BY-NC-ND 3.0 license Open Access Published by De Gruyter December 21, 2016

Characterization of human body odor and identification of aldehydes using chemical sensor

Sunil Kr. Jha

Abstract

Human body odor is a unique identity feature of individual as well as an established composite of numerous volatile organic compounds (VOCs) belonging to significant chemical classes. Several analytical methods have been used in the characterization of human body odor in order to recognize the chemical composition of VOCs in medical, forensic, and biometric applications. Besides, real-time sensing systems (based on the chemical sensors) are being researched and developed for qualitative and quantitative recognition of VOCs in body odor. The present review focuses the state-of-the-art research outcomes related to the characterization of human body odor with the objective to identify the VOCs belonging to aldehyde class. Furthermore, the application of chemical sensors in past studies for the detection of aldehydes besides other chemical compounds in body odor is summarized and the significance of aldehydes detection in different applications is discussed.

Introduction

Body odor is produced by skin bacteria such as Brevibacterium, Propionibacterium acnes, Corynebacterium, Staphylococcus hominis, Micrococcus luteus, and Staphylococcus epidermidis, etc. (Hart 1980, Zeng et al. 1991, Inaba and Inaba 1992, Rindisbacher 1992, Grice et al. 2009, Yamazaki et al. 2010). Skin bacteria decompose secretion outcomes (oil/wax, salts, proteins, etc.) of the sweat glands (eccrine, apocrine, etc.), which results in the complex composition of volatile organic compounds (VOCs) belonging to several chemical classes, including aldehyde, acid, amine, alcohol, hydrocarbon, ketones, sterols, sulfur compounds, and terpenoids, generating human body odor (Amoore 1977, Fang et al. 1998, Toan et al. 1999, Clancy and McVicar 2002, Jain 2004, Statheropoulos et al. 2005, Havlicek and Lenochova 2006, Steeghs et al. 2006, Wedekind et al. 2006, Penn et al. 2007, D’Amico et al. 2008b, Preti and Leyden 2010, Wisthaler and Weschler 2010, Yamazaki et al. 2010, Seeley et al. 2011, Thorn and Greenman 2012, Agapiou et al. 2015a,b, Buljubasic and Buchbauer 2015, Sorokowska et al. 2015, Allen et al. 2016, Colón-Crespo et al. 2016, Fialová et al. 2016, Gildersleeve et al. 2016, Prokop-Prigge et al. 2016, Sorokowska et al. 2016, Stefanuto and Focant 2016, Verhulst et al. 2016, Zuniga et al. 2016). Growth of bacteria living on the skin is reinforced by the secretions of body fluids from the different glands like pheromones and fatty acids (apocrine sweat glands) (Clancy and McVicar 2002, Seeley et al. 2011), ceruman (ceruminous gland) (Clancy and McVicar 2002), sebum (sebaceous glands) (Seeley et al. 2011), etc. and contributes in body odor. Human sebum is formed by UV, or O3 induced peroxidation of unsaturated fatty acids (Osada et al. 2004, Steeghs et al. 2006, Wisthaler and Weschler 2010). The apocrine sweat glands (found in the axilla, areola, and anogenital region) have a major influence in the body odor compared to the other.

On the basis of origin, human body odor is classified into three main categories: primary, secondary, and tertiary (Amoore 1977). The primary body odor is a distinguishing attribute of an individual, which varies with age (Haze et al. 2001, Yamazaki et al. 2010, Sorokowska et al. 2015), ethnicity (Colón-Crespo et al. 2016, Prokop-Prigge et al. 2016), gender (Penn et al. 2007, Colón-Crespo et al. 2016), body parts (face, neck, breath, axilla, foot) (Gaffar et al. 1977, Yamazaki et al. 2010, Liu et al. 2013, Jha et al. 2014a, Hara et al. 2015, Fialová et al. 2016), body condition (unhealthy or healthy) (Thorn and Greenman 2012), fertility status (Gildersleeve et al. 2016), and genetic feature (Wedekind et al. 2006, Preti and Leyden 2010), etc. It has significant contribution in medical (D’Amico et al. 2008b, Buljubasic and Buchbauer 2015), security, safety and forensic (Jain 2004, Statheropoulos et al. 2005, Agapiou et al. 2015a,b, Stefanuto and Focant 2016), cosmetic (Toan et al. 1999, Allen et al. 2016, Sorokowska et al. 2016, Verhulst et al. 2016) applications, etc. Secondary odor is produced mainly with specific diets (meat, onion, ginger, garlic, etc.) (Havlicek and Lenochova 2006, Fialová et al. 2016, Zuniga et al. 2016), living environmental conditions (humidity, temperature, pressure, etc.) (Fang et al. 1998), etc. The usage of body soap and shampoo, perfumes, and deodorants causes a temporary, tertiary odor. The determination of the chemical composition of body odor is essential in specific applications like health monitoring, forensic investigation, biometric recognition, etc. In the beginning, canines and human sensory panels were used for the human body odor sensing. Afterward, highly sophisticated instrumental analytical methods were implemented for the qualitative and quantitative chemical composition determination of body odor. Currently, several chemical sensor-based approaches, singly or in combination with analytical methods, are in use for the real-time and fast recognition of VOCs in body odor. Past research has presented the development and future prospects of human body odor, and their related component, VOC, recognition in specific applications is summarized in some reviews (Prada and Furton 2008, Pandey and Kim 2011, Li 2014). A schematic representation of human body odor, their components, sources of origin, affecting factors, etc., is shown in the Figure 1.

Figure 1: Schematic representation of human body odor details.

