solid-state gas sensors for breath analysis: a review

Analytica Chimica Acta xxx (2014) xxx–xxx

G ModelACA 233151 No. of Pages 17

Review

Solid-state gas sensors for breath analysis: A review

Corrado Di Natale a,*, Roberto Paolesse b, Eugenio Martinelli a, Rosamaria Capuano a

aDepartment of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, Roma 00133, ItalybDepartment of Chemical Science and Technology, University of Rome Tor Vergata, via della Ricerca Scientifica, Roma 00133, Italy

H I G H L I G H T S G R A P H I C A L A B S T R A C T

� A review of the applications of themajor sensor technologies in thefield of breath analysis.

� A review of the diseases that could bediagnosed with solid-state sensors.

� A discussion about the samplingmethods and the critical points inthe analysis.

A R T I C L E I N F O

Article history:Received 5 November 2013Received in revised form 10 March 2014Accepted 12 March 2014Available online xxx

Keywords:Gas sensorsSensor arraysBreath analysisMedical diagnosis

A B S T R A C T

The analysis of volatile compounds is an efficient method to appraise information about the chemicalcomposition of liquids and solids. This principle is applied to several practical applications, such as foodanalysis where many important features (e.g. freshness) can be directly inferred from the analysis ofvolatile compounds.The same approach can also be applied to a human body where the volatile compounds, collected fromthe skin, the breath or in the headspace of fluids, might contain information that could be used todiagnose several kinds of diseases. In particular, breath is widely studied and many diseases can bepotentially detected from breath analysis.The most fascinating propertyof breath analysis is thenon-invasiveness of the sample collection. Solid-statesensors are considered the natural complement to breath analysis, matching the non-invasiveness withtypical sensorfeaturessuch as low-cost,easiness of use, portability, andtheintegrationwith theinformationnetworks. Sensors based breath analysis is then expected to dramatically extend the diagnostic capabilitiesenabling the screening of large populations for the early diagnosis of pathologies.In the last years there has been an increased attention to the development of sensors specifically aimed tothis purpose. These investigations involve both specific sensors designed to detect individual compoundsand non-specific sensors, operated in array configurations, aimed at clustering subjects according to theirhealth conditions. In this paper, the recent significant applications of these sensors to breath analysis arereviewed and discussed.

ã 2014 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 002. Breath collection methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003. Selective sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

3.1. Nitric oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

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* Corresponding author.E-mail address: [email protected] (C. Di Natale).

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Please cite this article in press as: C. Di Natale, et al., Solid-state gas sensors for breath analysis: A review, Anal. Chim. Acta (2014), http://dx.doi.org/10.1016/j.aca.2014.03.014

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3.2. Acetone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.3. Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.4. Methylmercaptane and hydrogen sulfide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.5. Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.6. Sleep apnea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.7. Analytes in exhaled breath condensate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

4. Gas sensor arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.1. Lung cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.2. Other respiratory diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.3. Non respiratory diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.4. Conclusions on gas sensor arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

Corrado Di Natale is a full professor at theDepartment of Electronic Engineering of the Uni-versity of Rome Tor Vergata where he teachescourses on electronicdevices and sensors. Hisresearch activity is concerned with the developmentof chemical and bio-sensors, artificial sensorialsystems (olfaction andtaste), and the optical andelectronic properties of organic and molecularmaterials. He authored more than 450 papers oninternational journals and conferenceproceedings.He chaired the 9th International Symposium onolfaction and elec-tronic nose (Rome, 2002) andEurosensors XVIII Conference (Rome, 2004) andwasmember of the organizing committee of nation-al and international conferences insensors.

Roberto Paolesse is a full professor of generalchemistry at the Department of Chemical Scienceand Technology of the University of Rome TorVergata where he givescourses on general chemistryand supramolecular chemistry. His research inter-estsinclude the synthesis and reactivity of transitionmetal complexes with porphyrinsand related mac-rocycles and the development and application ofchemical sensors.He authored more than 400papers on international journals and conferences.Hewas chairman of the 4th International Conferenceon Porphyrins and Phtalocyanines(Rome, 2006) andhe is a member of the steering committee ofInternational Meetingof Chemical Sensors confer-ences series.

Eugenio Martinelli is an assistant professor inelectronics at the Department ofElectronic Engi-neering of the University of Rome Tor Vergata. Hisresearch activityis concerned with the developmentof chemical and biological sensors, artificialsenso-rial systems (olfaction and taste) and their applica-tions, sensor interfaces anddata processing. Heauthored 120 papers on international journals andconferences.

Rosamaria Capuano has a post-doc position at theDepartment of Electronic Engineering of the Uni-versity of Rome Tor Vergata. Her research interestsare in the field of chemical sensors and theirapplication for medical diagnosis. She authored 15papers on international journals and conferences.

1. Introduction

Analytical chemistry plays a conspicuous role in medicaldiagnostics. Advances in this discipline introduced a number ofmethodologies and instrumentations for the detection of targetmolecules in fluid, such as urine and blood, which can be sampledwith a relatively minimum invasiveness for the subject.

Progresses in the analysis of gaseous samples stimulated theinvestigation of the volatile compounds that are found in theatmosphere surrounding the human body. The breath is surely themost rich and accessible body domain for the collection ofendogenous volatile organic compounds (VOC). Therefore, breathanalysis is rapidly emerging as a fascinating application field forthe modern analytical chemistry. Furthermore, VOC analysis isattractive in medicine because of its absolutely non-invasivecharacter, and it can be applied to any stage of the life.

The correlation between VOC and health was well known in theold clinical practices. Indeed, modern medicine takes advantage ofthe instruments offered by the technological progress, but in thepast physicians interacted with the body of the patient using all thesenses, olfaction included.

Nowadays, the introduction of diagnostics instruments makesless relevant sensorial inspection of the body, and this almostdisappeared practice is rather confined to the realm of anecdotes [1].

Please cite this article in press as: C. Di Natale, et al., Solid-state gas sensororg/10.1016/j.aca.2014.03.014

Since the seventies, the improvement of instrumental analysisof VOC led to reconsider their role in medicine. The seminal paperof Linus Pauling [2] defined the frame for the definition of VOCprofile out of a human body. This could be considered the basis forthe finding of anomalies that can be connected to specificpathologies.

A number of studies appeared in the last decades correlates thepresence of VOC in breath to some specific disease. The quest ofbiomarkers, univocally connected to pathologies, has beenthwarted by the fact that many diseases are related to patternsof VOC instead than individual specific compounds. Theseresearches are reassumed by a number of review papers and theywill not be further reviewed here [3–5].

An additional burst to the interest in VOCs analysis for medicalpurposes has been provided by the development and the diffusionof solid-state chemical sensors. The impact of these devices andtheir possible applications is discussed in this review paperconsidering the case of breath analysis.

Breath is the natural interface for the extraction of VOC from theliving bodies. Inhaled air, besides O2 and N2, contains a number ofcompounds present in the environment. On the contrary, as aresult of the respiration, exhaled air is partially depleted of O2,enriched of CO2, and contains the VOC resulting from theinteraction with the environment and the metabolic processes.

s for breath analysis: A review, Anal. Chim. Acta (2014), http://dx.doi.



C. Di Natale et al. / Analytica Chimica Acta xxx (2014) xxx–xxx 3


In order to be excreted through the breath, the metabolic productsare broken down to volatile molecules that can be carried throughthe blood and lymphatic vessels to the lung tissues, where they canbe exchanged with air [6]. Besides, an important portion of VOC inbreath is produced by airway/pulmonary metabolism thusreflecting lung physiology and pathophysiology.

