

for

the

det

erm

inat

ion

of

hum

anm

outh

odor

[A]

GC

-bas

edm

ethods

Ord

erB

ody-

odor

type

Tar

get

com

pounds

Sam

ple

coll

ecti

on

Pre

trea

tmen

t/ex

trac

tion

Sam

ple

tran

sfer

Det

ecti

on

met

hod

Ref

.

1B

reat

h(S

aliv

a)Tri

met

hyl

amin

eSo

lid-p

has

em

icro

extr

ac-t

ion

(SPM

E)SP

ME

SPM

Ein

toth

eG

C-i

nje

ctor

GC

-MS

[1]

2B

reat

h(m

outh

air)

RSC

s(c

arbonyl

sulfi

de,

hyd

roge

nsu

lfide,

met

han

eth

iol,

and

dim

ethyl

sulfi

de)

Soli

d-p

has

em

icro

extr

ac-t

ion

(SPM

E)SP

ME

SPM

Ein

toth

eG

C-i

nje

ctor

GC

-FPD

[1]

3B

reat

h(m

outh

air)

RSC

s(h

ydro

gen

sulfi

de,

met

han

eth

iol,

and

dim

ethyl

sulfi

de)

GC

-FPD

[18]

4O

ral

mal

odor

Tota

lR

SCs

(hyd

roge

nsu

lfide,

met

han

eth

iol,

and

dim

ethyl

sulfi

de)

Gas

-tig

ht

syri

nge

Dir

ect

inje

ctio

nG

C-F

PD

[8]

5O

ral

mal

odor

Met

han

eth

iol

Loop-b

ased

GC

-FPD

[9]

[B]

Onli

ne

met

hods

Ord

erD

iagn

osi

s(d

isea

sest

ate)

Tar

get

com

pounds

Det

ecti

on

met

hod

Ref

.

1B

reat

hm

alodor

Vola

tile

sR

eal-

tim

eat

mosp

her

icpre

ssure

ioniz

atio

n(A

PI)

MS

[10]

2B

reat

hm

alodor

Satu

rate

dV

OC

sup

toC

14

+unsa

tura

ted

C7–C

10

+ke

tom

onoca

rboxy

lic

acid

s(C

6-C

10)

+al

deh

ydes

Rea

l-ti

me

API

[45]

3K

etosi

sA

ceto

ne

Pt-

In2O

3se

nso

r[7

3]

4D

iabet

esA

ceto

ne

Cav

ity

ringd

ow

nsp

ectr

osc

opy

(CR

DS)

[47]

5B

reat

hodor

from

smoki

ng

Ethyl

ene

MES

I-G

C[7

5]

6B

reat

hodor

Fatty

acid

sH

PLC

-ele

ctro

spra

yta

ndem

mas

ssp

ectr

om

etry

[78]

7B

reat

hodor

Ace

tone

Dif

fere

nti

alm

obil

ity

spec

trom

etry

(DM

S)[7

7]

8B

reat

hodor

Ace

tone

Enzy

mat

icel

ectr

och

emic

alse

nso

r[7

6]

9B

reat

han

alys

isA

ceto

ne,

isopre

ne,

amm

onia

,et

han

ol,

acet

aldeh

yde

Sele

cted

ion

flow

tube

mas

ssp

ectr

om

etry

(SIF

T-M

S)[7

9]




4. Detection methods for human BO

4.1. GC-based methodsFor the instrumental detection of odor components, moststudies have relied on GC-based approaches interfacedwith universal detection systems (e.g., flame ionizationdetection (FID), FPD and MS) [67–69]. As the odorcomponents released from human body are mostlyVOCs, GC has been the most common choice with itsexcellent separation capacity (Table 1). Moreover, theapplication of universal detectors (e.g., MS) offers a toolto detect a broad spectrum of components emanatingfrom the human body. For example, the application ofthe GC-MS system with TD allowed qualitative determi-nation for a total of 346 VOCs in human-skin emana-tions (including carboxylic acids, aldehydes, alcohols,aliphatics and aromatics, amides, amines, esters, halides,ketones, sulfides, and sulfonyls) [21,22].

GC-MS was also employed successfully in combinationwith HS-SPME to determine a number of acids qualita-tively as foot-malodor components [70]. Munk et al. [71]have shown that high-resolution GC combined with MS(HRGC-MS) accompanied by solvent extraction can besuitably utilized to detect a number of odor components(e.g., esters, ketones, and aldehydes) present in humanaxillary sweat.

In another application of GC-MS, 100 VOCs wereidentified as components of human-skin emanation.These authors also employed an additional selectivetechnique (i.e. FPD to identify volatile sulfur species). Inaddition to the determination of skin-odor components,GC-MS and GC-FPD have been employed mainly for thedetermination of odor components from the mouth aswell as urine (Tables 2 and 3).

Although the application of GC with MS offered avariety of information concerning human BO compo-nents, many of such attempts have still been limited tothe qualitative analysis of data. As such, most of themhave been unable to support the data with basic qualityassurance (LOD, reproducibility, and accuracy) or areliable range of determination for the target compoundsthat is essential to designate them as suitable biomark-ers.