Figure 1:

Schematic representation of human body odor details.

Year-wise variation in the total number of published reports related to body odor research in between the years 1946 and 2015 is shown in the Figure 2 (Web of Science). It represents the superfluous research and development in last two decades.

Figure 2: Published reports on body odor research (Web of Science).

Figure 2:

Published reports on body odor research (Web of Science).

Characterization of body odor

Different analytical methods have been used in the characterization of human body odor with the objective to determine the precise chemical composition of VOCs; among them, solid phase microextraction gas chromatography mass spectrometry (SPME-GC-MS) is the most common and widely used method in published literature (Zeng et al. 1991, Penn et al. 2007, Yamazaki et al. 2010, Thorn and Greenman 2012, Dormont et al. 2013b, Liu et al. 2013, Jha et al. 2014a,b, Jha and Hayashi 2015a,b, Jha et al. 2015). Besides that, GC-Fourier transform infrared spectroscopy (FTIR) (Zeng et al. 1991, Zeng et al. 1996), high-resolution GC-MS (Munk et al. 2000), thermal desorption GC-MS (Bernier et al. 1999, Bernier et al. 2000, Penn et al. 2007), atmospheric pressure ionization (API)-MS (Martínez-Lozano and Mora 2008, Martínez-Lozano and de la Mora 2009, Martínez-Lozano 2009), GC-flame photometric detector (FPD) (Gaffar et al. 1977, Risby et al. 2001), cavity ringdown spectroscopy (CRDS) (Wang and Mbi 2007, Wang et al. 2010, Ciaffoni et al. 2012), laser spectroscopy (Mürtz 2005, Wojtas et al. 2012, Adonis et al. 2014), high-performance liquid chromatography (HPLC) (Osada et al. 2004, Ishino et al. 2010, Hara et al. 2014, Ozeki and Moro 2016), selected ion flow tube (SIFT)-MS (Enderby et al. 2009, Spanel and Smith 2011, Turner 2011), electrospray tandem-mass spectrometry (Johnson 2008), liquid chromatography-tandem mass spectrometry (LC-TMS) (Rafii et al. 2009, Lee et al. 2010), proton transfer reaction mass spectrometry (PTR-MS) (Steeghs et al. 2006, Wisthaler and Weschler 2010, Yao et al. 2015), selective reagent ionization time-of-flight mass spectrometry (SRI-TOF-MS) (Mochalski et al. 2014), and ion mobility spectrometry (IMS) (Ruzsanyi et al. 2012, Vautz et al. 2013), etc., methods have been also used in the characterization of body odor. Further details of sampling strategies, separation methods, and identification mechanisms, etc., for body odor characterization in some of the previous studies is summarized in Table 1, while characterization outcomes of odor samples from different parts of body using different analytical methods are compiled in Table 2.

Table 1:

Experimental details of few odor characterization studies.

ReferenceAnalytical approachSampling strategySeparation methodIdentification mechanism
Zeng et al. 1991GC-MSHuman sensory panel for olfactory samplingStabilwax coated columnNBS library and spectra of synthetic compounds
Penn et al. 2007Polydimethylsiloxane (PDMS)-coated barDB-5MS capillaryAligning peaks with identical spectra and elution time
Dormont et al. 2013bSolvent extraction, SPME, chromatoprobeID WCOT CPSil-8CB capillaryNIST library and other published sources
Munk et al. 2000Ranking method by human sensory panelCapillary columnReference compounds
Zeng et al. 1996GC-FTIRHuman sensory panel for olfactory samplingStabilwax coated columnNBS library and spectra of synthetic compounds
Bernier et al. 1999, 2000Thermal desorption GC-MSLiquid nitrogen for cold trappingHP5 and HP-FFAP columnsMatching the spectra of a library
Martínez-Lozano and Mora 2008, Martínez-Lozano 2009, Martínez-Lozano and de la Mora 2009API-MSSampling tubesSilica capillaryWeb database mass bank
Gaffar et al. 1977GC-FPDGas sampling valveCapillary chromatographic separationComparing retention volume, mass spectra and calibration curve
Risby et al. 2001, Wang and Mbi 2007CRDSFree diffusion and typical breath collection bagsPhotomultiplier tube (R74000U-09 Hamamatsu)Change in ringdown time
Hara et al. 2014HPLCAuto-sampler (SIL-20 A)Photodiode detectorComparison of retention time
Ozeki and Moro 2016Thermal desorption rodsColumn separationToluene equivalent
Enderby et al. 2009SIFT-MSDirect samplingQuadrupole mass spectrometer and ion counting systemSIFT-MS kinetics database
Ruzsanyi et al. 2012IMS-GCCylindrical steel potColumn separationComparison of retention and drift time with NIST library
Vautz et al. 2013Sampling loop methodColumn separationComparing mobility and retention time with ISAS database
Table 2:

Summary of some body odor characterization studies.