It is then reasonable to suppose that alterations of metabolicprocesses can alter the composition of the exhaled breath, and acareful scrutiny of the breath could trace back the presence ofpathologies. These investigations are of particular interest in thewide field of cancer research. The accelerated metabolism of tumourcells may likely produce VOC patterns that can differ in terms ofquantity and quality from those released by healthy subjects.Consequently, a pattern of detectable VOC arising from living tissuescould provide a signature of fundamental biological processesincluding cell proliferation, growth arrest and cell death [6].

The application of these clear and straightforward conceptsmay be practically hampered by the fact that myriads of molecularspecies are contained in breath and only few of them areinteresting for diagnosis. Furthermore, such molecules naturallyoccur at a very low concentration and the alteration is still in verylow concentration range (typically lesser than ppb).

Exhaled breath is composed of nitrogen, oxygen, carbondioxide, carbon monoxide, nitric oxide, water vapour, and amixture of VOC such as hydrocarbons, alcohols, terpenes,aldheydes, and other nonvolatile molecules only detectable inbreath condensate (EBC) such as isoprostanes and cytokines [7–9].

In general it is possible to distinguish in the breath two differentportions of the respiratory system: the physiological dead space(from mouth to terminal bronchioles) and the alveolar portion(lungs). The composition of the dead space air consists of theinspired air saturated of water vapour; additionally it contains VOCproduced both in the nasal cavity and in the upper airways. The lastportion of deeply exhaled breath (alveolar air) is essentially theheadspace of the pulmonary tissue and it can also be considered asthe headspace of the blood circulating in the body. Alveolar air isinfluenced by VOC exchange across capillary membrane betweenblood and alveoli: the volatile compound diffuses from thecompartment with the higher vapour pressure to the lower, untilthe equilibrium between the two compartments is reached. Thisphenomenon is ruled by the Henry partition coefficient, whichdetermines for each VOC the relative concentration in the blood–breath interface [6,10,11].

Gaseous and capillary VOC equilibrate rapidly in the pulmonaryalveoli. The process varies with the phase of respiration. During theinspiratory phase, environmental VOCs are in equilibrium withpulmonary venous blood, while during the expiratory phase,pulmonary arterial blood is in equilibrium with VOC in alveolarbreath. This effect has been evidenced by the correlation of sensorarray signals with the changes of pulmonary arterial pressure [12].

Volatile compounds originate from systemic and metabolicprocesses (endogenous VOC) and from exogenous sources. Each ofthese groups of compounds contains information useful fordifferent scopes.

2. Breath collection methods

Exhaled breath contains thousands of endogenous VOC in lowconcentration ranging from mmol L�1 to fmol L�1 [13]. A recentreview identifed 1764 human related VOC, 874 of which are foundin exhaled breath [14].

The variability of analytical results obtained in different studiesdepends on the absence of a standard procedure for breathsampling and the related analytical techniques. This is probablyalso due also to impurities contamination from inspired air or todilution with dead-space air. Therefore, sampling procedures are of


outmost importance in breath analysis [15]. Two main aspectshave to be taken into account for an effective breath samplingprocedure: the portion of exhaled breath to be analyzed (totalbreath, including dead space air, or alveolar portion, and thesampling technique.

The collection of the total breath is the most direct sampling. Itdoes not require any additional apparatus and the subjects are onlyrequested to deeply breathe into the collecting system. Drawbacksof total breath sampling are the dilution and contamination effectsdue to the contribution of the dead space.

On the other hand, in the alveolar breath the endogenous VOCare more concentrated. The correct separation of alveolar breathfrom the total breath requires particular care. The simplest methodis the “time-controlled sampling” where the separation isautomatically done at some pre-specified time after the start ofexpiration. This technique obvioulsy suffers a big variability due towide differences of individual dead-space volumes (dependingamong the other variables on weight and height) and differentbreathing maneuvers. Controlled alveolar sampling, by means ofthe measure of expired CO2 concentration, is instead an effectivemethod to sample blood-borne volatile biomarkers [16].

Another aspect to consider is that breath sampling can beperformed for a single breath or for multiple breath cycles.Although sampling a single breath is simple, its composition mayshow a large variability among individuals due to very dissimilarmodes and depth of breathing. Multiple breath technique seems toovercome these problems providing a larger reproducibility [17].

The presence of contaminants in inspired air is anotherimportant problem for breath analysis. In addition, it is importantalso to consider the storage condition as possible confoundingfactor. To this regard, polyvinyl fluoride is a diffused material forbreath collection. However bags made of polyvinyl fluoride mayrelease N,N-dimethylacetamide and phenol [18]; septa oftenrelease carbon disulfide and sometimes other compounds like3-methyl-pentane; breath samples could be also contaminated byplasticizer VOC of tubings and valves (e.g. 2,2,4-trimethyl-1,3-pentanediol diisobutyrate [CAS: 6846-50-0] or pentanoic acid,2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester) [17,19]. Even-tually, the concentrations of VOC in inhaled air (e.g. thatoriginating from room air or ventilation systems) should bedetermined as the presence of substances at high concentrationsmay influence the concentrations of endogenous compounds. Inparticular, if concentrations increase, the correlations betweenblood and breath levels will be decreased [19]. There are two viablesolutions to such problems:

1. Pure air breathing. By this method, subjects breathe pure air forafixed time before measurements [20]. This approach is moretheoretical than actual, since it is quite impossible to eliminateVOC from air previously inspired. Moreover several exogenouscompounds absorbed from the organism require several hoursor days for a complete washout from the body [21].

2. Subtraction of the air background from the breath signal. Inthis method environmental air and exhaled breath arecollected. Both of them are analyzed and the VOC concentra-tion in alveolar air is given from the ‘alveolar gradient’.

Preconcentration of the sample is a frequently used techniqueto increase the concentration of relevant compounds. For thisscope the exhaled breath is absorbed into a solid phase materialand then, in order to be analyzed, it is desorbed at hightemperature.

Sorbent materials have to be chosen considering the differentparameters that can influence the analysis. Ideal sorbent materialshould be both ‘strong’ enough to retain sample analytes but alsoable to efficiently release them during the thermal desorption.






Furthermore, the sorbent is required to be thermally stable andchemically inert. Finally, it should also be hydrophobic, in order tobe independent from the changes of humidity [22,23]. Sorbentmaterials generally used in breath analysis provide good thermalstability. Porous polymers like Tenax11 are stable up to 350 �Cwhile carbon-based sorbents bear temperatures above 400 �C.Concerning sorbent properties, even if it is largely utilized,Tenax11 is not optimal for polar solvents and very volatilecompounds while sorbents. The latter group of compounds isbetter captured by carbon molecular sieves based materials.

Sorbent tubes are also useful to store and deliver collectedbreath samples. For instance, Tenax filled sorbent tubes can retainasthma related VOC up to two weeks [24].

The above mentioned sampling techniques are based on theaccumulation of the volatile compounds collected in multiplebreaths. Interestingly, some analytical techniques such as protontransfer reaction mass-spectrometry (PTR-MS) [25] and selectedflow tube ion mass-spectrometry (SIFT-MS) [26] have beendemonstrated to be sufficently sensitive to measure the exhaledvolatiles in a single breath allowing for a real-time monitoring ofthe breath composition.

An alternative to direct breath sampling is the exhaled breathcondensate (EBC) [27]. EBC is produced by cooling the exhaledbreath by means of a Peltier cell. The resulting fluid mainlycontains water, VOC solubles in water, and a number of nonvolatilecompounds such as low-molecular weight metabolites, eicosa-noids, hydrogen peroxide, leukotrienes, isoprostanes, cytokinesand prostaglandines [28,29].