4.2. Online methodsIn recent years, several researchers have shown thatsensor-based methods with various combinations can beemployed to monitor some selective biomarkers of hu-man odor [72] (Tables 1–3). For example, Neri et al. [73]monitored acetone in human-breath samples by meansof a metal-oxide semiconductor (based on In2O3 andPt-In2O3 nanopowders). By designating acetone as amarker of ketosis disease, these authors tested theperformance of their sensors in a concentration range1–100 ppm. However, their monitoring was affectedsignificantly by changes in relative humidity and tem-perature conditions.

In an analogous effort, Wang et al. [47] attempted tomonitor acetone as a biomarker of diabetes. Theseauthors built a pilot-scale breath-acetone analyzer basedon the cavity-ringdown spectroscopy (CRDS) techniqueand conducted breath tests with 34 Type 1 diabetic(T1D) patients, 10 type 2 diabetic (T2D) patients, and 15(apparently) healthy individuals. They were able to dis-tinguish acetone levels between healthy individuals(0.48 ppm) and patients (2.19 ppm). Shimouchi et al.[74] also developed a skin-gas sampling system to ana-lyze human breath and skin gas emanating duringexercise with API-MS. However, the effort was limited toassigning mass-intensity patterns only and provided aclue for further non-invasive studies on exercise effects.

In another study, Morley and Pawliszyn [75] proposedan on-line method to monitor ethylene in human breathwith the aid of membrane-extraction technique bycombining a sorbent interface (MESI) and GC. Theseauthors tested their methods to characterize and toquantify the ethylene-breath biomarker in two types ofhuman subjects (i.e. healthy and smoking volunteers)through a calibration method based on the isotropicrelationship between target analyte and calibrant. Theseauthors were able to detect ethylene in the range 7–16 ppb (healthy individuals) and 53–221 ppb (smokingindividuals).

Landini and Bravard [76] designed an enzymaticelectrochemical breath-acetone sensor for the analysis ofbreath condensate and reported the performance of theirsensor in detecting acetone at a level of 1 ppm. In

Table 3. Compilation of on-line and GC-based methods for the determination of human-urine odor

Order Target compounds Sample collection Sample transfer Detection method Ref.

1 2-butanone, 2-pentanone, 4-heptanone,dimethyl disulfide,alkyl furans, pyrrole, and carvone

Tenax GC Thermal desorption GC-MS [32]

2 4-heptanone, benzaldehyde,p-cresol, and phenol

Chromosorb 105 GC-MS [34]

3 Trimethyl amine SPME SPME GC-MS [36]4 4-heptanone Extraction from urine in liquid

phase with cyclohexaneDirect injection GC-MS [46]

5 3-hydrobutyric acid and acetone SIFT-MS Urine-headspace analysis SIFT-MS [79]




contrast, Davis et al. [77] proposed a method to analyzeVOCs in exhaled breath condensate (EBC) with a highlysensitive biochemical-characterization sensor systemthat relied on GC combined with differential mobilityspectrometry (DMS). These authors were able todetect acetone in EBC at concentrations of as low as100 ppt. Five fatty acids [i.e. 9,12,13-trihydroxyocta-decenoic acid (9,12,13-TriHOME), 9,10,13-TriHOME,12,13-dihydroxyoctadecenoic acid (12,13-DiHOME),12-hydroxyeicosatetraenoic acid (12-HETE), and 12(13)-epoxyoctadecenoic acid (12(13)-EpOME)] were alsomonitored in EBC samples through high performanceliquid chromatography (HPLC) combined with electro-spray tandem MS (HPLC-ESI-MS); this method washighly sensitive, with an on-column limit of quantitation(LOQ) in the pg range [78].

In recent years, selected ion flow tube MS (SIFT-MS)has been applied to the real-time quantification of trace-gas components in human breath and urine-HS [79].This technique relies on chemical ionization of the trace-gas molecules in air/breath samples introduced intohelium carrier gas using H3O+, NO+, and Oþ2 precursorions. The exhaled breath-gas sample or HS gas fromurine is allowed to enter the sampling port for reactionwith produced precursor ions for a given duration. Thefinal detection is achieved with a quadrupole MS. Al-though the full extent of the applicability of SIFT-MS stillremains to be validated, its employment has benefitedreal-time non-invasive breath analysis and urine HSanalysis through clinical diagnosis, therapeutic moni-toring, and physiology studies at the ppb level (down to10 ppb). For example, the temporal profiles of commonbreath metabolites were obtained from 5 healthy vol-unteers over a 30-day period [80]. Breath samples wereanalyzed on a daily basis in early morning for threeconsecutive breath exhalations. The mean concentra-tions (ppb) of chemicals were in the ranges 422–2389(ammonia), 293–870 (acetone), 55–121 (isoprene), 27–153 (ethanol), and 2–5 (acetaldehyde).