ReferenceBody odor typeCharacterization methodOutcomes
Zeng et al. 1991Axilla odorGC-MS and GC-FTIRC6–C11 saturated and unsaturated acids as key constituents
Zeng et al. 1996GC-FTIROrganic acids as the characteristic VOC in both the male and female
Martínez-Lozano and Mora 2008Breath odorAPI-MSOrganic acids (C6–C10) and aldehydes as main VOCs
Gaffar et al. 1977GC-FPDSeveral VOCs of different chemical classes were established
Risby et al. 2001GC-FPDVOCs related to hepatic disorder
Wang and Mbi 2007CRDSDetection of acetone (type 1 diabetes biomarker)
Wang et al. 2010CRDSDiscrimination of type 1 and type 2 diabetic patient and healthy subjects on basis of acetone detection
Ciaffoni et al. 2012CRDSMonitoring of acetone
Adonis et al. 2014Laser spectroscopyAcetone concentration estimation in type 1 diabetes patients
Enderby et al. 2009SIFT-MSEstimation of VOCs concentration in ppb level
Ligor et al. 2008SPME-GC-MSIdentification of 38 chemical compounds
Ligor et al. 2009SPME-GC-MSIdentification of 103 chemical compounds
Wisthaler and Weschler 2010Skin odorPTR-MSChemical compounds from carbonyl, carboxyl, groups
Steeghs et al. 2006PTR-MSAcetaldehyde, propanal, and other VOCS
Dormont et al. 2013bSPME-GC-MS44 VOCs identified in feet odor of 26 subjects
Bernier et al. 1999Thermal desorption GC-MSLactic acid, aliphatic fatty acids as major VOCs
Bernier et al. 2000Thermal desorption GC-MS346 chemical compounds recognized effectively
Martínez-Lozano and de la Mora 2009API-MSLactic acid and C12–C18 saturated acids as significant VOCs
Hara et al. 2014HPLCExplanation of diacetyl metabolism
Mochalski et al. 2014SRI-TOF-MSDetection of aldehydes, ketones, and other VOCs
Ruzsanyi et al. 2012IMS-GCDetection of aldehydes, ketones, and other VOCs
Osada et al. 2004Urine odorHPLCSeveral VOCs including 2-phenylacetamide, indole, phenol, etc.
Johnson 2008EST-MSDetection of genetic disorder metabolites
Rafii et al. 2009LC-MSRecognition of homocysteine and associated metabolites
Lee et al. 2010LC-MSDetection of amines
Liu et al. 2013Sweat odorSPME-GC-MSOrganic acids as the major chemical constituents besides other VOCs
Munk et al. 2000High-resolution GC-MSEsters, ketones and aldehydes as primary VOCs
Penn et al. 2007GC-MSIndividual and gender-specific VOCs in biometric and disease diagnostic applications
Jha et al. 2014aFace odorSPME-GC-MSCharacteristics and common VOCs of four subjects
Jha et al. 2014bFoot odorSPME-GC-MSOrganic acids and aldehydes as the major chemical constituents besides other VOCs
Jha et al. 2015Neck odorSPME-GC-MSCharacteristics and common VOCs at different sampling time of subjects
Martínez-Lozano 2009Hand odorAPI-MSAmines as the significant VOCs
Vautz et al. 2013Trapped body odorIMS-GCDetection of aldehydes, ammonia, ketones, and other VOCs

Existence of aldehydes in body odor

As discussed previously, human body odor is a complex medium of various VOCs of several chemical classes. Among them, both saturated and unsaturated aldehydes were recognized as the main constituents of human body odor in many studies (Ruth 1986, Goetz et al. 1988, Kubota et al. 1994, Bernier et al. 1999, Bernier et al. 2000, Munk et al. 2000, Haze et al. 2001, Curran et al. 2005, Zhang et al. 2005, Curran et al. 2007, Penn et al. 2007, Gallagher et al. 2008, Vass et al. 2008, Hoffman et al. 2009, Vaglio et al. 2009, Ruzsanyi et al. 2012, Dormont et al. 2013a,b, Liu et al. 2013, Vautz et al. 2013, Jha et al. 2014a,b, Mochalski et al. 2014, Jha and Hayashi 2015a,b, Jha et al. 2015) in different organs, for instance, in axilla, sweat, saliva, and urine odors (Penn et al. 2007); feet odor (Dormont et al. 2013b); axilla, neck, and forehead odor (Liu et al. 2013); axilla, face, foot, neck, and forehead odor (Jha et al. 2014a,b, Jha and Hayashi 2015a,b, Jha et al. 2015); sweat/sebum odor (Munk et al. 2000); skin odor (Bernier et al. 1999, Bernier et al. 2000); skin odor (Haze et al. 2001); scalp odor (Goetz et al. 1988, Kubota et al. 1994, Zhang et al. 2005); sweat odor (Vaglio et al. 2009); skin odor (Gallagher et al. 2008); and hand odor (Curran et al. 2007) etc. Table 3 summarizes the list of aldehydes identified in the past few studies(Ruth 1986, Goetz et al. 1988, Kubota et al. 1994, Bernier et al. 1999, Bernier et al. 2000, Munk et al. 2000, Haze et al. 2001, Curran et al. 2005, Zhang et al. 2005, Curran et al. 2007, Penn et al. 2007, Gallagher et al. 2008, Vass et al. 2008, Hoffman et al. 2009, Vaglio et al. 2009, Dormont et al. 2013a,b, Liu et al. 2013, Jha et al. 2014a,b, Jha and Hayashi 2015a,b, Jha et al. 2015). A detailed analysis of human body odor (axilla, sweat, saliva, and urine) is reported by Penn et al. (2007) for determination of VOC composition in 197 subjects. A total of 373 peaks of VOCs (both individual and specific) were identified in GC-MS analysis, including four aldehydes in saliva odor. Dormont et al. (2013b) have used four sampling methods (solvent extraction, SPME [contact and headspace], and chromatoprobe dynamic headspace) in feet odor characterization, which confirms the presence of aldehydes in majority (13 out of 44 VOCs). The presence of aldehyde (peak area 5.22%) is demonstrated in GC-MS characterization of axilla, neck, and forehead odor by Liu et al. (2013). The presence of aldehydes (both as individual and common) was confirmed in face odor of four subjects by Jha et al. (2014a). In other related studies (Jha et al. 2014b, Jha and Hayashi 2015a,b, Jha et al. 2015), several branched and unbranched aldehydes were identified in the male and female axilla and foot odor, face odor (Jha et al. 2014b, Jha and Hayashi 2015a,b), and neck odor (Jha et al. 2015). Munk et al. (2000) have established the presence of aldehydes in washed clothes soiled with axilla and sebum odor. The presence of 10 aldehydes was demonstrated by Bernier et al. (1999) in skin odor (maximum relative intensity 100% for Nonanal). In another study, 17 aldehydes were identified in GC-MS analysis of skin emanations of four subjects using thermal desorption (Bernier et al. 2000).