The sampling of EBC is still non-invasive: subjects inspirethrough a mouthpiece connected to a non re-breathing valve.Breath flows into the collection device that is immersed in acooling cuff, where the breath condensates in an appropriate vial[30]. Reference analytical techniques including NMR spectroscopyand MS have been applied to EBC analysis [31–34].

Finally, it is important to consider the influence of life-stylesand food intake in the breath composition. In practical studies, thesubjects are conditioned to follow a common dietary and hygenicprocedure. However these protocols are rather empiric because aquantitative description of the decay in breath of foods and otheradditives is largely unknown. Some researches point out thatparticular lifestyles can permanently influence the breath compo-sition. Besides the obvious cases, e.g. tobacco consumption, breathcomposition alterations have been found in subjects followingparticular diets such as a gluten-free diet [35]. Spices rich diets(like turmeric dietary in Indian cuisine), besides providing anobvious smells has also been found to increase the level ofhydrogen in breath [36].

3. Selective sensors

Only a restricted number of VOC or gases is known to becorrelated with the presence of specific diseases. These com-pounds can be actually considered as disease markers, and theirdetection requires selective sensors. Possible confounding com-pounds are those present in breath. Particularly important are themajor breath components whose oscillation in concentration caneasily hide the target molecules. Among them, an insidiouscompound is water. With respect to other applications, watervapour in breath analysis is obviously quite large (RH � 80%), but itis also rather stable. Then besides using sensors totally insensitiveto water vapour it is possible to exploit the fact that the situation ofzero sensitivity is also achieved in saturation conditions. Thiscondition can be reached by saturating the breath sample withwater vapour. However, it is straightforward that a sensor surfacesaturated with water molecules hardly can preserve the bindingconditions for the target molecules. For this reason, sensors for


breath analysis are often matched with some method for watervapour rejection. Carbon dioxide is another major component ofbreath that can interfere with the measurement of disease relatedcompound. CO2 can be adequately monitored with pH indicatorsembedded in suitable membranes [37,38].

In the following section some recent case studies relating to themost investigated VOC and gases are illustrated.

A more extended list of sensors focused on the detection ofspecific gases in breath is provided in Table 1. The table is organizedaccording to the target volatile compound. For each item the tableindicates the sensor technology and the sensitive material, if thesensor has been tested on human samples, the limit of detectionand if the sensor necessitates of particular sample treatment toimprove the detection.

3.1. Nitric oxide

Fractional exhaled nitric oxide (FENO) is associated to thepresence of inflammatory conditions of the airways, like asthma,and bronchiectasis among the others [39]. A concentration below25 ppb is considered normal, and a concentration above 50 ppbreveals an airway inflammation [40]. FENO measurement is a non-invasive, standardized, and validated technique for assessingairway inflammation in patients with asthma [41] with or withoutnasal polyposis [42]. FENO is also allegedely correlated with otherdiseases of the respiratory tract and in particular with chronicobstructive pulmonary disease (COPD) [43].

Currently used methods to evaluate NO in breath are mainlybased on the chemiluminescent reaction with NO of either ozoneor luminol [44]. These instruments can reach limits of detectionof the order of 2 ppb with a resolution of 1 ppb. A number ofcommercial, in many cases even portable, FENO detectors arecurrently available and their extensive use provided moreevidences to the supposed relationship between the concentra-tion of FENO and asthma. However, the level of nitric oxide doesnot univocally diagnose asthma. As shown in Fig. 1 FENO is ratherconstant among healthy subject but a large dispersion is observedamong asthma patients. The averages between the two groups aredifferent but the two distributions are somewhat overlapped.Eventually, FENO is currently the easiest to measure and theonly clinically approved surrogate marker of airway inflamma-tion. It is useful to monitor the evolution of asthma rather than todiagnose the disease and for assessing the effect of pharmaco-logical treatment and treatment withdrawal in patients withasthma [45].

Ozone-based chemiluminescence detectors are approved bythe US Food and Drug Administration to monitor the inflammationprocesses in asthma patients [46]. However, besides the complex-ity of the measurement principles these detectors are affected bysome drawbacks such as the necessity of frequent calibrations, thetechnical maintenance, the generation and destruction of ozone,and the use of high voltage. For these reasons, a generation ofalternative sensors is devised to make more affordable and easythe monitoring of airways inflammatory diseases.

The detection of FENO rises a twofold sets of problems, the firstis the large sensitivity and the low noise that are necessary to reachthe ppb detection limit; the second is obviously the selectivity.

Chemoresistors made of metal-oxide semiconductors are goodcandidates to reach the necessary sensitivity but their selectivity israther limited. However, materials formed by mixtures of n- and p-type oxides (such as WO3 and Cr2O3) shown interesting perfor-mance being able to detect NO down to 18 ppb and maintaining thedetection even in the presence of 20 ppm of carbon monoxide [47].The removal of CO2 from the sample allowed a chemoresistor madeof chemically functionalized carbon nanotube to achieve adetection limit of 5 ppb [48].




Fig. 1. Distribution of FENO in healthy and asthmatic subjects. Median values areshown with a bar. The distributions are statistically different but clearly FENO leveldoes not univocally discriminate between asthma and controls. Reproduced withpermission from [160].

Table 1Specific sensors.

Primary target Sensor technology Sensing materials Humantests

Limit of detection Sample treatment Reference

Volatile sulphurcompounds

Colorimetry Iodine Yes 0.05 mg L1 of H2S No [89]


Chemiresistor Gold nanoparticles decoratedpolyaniline

No 1 mM of H2S and CH3SH No [88]


Fiber optic Monoamine oxidase A and opticaloxygen sensor

Yes 200 ppb No [90]

Acetone Chemiresistor Si:WO3 Yes 20 ppb No [72]Acetone Chemiresistor Chitosan No 0.1 ppm No [73]Acetone Chemiresistor In2O3 and Pt–In2O3 No <1 ppm No [69]Acetone Chemiresistor Hemitubes of Pt–WO3 No 120 ppb No [67]Acetone Direct optical

spectroscopyCavity ringdown spectroscopy Yes 130 ppb Yes [75]

Hydrogen peroxide ChemFET Os-polyvinylpyridine containingperoxidase

No 0.8 mM Breath condensate [177]

Hydrogen peroxide Amperometric Pt electrode with agarose membrane Yes 50 ppb No [118]Hydrogen peroxide Potentiometric Prussian blue solid state salt 0.1 mM in aerosol No [119]Nitric oxide Potentiometric YSZ Yes 5 ppb Water removal in dry/acetone

bath[50]

Nitric oxide Chemiresistor PEDOT:PSS coated nanofibrous TiO2 No 6 ppb No [51]Nitric oxide Direct optical

spectroscopyTunable diode lase absorptionspectroscopy

Yes 11 ppb No [53]

Nitric oxide Direct opticalspectroscopy

Quantum cascade laser and cabityspectroscopy

Yes 4 ppb No [54]

Nitric oxide Chemiresistor Chemically functionalized carbonnanotubes

No 5 ppb Removal of CO2 with ascaritescrubble

[48]

Sleep apnea Chemiresistor Multi-wall carbon nanotubs Yes Less than 6 breaths perminute

No [99]

Ammonia Chemiresistor H2SO4 solution Yes <18 ppb No [81]Ammonia Chemiresistor MoO3 No <1 ppm No [83]Ammonia Optical TFE based membrane and pH dye Yes 50 ppb No [76]Carbon dioxide Optical Tetraoctylammonium hydroxide and

pH dyesNo 0.25% No [38]

Carbon dioxide Optical Tetraoctylammonium hydroxide andpH dyes

No <5% No [37]

Influenza virus Chemoresistor Antibody coated silicon nanowire Yes <1 pg mL1 Exhaled breath condensatediluted 100 folds

[107]

Influenza virus Dynamic lightscattering

Antibody coated gold nanoparticles No 8.6 TCID50mL1 No [108]

Drink test Potentiometry Alcohol oxydase and aldehydedehydrogenase

Yes Ethanol <1 ppmacetaldehyde <0.1 ppm

No [96]




Electrochemical sensors are also available to detect NO even ifin completely different conditions such as automotive exhaust gas.Attempts to convert such devices into sensors suitable for breathanalysis have been reported and a commercial FENO analyzer (suchas NIOX MINO), based on an electrochemical sensor, has beentested and validated [49].