In another study, to investigate the influence of foodintake (and starvation) on the levels of breath metabo-lites (acetone, ammonia, ethanol, and isoprene), alveolarbreath samples were analyzed by SIFT-MS after feedingthe volunteers with liquid protein [81] and carbohydratemeals [82]. They were able to study the variation intarget metabolites in the ppb range before and afterfeeding. Using SIFT-MS, Smith et al. [81] monitoredisoprene, one of the most abundant hydrocarbons inhuman breath, in healthy volunteers at 83 ± 45 ppb.SIFT-MS was also applied to investigate ethanol metab-olism by comparing its amount in breath and blood (inHS of 5-mL blood) after alcohol ingestion in parallel withstandard hospital forensic procedures for blood-alcoholanalysis [83]. The application of SIFT-MS was successfulin identifying the changes in acetone and ammonialevels in HS-urine samples of female volunteers [84].

These authors correlated the 3–12 fold increase of ace-tone to the ovulation phase in menstrual cycle.

In another study, Wang et al. [85] applied SIFT-MSto detect 3-hydrobutyric acid (3-HBA), a ketone bodyfor diagnosis of ketoacidosis, along with acetone inhealthy volunteers. They were able to detect 3-HBA atthe 40 ppbv level and concluded that SIFT-MS can beapplied as a rapid analytical method for screeningdiabetes, as the levels of 3-HBA fell in the ppm rangefor patients. In addition to these studies, there havebeen a number of pilot studies to check the potential ofSIFT-MS for screening disease states [e.g., urinaryinfection (oxides of nitrogen as marker) and cancer(formaldehyde and acetaldehyde as marker)] by ana-lyzing urine HS samples {[79] and references therein}.Moreover, it was pointed out that the analysis of ex-haled breath can reveal conditions of renal failure andgastrointestinal tract infection and can be extended todetect drug abuse.

5. Current status, limitations and future prospects

The analytical techniques applied to the determination ofhuman BO components have advanced to offer a uniqueopportunity for the establishment of suitable biomarkersfor disease-state monitoring and assessing environmen-tal exposure to pollutants. In this respect, most availableresearch has been directed to the analysis of axillarysecretions, skin emanations and oral malodor (salivarycontents or breath volatiles).

In order to determine the axillary odor components,many studies collected sweat samples from armpit areaswith pads (or sponges) or on fiber materials. The odorcomponents were then commonly retrieved by means ofsome solvent-based extraction technique or SPME forfinal determination with GC-based methods. In contrastto axillary odor, the collection of breath volatiles is ra-ther delicate, as it can suffer from contamination ofsamples with dead-space gas and/or dilution of samples.Undoubtedly, these biases in the sampling procedure cancause underestimation of analytes. At present, mixedexpiratory (ME) sampling (total breath including dead-space air) has been used most frequently, as it facilitatescollection from spontaneously breathing subjects with-out additional equipment.

For the detection of the odor components releasedfrom human body, most approaches were developedcommonly on the basis of GC techniques employinguniversal (e.g., FID or MS) or selective (e.g., FPD forsulfur) detectors. Employment of GC-based methods hasbeen quite fruitful in allowing us to learn more aboutmany volatile biomarkers for health diagnosis. In spite ofthe great success of these GC methods in qualitativeanalysis of human BO, our knowledge is still limited inobtaining and building up the quantitative data sets




mainly due to limitations in pre-concentration method-ologies used prior to analysis.

In recent years, there have been a number of efforts todevelop more efficient and convenient on-line methodsfor monitoring of some selective biomarkers (e.g., ace-tone and ethylene) based on sensing techniques. Thesesensor-based methods have been successful in offering agreat deal of convenience, which is potent enough toreplace tedious off-line analytical protocols. However, inmost cases, there are significant gaps in their applicationwith respect to their selectivity of target and scale ofdetectability: note that most of the biomarkers exist atppb or sub-ppb levels, while most sensors tend to detecttargets at the ppm level. For real-time analysis of breathand urine-HS gas, SIFT-MS has offered an excellent op-tion that can detect a number of marker species in theppb range (down to 10 ppb).

Considering many technological restrictions in thedetermination of human BO, there is a pressing need todevelop the following fields of study:(1) more deliberate applications of the GC-based

methods to reflect all basic quality assurance in aquantifiable manner;

(2) improvement of absolute sensitivity and selectivityfor on-line detection methods (sensor-basedmethods) to cover the low level biomarkers as wellas expansion of target compounds.

Moreover, research needs to focus on additional sourcesof odor components for human BO with the aid of ad-vanced analytical techniques. More importantly, asmany analytical techniques are sensitive enough to de-tect diverse human-BO components, immediate effortsare desirable to push technological advancement in theirsampling and/or preconcentration. These developmentsshould definitely contribute to the build up of a database,especially in the field of clinical diagnosis.

AcknowledgementsThis work was supported by a Grant from the NationalResearch Foundation (NRF) of Korea funded by theMinistry of Education, Science and Technology (MEST)(No. 2009-0093848). We gratefully acknowledge thevaluable comments provided by the two anonymousreviewers who helped improve the final version of thismanuscript.

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human body-odor components and their determination

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