Table 3:

Aldehydes identified in human body odor characterization studies.

ReferenceCharacterization methodOdor sourceDetected aldehydesCAS no.Quantity
Penn et al. 2007GC-MSSaliva odor3,7-Dimethylocta-2,6-dienal5392-40-5Not available
Undecanal112-44-7
Tridecanal10486-19-8
3-(4-tert-butylphenyl)-2-methylpropanal80-54-6
Bernier et al. 1999, 2000Skin odorButanal123-72-8Not available
3-Methylbutanal590-86-3
2-Methylbutanal96-17-3
Pentanal110-62-3
Hexanal66-25-1
Heptanal111-71-7
Octanal124-13-0
Phenylacetaldehyde122-78-1
Nonanal124-19-6
Decanal112-31-2
Undecanal112-44-7
Propanal123-38-6
Nonanal124-19-6
2-Methylpropanal78-84-2
3,7-Dimethyl-2,6-octadienal141-27-5
2-Methyl-2-butenal497-03-0
Decanal112-31-2
2-Methylbutanal96-17-3
Dodecanal112-54-9
3-Methylpentanal15877-57-3
2-Methylhexadecanal55019-46-0
Heptanal111-71-7
Benzaldehyde100-52-7
2,2-Dimethylhexanal996-12-3
3-Hydroxy-4-methylbenzaldehyde57295-30-4
Octanal124-13-0
4-phenylmethoxybenzaldehyde4397-53-9
Haze et al. 2001Skin odorHexanal66-25-1Detection rate 23–33%
Heptanal111-71-7Detection rate 11–15%
Octanal124-13-0Detection rate 85–89%
Nonanal124-19-6Detection rate 85–89%
Decanal112-31-2Detection rate 69–89%
2-Nonenal18829-56-6Detection rate 0–69%
Curran et al. 2005Axillary sweat odorHexanal66-25-1Not available
Heptanal111-71-7
Octanal124-13-0
Nonanal124-19-60.04–0.45 relative to decanal
Decanal112-31-2Not available
2-Furancarboxaldehyde98-01-1
(E)-2-Nonenal18829-56-6
Benzaldehyde100-52-7
Tetradecanal124-25-40.05–0.30 relative to decanal
Undecanal112-44-70.0–0.33 relative to decanal
Vass et al. 2008Bone odor after decompositionDecanal112-31-236 ppt
Nonanal124-19-610 ppt
Dormont et al. 2013bSPME-GC-MSSkin odorHexanal66-25-134–2% of total volatile compounds
Heptenal18829-55-5
Benzaldehyde100-52-7
Octenal2548-87-0
Nonanal124-19-6
Decanal112-31-2
Undecanal112-44-7
Dodecanal112-54-9
Tridecanal10486-19-8
3-(4-tert-Butylphenyl)-2-methylpropanal80-54-6
Liu et al. 2013Sweat odorNonanal124-19-6Peak area 5.22%
Jha et al. 2014aFace odorBenzaldehyde100-52-7Peak area 7.8%
Jha et al. 2014bFemale axilla odorOctanal124-13-0Peak area 8.9%
Nonanal124-19-6Peak area 4.94%
Jha and Hayashi 2015a,b, Jha et al. 2015Male and female axilla and foot odorHexanal66-25-1Peak area 1.7%
Heptanal111-71-7Peak area 0.51%
Octanal124-13-0Peak area 4.94%
Nonanal124-19-6Peak area 5.15%
Decanal112-31-2Peak area 3.87%
Undecanal112-44-7Peak area 0.27%
3-(4-tert-Butylphenyl)-2-methylpropanal80-54-6Peak area 0.56%
2-Methyl-3-phenylpropanal5445-77-2Peak area 0.67%
4-(1-Methylethyl) benzaldehyde122-03-2Peak area 0.27%
2,3 Dihydroxy-propanal497-09-6Peak area 0.28%
Tetradecanal124-25-4Peak area 0.20%
2-Ethylbutanal97-96-1Peak area 0.13%
2-Isopropyl-5-oxohexanal15303-46-5Peak area 0.20%
Zhang et al. 2005, Vaglio et al. 2009Skin odorBenzaldehyde100-52-7Not available
Octanal124-13-01.23%
Nonanal124-19-66.07%
Decanal112-31-21.23%
3-(4-tert-Butylphenyl)-2-methylpropanal80-54-6Not available
α-Hexyl-cinnamic aldehyde101-86-00.270%
Curran et al. 2007Hand odorHexanal66-25-11.67%
Heptanal111-71-713.33%
Benzaldehyde100-52-715.0%
Octanal124-13-016.67%
Nonanal124-19-6100%
Dodecanal112-54-9100%
Decanal112-31-2Not available
(E)-2-Decenal3913-81-3
Undecanal112-44-7
Tetradecanal124-25-4
(E)-2-Octenal2548-87-0
(E)-2-Nonenal18829-56-6
Tridecanal10486-19-8
2-Methyl-2-butenal497-03-0
Hoffman et al. 2009Human tissue decomposition odor2-Hexenal6728-26-3Frequency 21%
Hexanal66-25-1Frequency 50%
Benzaldehyde100-52-7Frequency 42%
2,4-Heptadienal4313-03-5Frequency 14%
2-Heptenal18829-55-5Frequency 7%
Heptanal111-71-7Frequency 36%
2-Octenal2548-87-0Frequency 29%
Octanal124-13-0Frequency 43%
2,4-Nonadienal5910-87-2Frequency 14%
2-Nonenal18829-56-6Frequency 29%
Nonanal124-19-6Frequency 43%
2,4-Nonadienal5910-87-2Frequency 21%
Ligor et al. 2008, 2009Breath odorPropanal123-38-6< 3%
Hexanal66-25-1
Heptanal111-71-7
Prop-2-enal107-02-8Not available
Benzaldehyde100-52-7Ratio 0.09–0.33
n-Pentanal110-62-3Ratio 0–0.064
Acetaldehyde75-07-0Ratio 0–0.071
2-Methyl-2-propenal19125-76-9Ratio 0–0.035
3-Methyl-2-butenal107-86-8Ratio 0–0.032
Munk et al. 2000High-resolution GC-MSSweat and sebum odor in clothsHexanal66-25-1Flavor dilution (FD) factor 1, 2
Heptenal18829-55-5FD 128, 512
Octanal124-13-0FD 64, 512
Octenal2548-87-0FD 32, 128
(Z)-2-Nonenal60784-31-8FD 128, 512
(E,Z)-2,4-Decadienal25152-83-4FD 1, 8
(E,E)-2,4-Decadienal25152-84-5FD 16, 128
(E)-4,5-Epoxy-(E)-2-decenal134454-31-2FD 16, 64
4-Methoxybenzaldehyde123-11-5FD 128, 64
2,6-(E,Z)-Nonadienal557-48-2FD 128
Martínez-Lozano and Mora 2008, Martínez-Lozano and de la Mora 2009API-MSBreath odor3-Methylbutanal590-86-3Not available
3-Methylbut-2-enal107-86-8
3-Hexenal6789-80-6
4-Methylpentanal1119-16-0
Heptanal111-71-7
Skin odor2-Oxopropanal78-98-8
Goetz et al. 1988Headspace GC-MSHair and scalp odorPentanal110-62-3Not available
Hexanal66-25-1
Heptanal111-71-7
Octanal124-13-0
Nonanal124-19-6
Decanal112-31-2
Undecanal112-44-7
Dodecanal112-54-9
Tridecanal10486-19-8
Kubota et al. 1994Thermal desorption GC-MSHair odorn-Pentanal110-62-3Peak area 0.53%
Hexanal66-25-1Peak area 0.92%
Heptanal111-71-7Peak area 0.92%
Decanal112-31-2Peak area 0.92%
Benzaldehyde100-52-7Peak area 0.57%
Gallagher et al. 2008
GC-MS and GC-FPDSkin odorOctanal124-13-0< 2%
Nonanal124-19-6< 8%
Decanal112-31-2< 15%
Benzaldehyde100-52-7Not available
Dodecanal112-54-9
2-(4-tert-Butylphenyl) propanal (p-tert-butyl dihydrocinnamaldehyde)Not available
3-(4-tert-Butylphenyl)-2-methylpropanal80-54-6
α-Hexyl cinnamaldehyde101-86-0
5-(Hydroxymethyl)-2-furaldehyde67-47-0