Prakash Mondal et al. reported the development of a sensorformed by the series connection of up to twenty potentiometricsensors made of yttria stabilized zirconia (YSZ) as solid electrolyteconnected with platinum wires [50]. The device allowed thedetection of NO down to 5 ppb. The selectivity was ensured by acatalytic filter necessary to remove other oxidizable species. Theinterference of water was particularly severe, and a water removalin dry ice/acetone bath was necessary to reach the abovementioned limit of detection. Interestingly, the authors suggestto keep water concentration constant instead to try to remove it, byusing a water saturated background air. This could be a validgeneral method for breath analysis.

Breath analysis is sometimes limited by the amount of collectedsample. Pantalei et al. [51] have recently reported an increase ofsensitivity obtained with an engineered sensor cell allowing thesample recirculation. The sensor tested in this configuration was a






chemiresistor based on TiO2 nanofibers coated with a layer of poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) and it reached a detection limit of 6 ppb with a resolutionlimited to 3 ppb in the range 0–50 ppb.

Another viable method for NO detection is the directspectroscopic detection. The selectivity in this case depends onthe resolution in wavelength of the spectrometer. Availableequipments allow to achieve the necessary performance forbreath analysis [52].

The introduction of tunable lasers in the mid-infrared rangemake possible a more viable equipment turning the spectroscopyto a detection confined to the wavelength range of the laseremission [53]. A device with a laser emission centered at the1828 cm�1 NO absorption peak with a bandwidth of about 10 cm�1

has been tested on human breath. Results show a substantialagreement with the chemoluminescence detector. Direct spectro-scopic detection avoids some of the above mentioned drawbacksbut increasing the hardware complexity, and the cost, of theanalyzer [54].

3.2. Acetone

Acetone is one of the most abundant VOC in breath, with aconcentration in healthy subjects in the range 300–900 ppb [26].Acetone is mostly produced in liver by many pathways involvinglipid peroxidation and Krebs cycle. Anomalous concentrationsexceeding 1800 ppb are found in the breath of diabetes mellituspatients [55]. For this reason, its detection is a promising candidateto monitor the behaviour of diabete affected individual. However,acetone can also be found at high concentration even in nondiabetic subjects.

Metal-oxide semiconductors based chemoresistors have beeninvestigated for the detection of acetone showing the necessarysensitivity and selectivity for diabetes mellitus diagnosis.

Righettoni et al. developed a promising device based on silicondioxide doped WO3 nanoparticles prepared by the flame aerosoltechnique. The sensitivity and selectivity to acetone are found in ametastable crystalline phase of the tungsten oxide known ase-WO3 [56]. This sensor can detect acetone down to 20 ppb even invery humid air, and it was tested in human breath collected underrelax and physical exercise. Fig. 2 shows the sensor estimatecompared with the concentration measurement provided by the

Fig. 2. Comparison between acetone level in breath measured with a WO3 basedsensors and PTR-MS. Data were collected during a physical excersise session.Isoprene level measured by PTR-MS is also shown. Reproduced with permissionfrom [72].


proton transfer reaction-mass spectrometry (PTR-MS), which is astandard analytical tool for acetone assessing in breath [57]. Theresult shown in Fig. 2 is also a good example of a real-timemeasurement of a breath component. In this case the sensor offersa good alternative to more complex analytical tools such as PTR-MS. The same sensor has also been used to study the correlationbetween blood glucose and breath acetone [58].

An inset in Fig. 2 shows the profile of isoprene concentration.Isoprene is another major compound in breath with an averageconcentration of about 100 ppb [26]. Isoprene concentrationincreases during physical activities [59] and its decrease has beenfound to be related to severe respiratory [60], heart failureconditions [61], lung cancer [62] and breast cancer [63]. Thedecrease in lung cancer is associated with the concentration ofimmune marker neopterin in blood [64]. Finally, it is worth tomention that isoprene is an interesting case study compound forreal time analysis [65,66]

An interesting development of WO3 based acetone sensors hasbeen recently shown by Choi et al. that studied the breath analysisapplication of thin walled WO3 hemitubes functionalized byplatinum dots [67]. A detail of the nanostructure morphology isshown in Fig. 3. Interestingly undoped WO3 structures weresensitive to H2S and almost not sensitive to acetone, while theaddition of platinum and iridium oxide clusters on the surfaceprompted the sensitivity to acetone [68]. In this case the responseto acetone exceed the response to H2S, and make the devicesuitable for the detection of acetone down to 120 ppb. Notewor-thy H2S is a typical halitosis related compound, then theapplication of combined doped and undoped sensors coulddiscriminate between generic oral malodour and halitosis due todiabetes.

Platinum was also found to increase the sensitivity to acetone inIn2O3 [69] and SnO2 thin-wall assembled nanofibers [70] with alimit of detection comparable to that exhibited by Pt–WO3

hemitubes. Pt–SnO2 fibers based sensor was also found sensitiveto toluene. The sensitivity to acetone was larger than to toluene,but the concentration of this aromatic compounds is altered inmany diseases such as lung cancer [71]. Anyway, the concentrationof toluene in breath is order of magnitude lower than that ofacetone.

The previously discussed examples show that WO3 has aparticular sensitivity to acetone, which can be enforced by propersurface functionalization and nanostructured shape. The mostinteresting paper here is the work of Righettoni et al. [72] whichshown that a particular metastable crystalline form of tungstenoxide shows an increased selectivity towards acetone.

The electric resistance of a chitosan layer was also found to besensitive to acetone allowing for a resolution of the order of100 ppb [73].

Optical sensors are an interesting alternative to chemoresistors.Recently, Worrall et al. showed a sensor based on the reaction ofacetone with resorcinol embedded in a perfluorosulfonic acidpolymer membrane [74]. The highly specific reaction product (aflavan) induces a change in the visible absorbance spectrum of themembrane that can be detected with a colorimetric setup. Thedetection limit is below 0.5 ppm making it suitable for diabetescontrol. Direct detection of acetone in breath can also beperformed with cavity ringdown spectroscopy achieving adetection limit of 130 ppb [75].

3.3. Ammonia

Ammonia in humans is converted in urea in the liver and thenpasses in the urines via the kidney. Unconverted ammonia isexcreted in breath with a concentration in healthy subjects of tensof ppb. The concentration increases in case of malfunctioning of




Fig. 3. Morphology of hemitubes of WO3 coated with platinum atomic clusters. At left the sketch of the structure and left the electron microscope image. Reproduced withpermission from [67].



liver and kidney reaching more than 1 ppm in the presence of renalfailure [76]. However, anomalous ammonia concentrations canalso signal the presence of other diseases such as Helicobacterpylori infection [77].

The measure of the absorbance in the far-infrared spectralregion is a viable technique to measure the concentration ofammonia. One of the first measurements of ammonia in humanbreath was performed by Lachish et al. that used a tunable diodelaser absorption spectroscopy technique [78]. For real timemeasurements, the laser was locked around the absorption peakat 10.74 mm and a multiple reflection gas cell, with a total opticalpath of 50 cm, was used. The limit of detection of 1 ppm wasreached with an integration time of 1 s. Advances in tunable lasersources allowed an increase of performance. Recently, a detectionlimit of 4 ppb with an integration time of 5 s has been obtainedwith a quantum cascade laser diode coupled with a astigmaticHerriot cell with 150 m of optical path [79].