Besides several fatty acids in breath odor, the presence of six aldehydes is also reported by Martínez-Lozano and Mora (2008). The presence of aldehydes was also confirmed in skin odor and hand odor in another study (Martínez-Lozano and de la Mora 2009) by the similar research group. Haze et al. (2001) have established the presence of six aldehydes (saturated and unsaturated) due to oxidation of fatty acids in skin odor. The presence of C5–C10 and other branched aldehydes was demonstrated in hair and scalp odor by Goetz et al. (1988). In another study, Kubota et al. (1994) have also confirmed the existence of hexanal, heptanal, and decanal besides other three aldehydes. Zhang et al. (2005) have analyzed arm skin odor and found five aldehydes besides several other VOCs. Aldehyde is also identified as a major chemical constituent in the volatile signal during the pregnancy in para-axillary and nipple-areola regions of the body (Vaglio et al. 2009). Nine aldehydes, including C8–C10, were recognized in the examination of forearm and upper back odor from 25 subjects by Gallagher et al. (2008). Sixty-three VOCs were detected in the hand odor of 60 subjects, including 14 aldehydes, by Curran et al. (2007). In another study, C6–C10 and other aldehydes were detected in the axillary sweat of different subjects (Curran et al. 2005). Besides living body odor, several aldehydes along with VOCs from other chemical classes were also identified in human body odor after decomposition (maximum frequency of occurrence 50% for hexanal among the aldehydes) by Hoffman et al. (2009). Vass et al. (2008) have also confirmed the presence of several aldehydes from buried human body decomposition. A comprehensive list of 1870 VOCs, including several aldehydes from breath, saliva, blood, milk, skin, urine, and feces, has been reported (de Lacy Costello et al. 2014). Breath odor analysis using SPME-GC-MS by Ligor et al. (2008, 2009) recognized several aldehydes in both healthy subjects and lung cancer patients. GC-MS is a significant analytical method in human body odor characterization for the identification of chemical compounds. There are attempts to further improve the recognition performance of GC-MS method by selecting novel data sampling, increasing metabolomic exposure, and analysis methods in some recent studies (Birkemeyer et al. 2016, Delgado-Povedano et al. 2016, Jha et al. 2016).

Detection of aldehyde and other chemical compounds in body odor with chemical sensors and pattern recognition

The analytical methods were used proficiently in the characterization of human body odor for the determination of VOC composition, especially the recognition of characteristic chemical peaks related to different chemical compounds. Nevertheless, there are some practical concerns that constrain the real-time off-site applications of analytical methods, like costly, bulky, high analysis time, tough operation, etc. Therefore, the novel research focused on the development of handheld devices for instantaneous detection of VOCs present in human body odor in different applications since last few years. Especially, chemical sensor-array-based systems are established as complementary to analytical instruments in the detection of VOCs in body odor (Natale et al. 2000, Lin et al. 2001, Teo et al. 2002, Dalton et al. 2004, Vass et al. 2004, Natale et al. 2005, D’Amico et al. 2008a, Pennazza et al. 2008, Kateb et al. 2009, Wongchoosuk et al. 2009, Johnson et al. 2010, Simon 2010, Kong et al. 2011, Shirasu and Touhara 2011, Hines et al. 2012, Dymerski et al. 2013, Kybert et al. 2013, Liu et al. 2013, Jha et al. 2014b, Leunis et al. 2014, Lorwongtragool et al. 2014, Voss et al. 2014, Chinen et al. 2015, He et al. 2015, Jha and Hayashi 2015a,b, Seesaard et al. 2015, Zhao et al. 2016). However, there are a few published reports based on the detection of aldehydes in human body odor using chemical sensors. The most common types of chemical sensors used in VOC sensing applications in past studies include metal-oxide semiconductor (MOX) and conducting composite polymer (CCP) chemiresistors, quartz crystal microbalance (QCM) and surface acoustic wave (SAW) gravitational sensors, fiber-optic evanescent wave, and micro-electromechanical system sensors (Albert et al. 2000, Arshak et al. 2004, Janata 2008). Nevertheless, certain limitations of the chemical sensors, including selectivity, sensitivity, reproducibility, response time, etc., need to further improve for efficient detection of specific VOCs in the complex composition of body odor as well as in the presence of the other interfering chemicals. Previous applications of chemical sensors in human body odor sensing applications for the detection of aldehydes and other chemicals are summarized in Table 4. Other chemicals were also identified in body odor samples (Table 4), although the existence of aldehydes in previous characterization studies (Table 3) confirms the ability of chemical sensors (Table 4) in aldehyde sensing. The QCM sensor is low cost, small in size, and reliable in VOC sensing applications. The selection of suitable selective material over the surface of the QCM further improves its sensing performance. Molecular imprinted polymer (MIP) is the novel chemoselective material for the QCM sensor reported in some studies used in aldehyde sensing (Jha and Hayashi 2015a,b). Three MIPs were prepared using polyacrylic acid (PAA) as host polymer, with propenoic acid, hexanoic acid, and octanoic acid as the template molecules independently and used as chemoselective surface coating materials of four QCM sensors (three QCM coated with the MIP and one with pure PAA [non-MIP]) (Jha and Hayashi 2015a). The four-element QCM sensor array is used in the discrimination and identification of three aldehydes: hexanal, heptanal, and nonanal (established in SPME-GC-MS characterization of body odor samples) in individually as well as in binary and tertiary combinations at different concentrations (Jha and Hayashi 2015a). The best response time and recovery time for a specific MIP-QCM were 5 s and 12 s, respectively. The analysis of QCM sensor array response with principal component analysis (PCA) results in good clustering of three aldehydes and their binary and tertiary mixtures in the PC space. Support vector machine (SVM) classifier is used in quantitative class recognition (using the PC scores as input), which results in correct recognition rate of 79% for binary mixtures of three aldehydes and 83% for single, binary, and tertiary mixtures (Jha and Hayashi 2015a). In another study, novel MIPs were prepared by using PAA as host polymer and hexanal, heptanal, and nonanal as the template molecules (Jha and Hayashi 2015b). Four-element MIP-QCM sensors (three QCMs coated with the MIP and one with the non-MIP) are used for the identification of hexanal, heptanal, and nonanal separately and in mixtures at different concentrations. In addition, the water vapor is assumed as the significant interferents. A typical MIP-QCM sensor has a response time and recovery time of 5 s and 10 s, respectively, for one of the aldehyde odors. Better class discrimination of aldehydes was achieved in the PC space; furthermore, the SVM classifier results in 89% class recognition rate for the binary mixtures of aldehydes and 79% in the presence of single, binary, and tertiary mixtures using the PC scores (Jha and Hayashi 2015b). The medical application of VOC sensing in body odor using chemical sensors is available in some review reports (Albert et al. 2000, Arshak et al. 2004, D’Amico et al. 2008b, Janata 2008, Buljubasic and Buchbauer 2015): the past applications and future potential of chemical sensor-based artificial olfactory system (also referred as the electronic nose [E-nose]) are briefly reviewed by D’Amico et al. (2008b); applications of chemical sensors in status monitoring of diabetes by recognition of VOCs in breath, body, and urine odor are summarized by Dalton et al. (2004); Simon (2010) have presented a brief review based on cancer diagnosis by sensing the biomarker VOCs present in human body odor with chemical sensors; a brief report based on olfactory disease diagnosis by detection of biomarker VOCs in blood, urine, breath, and skin odor is reviewed by Shirasu and Touhara (2011); and Chinen et al. (2015) have presented the significance of nanoparticle-based chemical sensing probes in the detection of cancer biomarkers. The summary of some other research reports based on human body odor and biomarker aldehyde sensing using chemical sensors is as follows. An eight-element QCM sensor array (metalloporphyrin as surface coating material) exhibits maximum efficiency in the recognition of skin odor VOCs (Natale et al. 2000). A six-element QCM sensor array was developed for uremia diagnosis by Lin et al. (2001). Sensor array response was measured for the breath odor of normal and subjects suffering from uremia, chronic renal insufficiency, and renal failure. The discriminant analysis of sensor array response results in a class recognition rate of 86.78%. The response of MOX sensor-array-based E-nose system has been measured for odors from different sources, including the sweat, urine, feces, saliva, etc., by Teo et al. (2002). The sensor array response is analyzed with the artificial neural network (ANN) in odor to discriminate the odors successfully. The human body odor decomposition database (consisting of 424 VOCs) prepared by Vass et al. (2004) is advantageous in the development of chemical sensor array portable analytical instrument for human body odor recognition. GC-MS and QCM sensor array are used in the characterization of sweat odor and recognition of present VOCs for discrimination of three groups of subjects, including those who are normal and those suffering from schizophrenia and mental disorder diseases (Natale et al. 2005). Pennazza et al. (2008) have used QCM sensor-array-based E-nose system in the detection of biomarker VOCs related to halitosis and discrimination of breath odor of normal subjects and halitosis-affected patients. Chemical sensor array has been used for discrimination between normal and malignant cells by sensing the VOCs in skin odor (D’Amico et al. 2008a). Wongchoosuk et al. (2009) have used five-element MOX sensor array (based on SnO2 and WO3) for human body odor (specifically the axilla odor) classification by analyzing sensor array response with the PCA. Moreover, a noise correction strategy using a humidity generator is also implemented. A 16-element CCP sensor-array-based E-nose system was developed by the Jet Propulsion Lab (JPL), USA, for the discrimination of two types of tumor cell lines by measuring the sensor response resulting from the odor of tissues (Kateb et al. 2009). A carbon nanotube field-effect transistor coated with DNA is developed for the efficient recognition of aldehydes and acids present in breath odor (Johnson et al. 2010). Nanomaterial-based chemiluminescence sensor has been developed for discrimination of normal, cancerous, and metastatic cells by using linear discriminant analysis for the response analysis by Kong et al. (2011). Cyranose 320 (32 CCP sensor array) has been used in the identification of bacteria causing ENT diseases in blood samples of patients by Hines et al. (2012). The analysis of sensor array response with the ANN methods results in a class recognition efficiency of 92.8%–97.6%. Field effect transistors using DNA-carbon-nanotube-based chemical sensor array have been used in the detection of VOCs present in skin odor. DNA-carbon nanotube functionality assists in complex body odor matrix analysis Kybert et al. (2013).