Optics can also be used to detect changes occurring in achromophore reacting with ammonia. For instance, the fluores-cence changes in a o-phthalaldehyde derivative can be used forcontinuous ammonia quantification [80].

DuBois et al. demonstrated a optical sensors formed by a thinselective membrane embedded in a pH indicator deposited on thetip of a thin fiber optic (250 mm diameter) [76]. The membrane isreported to be rather selective to gaseous ammonia but unaffectedby dissolved ions (e.g. ammonium ion) and other major gases inbreath (e.g. CO2). Such a sensor is allowed to measure ammonia inbreath in the range of 50–2000 ppb. The device has been used in amedical research to test if ammonia is a marker for hepaticencephalopathy.

Toda et al. reported a sensor based on the change ofconductivity of a liquid in contact with the breath [81]. The sensoris based on a film of diluted H2SO4 formed on the top of twohydrophilic capillary tubes placed in a concentric annulararrangement and functioning as electrodes for conductivitymeasurement. As breath passes the film collects ammonia andthe conductivity of the solution decreases. The sensor can detectammonia down to tens of ppb and it is quite insensitive to CO2 (theother major ionogenic gas in breath). The device has beendemonstrated to detect the changes of ammonia in breath as aconsequence of protein-based food in-take.

Sufficient sensitivity for breath analysis purposes has also beenshown by a zirconium phosphate coated quartz microbalances [82]and chemosensors based on nanostructured MgO3 [83].

3.4. Methylmercaptane and hydrogen sulfide

Elevated concentrations of sulphur-containing compoundshave been reported in patients with liver diseases [84] and also


in liver transplanted patients [19]. Carbon disulfide is aninteresting volatile compound, not found in the breath of healthyindividuals. This molecule is supposed to be generated as a by-product of methionine metabolism and may act as a marker fororgan rejection after lung transplantation [85].

Methyl mercaptane and hydrogen sulphide are metabolicproducts of bacteria colonizing the oral cavity. These compoundsare among the major responsibles of halitosis. They are mainlyproduced on the tongue and dental coating; often, their presence isnot associated to any manifested pathology [86]. Nonetheless, oralmalodour is an important issue in social relationship and there isan increasing request of treatment. Dentists evaluate halitosisusing their olfaction, then the evaluation of the condition and,most of all its behaviour during treatment, is largely subjective.Hence, simple and low-cost devices for an objective assessment ofhalitosis are requested.

Hydrogen sulfide is the main compound causing halitosis, butoral unpleased smell is also elicited by low concentrations ofmethyl mercaptane [87]. Halitosis has been also studied withsensor arrays (see Section 3.5) here some examples of sensorspecific to H2S and methyl mercaptane are discussed.

A chemoresistor made of a nanostructured polyaniline fiberscoated by gold nanoparticle was found to be able to detect H2S andCH3SH well below 1 ppm [88].

Some acetone sensors discussed above, showed a sufficientsensitivity to sense also H2S [68]. This means that these sensorscould be used to detect breath malodour in individuals not affectedby diabetes.

An example of colorimetric sensor enough sensitive andselective for breath malodour assessment has been proposed[89]. It is based on a sensing solution where the depletion of iodineupon reaction with hydrogen sulphide is colorimetric detectedusing starch. The device is rather bulky but it allows the detectionof H2S down to 0.05 mg L�1 an order of magnitude below thenormal level of H2S in human breath.

The detection of methyl mercaptane has been demonstratedwith a biosensor based on the enzyme monoamine oxydasetype A. The enzymatic reaction was detected by a commercialfluorescent oxygen detector formed by a fiber optic having thetip functionalized with a ruthenium organic complex [90]. Theenzyme membrane was contained in a measurement cell wherethe reaction with the methylmercaptane takes place. Theoxygen sensor measures the oxygen consumption in the cell,allowing the quantification of methyl mercaptane in breath. Thesensor was tested with real samples and demonstrated to beoperative in the range 8.7–11500 ppb. It is important to considerthat the human olfactory threshold for methyl mercaptane isaround 10 ppb and halitosis diagnosis considers at least 200 ppbof this sulphide.






3.5. Ethanol

Ethanol is not a direct product of cellular metabolism but ratherof the alcoholic fermentation induced by intestinal bacteria in caseof excessive carbohydrates uptake [91].

The average concentration of ethanol in the breath of healthyindividuals is around 100 ppb. Excess of ethanol has been related torenal failure [92], cardiopulmonary diseases [93], and proteinintoxication and abuse [94].

Although of limited interest for diagnosis, the detection ofethanol in breath is extremely valuable to control the alcoholassumption and the behaviour of treated alcoholists. In particular,the introduction of severe laws against the alcohol intake for cardrivers requires the necessity of simple, low cost and reliableethanol analyzers for the fast identification of the alcoholassumption.

Selective detection of ethanol is rather complex with chemicalsensors. The conversion of alcohol into acetaldehyde requires alsothe quantification of this compound. A stick-type biosensor basedon alcohol oxidase and aldehyde dehydrogenase has been shownto detect the alteration of ethanol and acetaldehyde in breath morethan three hours after a moderate ingestion of alcohol [95,96].

3.6. Sleep apnea

Sleep apnea is a disorder of breath occurring during sleep,which consists in reduced rate of breath per minute. Theoccurrence of sleep apnea can impair the subject during thenormal daytime activities. The diagnosis is the measurement ofbreath rhythm during sleep without perturbing the normal sleep ofthe patient. A number of sensors have been proposed for the scopemostly based on physical properties such as sound and tempera-ture [97]. Humidity is also a good candidate to monitor the breathrhythm, and special sensors of reduced response time have beendeveloped for the scope [98]. A good compromise betweensensitivity and response time has been achieved by a chemo-resistor based on multi-walled carbon nanotubes [99].

3.7. Analytes in exhaled breath condensate

EBC is largely constituted of water along with nonvolatilecompounds (e.g. proteins), as well as volatile substances thatdissolve in water [100]. Breath condensate can entrap in a liquidenvironment the nonvolatile compounds and elements present inbreath [101]. An important source of information is the detectionof viruses and bacteria that can strongly improve a quick diagnosisof diseases. Investigations detected the presence of viruses in EBC.For instance Rt-qPCR technique was applied to detect the presenceof influenza virus [102]. Anyway, such a method requires a longtime, since it involves RNA extraction and amplification, and it isnon compatible with the speed requirement in emergency roomsduring influenza outbreak. As a consequence, there is the necessityof sensors that can detect the presence of viruses and bacteriawithout the necessity of long incubation periods.

The label-free identification of influenza viruses have beendemonstrated with DNA coated carbon nanotubes [103], nano-beads amplified immunodetection on quartz microbalances [104],and semiconducting silicon nanowires [105–107]. All theseapproaches are emerging as general platforms for label-free andultrasensitive virus detection, which could be positively applied tobreath condensate. An optical method based on dynamic lightscattering detected the interaction of influenza virus withinfluenza specific antibodies immobilized onto gold nanoparticles[108]. The method, even if rather bulky in terms of transduction,can detect influenza virus at the concentration of 8.6 TCID50mL�1,three orders of magnitude smaller than Food and Drug


Administration approved test kits [109]. TCID50 is the tissueculture infective dose of virus necessary to kill the half of infectedcells.

The pH of exhaled breath condensate is a valuable source ofinformation about the acid–base balance in airway surface liningfluids. Inflammatory diseases such as asthma, chronic obstructivepulmonary disease, and cystic fibrosis can induce an acidificationof the breath condensate [110].