Table 4

Summary of chemical sensors used in body odor discrimination and detection of aldehydes and other chemical compounds.

ReferenceChemical sensorOdor sourceDetected aldehydes and other chemicalsCAS no.Concentration
Jha and Hayashi 2015a,bQCM sensorAxilla odor, neck odor, and face odorHexanal66-25-1Few parts per million (ppm)
Heptanal18829-55-5
Nonanal124-19-6
Natale et al. 2000Skin odor5a-Androst-16-en-3-one18339-16-7Up to 50 ng/ml
Lin et al. 2001Breath odorDimethylamine124-40-3mg/l
Trimethylamine75-50-3
Pennazza et al. 2008Breath odorValeric acid109-52-4Up to 2500 ppb
Hydrogen sulphide7783-06-4
Butyric acid107-92-6
D’Amico et al. 2008aSkin odorNot available
Vass et al. 2004GC-MS and QCM sensorAxilla odortrans-3-methyl-2-hexenoic acid27960-21-0Not available
Teo et al. 2002Metal oxide gas sensorFeces, urine, saliva, and sweat odorNot availableml
D’Amico et al. 2008aAxilla odorIsovaleric acid503-74-2mm
Breath odorNot available
Voss et al. 2014Skin odorNot available
Dymerski et al. 2013Breath odorChronic obstructive pulmonary disease (COPD) biomarkersppb
Kateb et al. 2009CCP sensorGlioblastoma and Melanoma cell odorNot available
Hines et al. 2012Blood odorNot availableColony forming units (cfu)/ml
Seesaard et al. 2015Axilla, breath, and urine odorNot available50–1000 ppm
Lorwongtragool et al. 2014Carbon nanotube polymer sensorAxilla odorAmmonia7664-41-7500 ppm
Acetic acid64-19-7
Acetone67-64-1
Ethanol64-17-5
Johnson et al. 2010Carbon nanotube field-effect transistor sensorBreath odorOctanal2548-87-0ppm
Nonanal124-19-6
Decanal112-31-2
Kybert et al. 2013Skin odorNonanal124-19-60.01 mg/ml
Kong et al. 2011Chemiluminescence sensorProteins and cell odorNot availableμg/ml
He et al. 2015SAW sensor and GC-MSBreath odorC6–C14 alkanesppt
Zhao et al. 2016Optical gas sensorNot availableFormaldehyde and other chemical compounds50-00-0Not available

A low-cost, wearable E-nose based on CNTs/polymer sensor array is used in the identification of individual axilla odor (Lorwongtragool et al. 2014). The PCA analysis of sensor array response results in better discrimination of body odors in the PC space. A 12-element MOX sensor array (four different types in the triplicate) has been used by Leunis et al. (2014) in breath odor analysis of 23 patients suffering from head and neck cancer. The sensor array results in better discrimination of healthy and cancer-affected subjects with a sensitivity of 90% and specificity of 80%. MOX sensor-array-based E-nose is used in the discrimination of cannabis- and tobacco-consuming subjects by analyzing their skin odor (Voss et al. 2014). PCA and SVM classifier are used in the sensor array response analysis; the later results in 92.5% classification accuracy. SAW sensor and GC-MS is used in the recognition of VOCs and semi-VOCs (from parts per billion [ppb] to parts per trillion [ppt] orders) in the breath odor samples of normal subjects and cancer-affected patients (He et al. 2015). A wearable E-nose based on CCP sensors (using different polymers and carbon nanotubes) is developed and used in axilla, breath, and urine odor discrimination and recognition using PCA analysis (Seesaard et al. 2015). Metal oxide sensor array is used in the identification of 15 chronic obstructive pulmonary disease (COPD) biomarkers up to ppb levels (Dymerski et al. 2013). Optical gas sensors were used in the detection of nine gases including formaldehyde by analyzing their absorption spectra by a feature-based analysis technique (Zhao et al. 2016).

Significance of aldehyde sensing

The most significant application of aldehyde sensing in body odor is in medical diagnosis, as reported in past studies (Kateb et al. 2009, Johnson et al. 2010, Kong et al. 2011, Hines et al. 2012, Kybert et al. 2013, Lorwongtragool et al. 2014, Leunis et al. 2014, He et al. 2015, Seesaard et al. 2015), since the disease-affected body organ emits representative VOC signature, which can be used as a biomarker for diagnostic applications. Especially, characterization of axilla, skin, breath, blood, urine, etc., odor for the detection of biomarker VOCs is a significant noninvasive future medical diagnostic tool. However, there is a need to improve the selectivity and sensitivity of present chemical sensor-array-based E-nose in the recognition of disease-specific VOC in the presence of others. Quantitative detection of disease biomarker aldehydes is useful in the early presentation of diseases and progress monitoring of the patient during the treatment. E-nose could be employed as a noninvasive, real-time, precise, and fast analysis method compared to conventional disease diagnostic methods. Besides, better organization of analytical methods and E-nose could make it more effective in fast and real-time body condition monitoring. Body odor is influenced by the individual metabolic process, heredity, and living style, as mentioned earlier, which results in the distinguishable composition of VOCs. Therefore, the composition of aldehydes in body odor can be used as a biometric identification method. Body odor and mood have a convinced correlation, which can be used in the valuation of human activities like lie detection in future. The detection of VOCs in the body odor of dead people is significant in forensic applications. Also, discrimination of alive vs. dead people using VOC composition in body odor is vital for rescuing humans in natural and manmade calamities. Several aldehydes were identified in trapped body odor (Vautz et al. 2013), which could be used in forensic and rescue applications in future.

Some specific applications of aldehyde odor sensing in medical applications are as follows. 2-nominal resulting from the fatty acids is confirmed in the body odor of aged people in several studies (Haze et al. 2001, Ishino et al. 2010); consequently, it can be also used as a health biomarker. Besides nonanal, decanal and other aldehydes were also identified in skin odor, which can be used in analysis control and monitoring of skin-related diseases. The intensity of acetaldehyde and propanal in skin odor indicates the effect of UV radiation (Steeghs et al. 2006). It could be helpful in the diagnosis of skin-related diseases due to UV radiation. Controlling the concentration of nonanal and decanal was identified in foot odor (Dormont et al. 2013b) in the presence of other VOCs, which establish a possible link in origin and treatment of foot malodor. The presence of aldehydes as main odor from axilla sweat and sebum in washed and unwashed clothes (Munk et al. 2000) provides valuable information in making odor-controlling cosmetics and clothes. Detection of aldehyde plays a significant role in the diagnosis of COPD. For instance, a high concentration of malondialdehyde (propanedial) is reported in the body odor of subjects suffering from COPD (Corradi et al. 2003). Aldehydes are also established as biomarkers for other diseases like formaldehyde for breast cancer (Gordon et al. 1985), emission of acetaldehyde by malignant tissue (lung cancer cell lines) (Smith et al. 2003, Jelski and Szmitkowski 2008), etc. Several aldehydes were identified in body odor samples and originated mainly due to the lipid peroxidation of proteins (Uchida 2015); besides, butane-2, 3-dione is one of the initiators of axilla and foot odor (Hara et al. 2015).

Conclusion

The present review introduces a short description of body odor and its constituents. The analytical methods used for the characterization of human body odor with the objective to determine the composition of VOCs is briefly reviewed. The application of chemical sensor-array-based E-nose system in the detection of aldehydes in body odor and the body odor itself in several applications using some pattern recognition methods for sensors response analysis is described in detail. Lastly, the significance of chemical composition determination of body odor in different applications, especially in medical application, is highlighted. The future research in the present research domain should focus on the development of efficient body odor characterization strategy in order to search the specific biomarker VOCs in different applications. Furthermore, the development of more robust chemical sensors and pattern recognition methods for the recognition of identified VOCs in the complex matrix of body odor is also essential.

Acknowledgments

The author acknowledges Dr. S.S. Murthy (CFSL, Bhopal, India) and reviewers for their valuable comments and suggestions during the preparation of this review.

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Received: 2016-4-27
Accepted: 2016-11-1
Published Online: 2016-12-21

©2017 Walter de Gruyter GmbH, Berlin/Boston

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