The measure of pH in condensate breath is made difficult by thesmall quantity of sample, the limited pH range between 7 and 8,and the rapid changes in concentration due to the evaporation ofwater. A possible solution is offered by microfabricated sensorsthat can measure the pH in a volume of about 200 mL.

An example of pH sensor in breath is offered by an AlGaN/GaNhigh mobility transistor integrated with a Peltier element wherethe condensation of breath and the pH measurement aresimultaneously performed [111]. The same technology was furtherimproved to detect also glucose levels [112]. Another quantity ofinterest measured in EBC is lactate, which is an indicator ofphysical fitness in healthy subject and it is correlated with cardio-respiratory and metabolic diseases. For this purpose it hasdeveloped a biosensor based on the lactate oxydase enzyme thatconverts lactate into pyruvate and H2O2, it has to be noted that thislast compound is unstable in EBC [113].

H2O2 is known to be indicative of oxidative stress occurring in avariety of inflammatory diseases of lungs such as asthma andchronic obstructive pulmonary disease (COPD) [114]. The detectionof H2O2 is particularly interesting in exhaled breath condensate(EBC). It has been reported that EBC of patients with COPD containsH2O2 levels varying from mean (or median) values of 0.2–2.6 mM[115]. Thus far, typical measurement protocols encompass collec-tion of the exhaled breath in condensation units and subsequentdetection of H2O2 [116].

Anh et al. demonstrated a hydrogen peroxide sensor based onan electrolyte metal oxide semiconductor transistor [117]. Thetransistor gate was formed by a Os-polyvinylpyridine layer wherehorseradish peroxidase was immobilized. Such a device wasmatched with a Peltier element driven condenser. It allowed thedetection of H2O2 in EBC with a detection limit close to 0.8 mM. Thecondensation rate of the device was about 55 mL min�1 limiting thewhole measurement to less than 15 min.

Wiedemair et al. introduced a integrated amperometric sensorthat could enable the detection of H2O2 down to 0.1 mM [118].Interestingly the addition of agarose membrane extends the use ofthe device in the gaseous phase where H2O2 can be measured witha detection limit of about 42 ppb.

A potentiometric setup based on a cotton or paper filamentimpregnated by prussian blue solid salt was recently demonstrated[119]. This sensor can measure hydrogen peroxide down to 0.1 mMin aerosol.

4. Gas sensor arrays

The aggregation of chemical sensors into arrays was firstintroduced as a method to overcome the limited selectivity ofsolid-state gas sensors. When complemented by a multivariatedata analysis technique, such as those used in chemometrics, asensor array allows for the quantification and the identification ofcompounds with a performance well beyond that of a single device[120,121]. Gas sensor arrays have a very close similitude with theproperties of the olfactory receptor neurons (ORN). Indeed, like asensor, each ORN expresses one of the possible gene encodedreceptors [122], and like a sensor, the selectivity of a ORN is ratherlimited so that a single ORN senses many compound and the samecompound is sensed by more than a ORN. This behaviour has beenobserved in many kinds of animals such as amphibians [123],






insects [124], and mammals [125]. The recognition of a specificcompound or a specific blend of compounds is a collectiveachievement of the totality of the ORNs. This combinatorialselectivity enables for instance humans to learn more than 100,000different odors with a repertoire of only about 300 differentreceptors. Due to this parallel between olfaction and gas sensorarrays, these last are often nicknamed as “electronic noses”.

The first model of electronic nose based on the similitudesbetween the ORN properties and the sensors properties weredemonstrated in 1982 with a set of metal-oxide semiconductor gassensors [126]. Rather interestingly, the demonstration was givenusing sensor technologies very distant from the biologicalprinciple. Electronic noses soon became a practical methodologyto combine scarcely selective sensors for qualitative, andsometimes quantitative, analysis of gaseous samples. Since theubiquities of lack of selectivity almost all sensor technologies havebeen used to assemble electronic noses.

It is important to observe that even if natural and artificialolfaction share the same principle of combinatorial selectivity theyare still showing important different behaviours. For instance theidentification of a single compound in a background of VOC is almostimpossible with current electronic noses but it is a routine practicefor natural olfaction. This task could be the result of the signalprocessing whose properties are still unclear. However, this lack ofperformance is also likely due to the fact that the range of selectivityof chemical sensors is certainly less specific than those of ORNs.

As a consequence, electronic noses are good instruments for theclassification of different VOC patterns, so they can adequately beapplied to those cases where the difference between samplesinvolves a number of different compounds. This is the caseobserved in several pathologies where rather than the change ofthe concentration of one or few biomarkers the global VOCs profileis altered [127,128].

Electronic noses are mainly utilized for classification purposesand only in some cases they can provide the quantification ofspecific compounds. As a consequence, electronic noses are oflimited help to identify diseases related volatile compounds, butthey are unvaluable tools to classify individuals according to thepathology of interest.

In an electronic nose the measured samples are translated intothe pattern of signals of the individual sensors of the instrument.Then, electronic noses data requires the application of multivariatedata analysis techniques in order to be properly analyzed.

The most utilized algorithms to this regard are those derivedfrom chemometrics such as the principal component analysis(PCA) for unsupervised classification and linear discriminantanalysis (LDA) and partial least squares discriminant analysis(PLS-DA) for supervised classification. More complex classifiers arealso used among them neural networks and supervised vectormachine techniques. Data analysis for electronic noses is discussedin excellent review papers [129–131]. Since electronic noses arealmost used for classification purposes, the performance of thesedevices are typically reported in terms of classification rates.

Electronic noses are of wide interest in respiratory medicineand in the wide field of cancer research. The known acceleratedmetabolism of tumor cells may likely produce VOC patterns thatcan differ in terms of quantity and quality from those released byhealthy subjects.

Lung cancer attracted most of the interest. Actually, breath cancollect evidences of tumors in other body compartment than therespiratory tract as it will be discussed later.

4.1. Lung cancer

The relationship between exhaled breath composition and lungcancer has been investigated by several authors [132]. A number of


GC-MS studies that appeared in the last ten years [71,133–135]gave a rather sparse result, due to a general lack of standardizationof breath sampling and analysis. About 170 different VOCs weredetected in total and only 17 compounds were found at least in twoexperiments [136].

Fig. 4 shows the findings from Peng et al. [135] where a patternof 42 VOC are compared between a population of diseased and apopulation of controls. Different chemical families are observedsuch as ketones, alkanes, aromatic, alcohols and hydrazines. Noclear relationship between molecular structure and abundance isfound. Isomers with a slight difference in lateral groups positioncan appear with very different abundances. For instance 1-methyl-2-(1-methylethyl)-benzene (CAS: 527-84-4) is equally abundant inlung cancer and healthy group but 1-methyl-3-(1methylethyl)-benzene (CAS: 535-77-3) is about five times more abundant in lungcancer group than controls. An interesting paper recently reviewedthe VOC pattern compound in breath of lung cancer individualsadvancing an explanation for their possible pathway [137].

All these studies evidence that it is not possible to detect anindividual marker for the lung cancer, but a number of chemicalfamilies are altered by the disease. This is a typical situation wherechemical sensor arrays can be applied. Fig. 5 shows the patterns oflung cancer exhaled breath, in terms of molecular families,compared with the evolution of fish’s headspace at differentstorage time. In both cases the difference between the differentstates is clearly captured by the pattern of chemical families. Onthese basis electronic noses have been applied to the identificationof lung cancer disease from the analysis of breath. Up to now fourmajor sensors technologies have been adopted for the scope.

The first experiment pointing out the possibility to diagnoselung cancer with a gas sensor array has been produced by an arrayof porphyrins coated quartz microbalances [138]. Porphyrins showunique binding properties that are widely exploited in nature toaccomplish functions essential for life; the potential mimic ofthese functions with synthetic counterparts has provided the basisof many kinds of chemical sensors. The porphyrin molecularframework offers a wide range of interaction mechanisms foranalyte binding, spanning from the weak Van der Waals forces tohydrogen bond, to p–p interactions and finally to the coordinationto the central metal ion. In spite of the QMB simplicity and theirrelative low sensitivity to the amount of the adsorbed molecules,porphyrins coated QMB have been demonstrated to be an efficientelement for electronic nose in particular for food analysis andmedical diagnosis applications [139].

Di Natale et al. [138] investigated the possibility of usingporphyrins coated QMB to check whether volatile compounds inexhaled air may diagnose lung cancer. Breath samples werecollected and immediately analysed. A total of 60 individuals wereinvolved in the study. 35 of them were affected by lung cancer, 18healthy subjects, and nine lung cancer affected were also measuredafter the surgical therapy. The application of a ‘partial least squaresdiscriminant analysis’ (PLS-DA) found out a 100% classification oflung cancer affected patients, 94% of reference was correctlyclassified. Furthermore, among the nine subjects measured afterthe tumor resection, five were classified as healthy subjects andfour as a separate class.

This preliminary result was corroborated by successive inves-tigations aimed at extending studied cases introducing comorbid-ities such as COPD [140]. Besides, confirming the previous findings,this paper shows that porphyrins coated QMB can capture thedifferences induced by the presence of comorbidities. In the samepaper, the effects of some compounds (aniline, o-toluidine, andcyclopentane) frequently found in the breath of lung cancersubjects have also been studied. These three compounds have beenadded each at the concentration of 400 ppb to the breath of healthyindividuals. Results indicate that breath samples of control




Fig. 5. Left: average abundance of families of VOC in the breath of healty and lung cancer affected people (plot based on data from [135]). Right: time evolution of the familiesof VOC in the headspace of fish during the storage at 0 �C. Plot based on data from [195].

Fig. 4. Comparison of the abundace of 42 volatile compounds in the breath of subjects affected by lung cancer and healthy controls. CAS is indicated for those compounds forwhich it is available. Plot based on data from [135].



Please cite this article in press as: C. Di Natale, et al., Solid-state gas sensors for breath analysis: A review, Anal. Chim. Acta (2014), http://dx.doi.org/10.1016/j.aca.2014.03.014





individuals drift towards the lung cancer group when added witheither single or mixtures of these alleged cancer-related com-pounds. More recently the same electronic nose has been used tomeasure the alveolar air samples in-situ by a modified endoscopicprobe [141]. Results indicate the tumor region as the source ofchemicals and furthermore evidenced the possibility to detectamong different kinds of lung cancers.

The results obtained by the porphyrins based QMB wereconfirmed and expanded by a number of different experimentsusing various sensors technologies. Particularly interesting is theuse of sensors based on the conductivity changes of a layer of goldnanoparticles coated with various functional organic molecules.Peng et al. used an array of gold nanoparticles (GNP) coated byalkanethiols [135]. The same sensor array was previouslydemonstrated to be able to identify lung cancer cell lines culturesfrom normal lung cell cultures [142].

Organically capped GNP are known to give rise to highsensitive chemoresistors, which can be easily coated by organicmolecules functionalized with a thiol group to ensure a covalentbinding onto the gold surface. The GNP units form a layer onto asurface where planar electrodes are embedded; the organicmolecules onto the gold surface act a sort of spacer modulatingthe tunneling between adjacent GNPs. The absorption of volatilecompound on the organic molecule can then modulate thetransmission of electrons from adjacent GNPs resulting in a globalmodulation of the conductivity [143].

Their results are rather interesting, since the sensitive layers areexpected to be rather unselective, being based on alkyl chains. Theinteractions with alkanes indeed are supposed to be ruled mainlyby dispersion interactions [144]. Fig. 6 shows the first two principalcomponents of the experimental data. Real samples have beencomplemented by an artificial breath simulation where some ofthe compounds of Fig. 5 are added.

An inspection of the results suggests that the sensors are rathercorrelated and that the identification is based on the differentabundance of VOC in the breath of lung cancer affected individuals.Besides, the separation between artificial samples is remarkable.Actually the total difference in the samples were of few tens of ppb,this shows the great sensitivity of organically modified GNP even ifin this case the scarce selectivity and the high cross-correlation ofthe sensors rise questions about the behaviour of the array in thepresence of confounding factors such as co-morbidities.

Fig. 6. Principal component analysis scores plot of real breath and simulated breathdata distinguishes between healthy a nd lung cancer cases. Reproduced withpermission from [135].


Indeed, it should be considered that the VOC potentiallyconnected with the disease are likely concealed by the largeamount of endogenous and exogenous factors. For example, drugsuptake and different lifestyles may influence the VOC produced invivo by subjects, fabricating the differences with regard to a givencontrol group.

The potentialities of the organically capped GNPs have beenfurther demonstrated identifying both first-stages and post tumorresection cases [145].

The concept of a chemical sensor formed by a non-conductingbut chemically sensitive organic molecule matched with aconducting but non sensitive inorganic structure was imple-mented some years ago using carbon black filaments as theconductive sensor element [146]. Carbon black was mixed with alarge number of organic polymers giving rise to large arrays thatwere at the basis of a commercially avaiable electronic nose(Cyranose). Cyranose was built to be simply operated and probablyfor this reason it has been quite popular in the community ofphysicians attracted by the use of electronic noses for diagnosispurposes. Enose measurements with Cyranose have good withinday- and between-day variability [147] making it suitable forlongitudinal assessment of lung inflammation, monitoring theeffect of pharmacological treatment in patients with respiratorydisease [148], and, potentially, personalized therapeutic strategies[149].

Among the various applications, it was also applied to lungcancer diagnosis. Positive results were obtained in the identifica-tion of lung cancer from control population [150] and also in athree classes problem where a group of COPD affected patientswere also included [151]. The same electronic nose was also foundable to identify the breath of patients affected by mesothelioma[152]; this is an aggressive tumor of the pleura surface cells that ismainly consequent to asbestos exposure.

Porphyrins and other Lewis acid-basis indicators were theelements of a colorimetric electronic nose that was brilliantlycomplemented by low-cost highly distributed color measurementinstruments such as digital flatbed scanners and digital cameras[153]. This sensor technology was also demonstrated to be able todistinguish the breath of lung cancer from the breath of a controlpopulation [154,155]. Interestingy, such a sensor array iscompletely different from that demonstrated by Peng et al.[135]. Indeed, dispersion interactions are not supposed to elicitcolor changes in optical indicators and then a completely different(even if likely overlapping) interaction takes place.

Lung cancer identification was also approached with an array ofsurface acoustic waves devices coated with different chro-matographic solid phases [156]. Even if based on a limited numberof subjects, this result evidence the large alteration of the breathcomposition. Indeed, the fact that positive identification isobtained with so different sensors shows the complexity of theVOC pattern alteration in case of lung cancer.

Finally, it is interesting to consider that the sensor arrays thathave been demonstrated successful for lung cancer identificationare based on organic sensing layers. The four major involvedtechnologies are based on: the composite polymers–carbon blackforming the Cyranose technology [146], the organically function-alized metallic nanoparticles and carbon nanotubes developed atthe Technion Institute [143], the porphyrins coated quartzmicrobalance developed at the University of Rome Tor Vergata[139], and the colorimetric porphyrins and acid–base indicatorsoriginally developed at the Illinois University [153].

These sensors differ in terms of the actually measured quantity.Cyranose and Technion sensors are resistors, University of RomeTor Vergata are resonators, where the frequency of an oscillatorcircuit is measured, and in case of the Illinois University sensors thechanges of colour are measured.






However, in terms of chemical selectivity, these sensors are lessdifferent than expected. Indeed, all of them are based on polymersor molecular films of various compositions, so their sensitivity is aunique blend of Van der Waals forces, hydrogen bonds and in somecases coordination.

This common ground of interactions makes less surprising thefact that all these different sensor technologies can detect lungcancer.

However, each sensor array likely captures the total VOCpattern alteration from a different point of view, it is then easy todevise that a combined use of different arrays could drasticallyimprove the performance. The concept of a hybrid electronic nosewas raised several years ago and the effective benefit of using amulti-technology approach has been provided even if in othercontexts [157,158].

To this regard it is interesting to mention a comparative studybetween porphyrin coated QMB and Cyranose sensors performedin the frame of a European project aimed at a global study ofasthma [159]. Both sensors were simultaneous calibrated towardsa number of VOC such as alcohols, alkanes and ethers, and theresults show that both instruments can detect these compounds atconcentration of hundreds of ppb; more interestingly thecombination of sensor technologies reduces the limit of detectionof an order of magnitude.

4.2. Other respiratory diseases

Electronic noses have also been investigated for the detection ofother respiratory pathologies, besides cancer. For instance, asthmawas studied with a porphyrin based QMB [160] and Cyranosetechnology was used for both asthma, COPD [161–165] andsarcoidosis [166].

Oligo-peptides coated surface acoustic waves array was used toidentify different bacterial infections in the airways of patients inan intensive care unit [167]. The use of artificial peptide chains is anattractive method to design chemical sensors of either enhancedselectivity or enhanced cross-selectivity. In the mentioned paper,hydrophobic, hydrophilic, acidic and basic oligo-peptides weresynthesized giving rise to a wide spectrum or cross-selectivesensors.

Cyranose technology again was proven to be able to identifypatients affected by pneumonia [168–170]. Pneumonia detectionhas also been shown by a hybrid array formed by metal-oxidechemosensors and Pt and Pd field effect transistors [171].

An appealing application for breath analysis is the detection oftuberculosis from the breath. Tuberculosis is endemic in manythird world countries and a fast and non-invasive detection of thediseases is fundamental to a quick segregation of the patients andthen a reduced spread of the infection. Some VOC such asnaphthalene,1-methyl- (CAS:90-12-0), 3-heptanone (CAS:106-35-4), and methylcyclododecane (CAS:1731-43-7) were found alteredin the breath of individuals affected by tuberculosis [172].Although an electronic nose based on conductive polymers wasapplied to the detection of tuberculosis, it is impossible to discussthe performance of the sensors, since details about the sensorscomposition were not disclosed in the paper [173].

The changes in exhaled breath in lung transplanted individualwas also studied with a Cyranose device [174].

4.3. Non respiratory diseases

Oxydative stress metabolites are supposed to be a majorcomponent of lung cancer breath, and these findings introduce thepossibility that breath composition is altered also in the presenceof cancers affecting organs other than the respiratory tract. Therelationship between breast cancer and volatile compounds was


studied by Phillips et al. [175]. They demonstrated that the VOCprofile, adequately processed by a fuzzy logics based classifier, canidentify breast cancer.

This hypothesis was confirmed in the case of breast cancer usingGC–MS investigations but organically modified GNPs provided asubstantial demonstration of this assumption differentiating be-tween ‘healthy’ and ‘cancerous’ breath in case of lung, breast,colorectal, and prostate cancers [176,177]. Interestingly, the resultshave been positive both in binary classifications and in multiclassclassification where more cancer types were considered.

Besidesthe respiratorytract, breath compositon issupposed to becontributed by the digestive system. Therefore breath analysis is alsoexptected to be a valid method to detect diseases of digestive organs.This assumption has not been studied in detail so far, but recently thedetection of gastric cancer by an array of organically functionalizedgold and platinum nanoparticles has been shown [178].

Kidney functionality was studied with an array of organicallyfunctionalized gold nanoparticles [179] and peptides coated quartzmicrobalances [180]

The previous examples are somewhat expected, but therelationship between breath composition and neurological dis-eases is not straightforward. However, some evidences have beenprovided for the identification of the breath of multiple sclerosispatients with respect to a control population using an array ofchemoresistors formed by a network of single walled carbonnanotubes (SWCNT) coated by polycyclic aromatic hydrocarbonsderivatives (PAH) [181]. Interestingly, PAH, known as majoraggressive pollutants, can play a role in diagnosis. In this paper,the classification of multiple sclerosis vs. healthy subjectsoutperforms other classification schemes that could be consideredas confounding factors such as smokers vs. non-smokers and malesvs. females.

Parkinson disease was also investigated in a rat modelpopulation. The breath of rats with a 50% of asymptomaticdopaminergic neurons lesions in substantia nigra was measuredwith the organically capped GNPs previously discussed and datawere compared with those from a healthy population of rats [182].

A more interesting paper studied Parkinson and Alzheimerdiseases evidencing the possibility to distinguish between thediseases and healthy control and furthermore between Parkinsonand Alzheimer diseases [183].

These two latter studies were performed with a hybrid sensorarray formed by organically capped metallic nanoparticles andorganically functionalized SWCNT. Significantly, a mixed sensortechnology was used.

Remarkable applications regard again the investigation ofmodel rats to test the capability to identify in one case the chronicrenal failure with an array of organically capped nanoparticles[184] and in another case acute liver failure with an array of metal-oxide semiconductors [185].

Another important field of application is related to breath odourcontrol, this can include both pathological and non pathologicalconditions. In Sections 3.2 and 3.4 the application of ammonia andmethylmercaptane sensors to monitor diabetes and halitosis havebeen discussed. However, these conditions can also be studiedwith arrays of non selective sensors. Diabetes in particular can bemonitored with arrays of chemoresistors based on metal-oxidesemiconductors [186] and conducting polymers [187]. Oralmalodor have been shown to be characterized by different sensortechnologies such as: porphyrins coated quartz microbalance[188], GC solid phase coated quartz microbalance [189], and metal-oxide semiconductors [190,191].

It is worth to end this discussion mentioning a couple of furtherapplications pointing out the relationship between metabolismand breath. The first considers the breath alteration indiced byphysical stress and the second by the pregnancy state.




Table 2Arrays of non specific sensors.

Sensor arraytechnology

Sensing materials Classification tool Target disease Recruited people Classification rate Reference

Chemoresistors Organically functionalized Auand Pt nanoparticles

Discriminant analysis Lung cancer follow-up 17 patients >80% [145]

Chemoresistors Organically functionalized Aunanoparticles

Principal component analysis Lung cancer 40 lung cancer 56 controls >86% [135]

Quartz microbalance Metalloporphyrins Partial least squaresdiscriminant

Lung cancer 35 lung cancer, 9 post cancer resection, 18 healths 90% [138]


Lung cancer 20 lung cancer, 10 healthy 91% [141]


Lung cancer 28 lung cancer, 28 other lung pathologies, 36 controls 93% cancer vs. controls85% cancer vs. other pathologies

[140]

Chemoresistors Carbon black – polymerscomposites

Discriminant analysis Lung cancer and chronicobstructive pulmonarydisease

10 patients lung cancer, 10 patients asthma, 10controls

>85 % [151]


Principal components Lung transplant follow up 16 transplants, 33 healthy 73% [174]


Support vector machine Lung cancer 14 lung cancer, 20 healthy 88% [150]

Colorimetry Various non reported colorindicators

Random forest classifier Lung cancer 49 lung cancer, 18 COPD, 15 pulmonary fibrosis, 20pulmonary artherial hypertension, 20 sarcoidosis, 21controls

86% [154]

Colorimetry Porphyrins, acid basis indicators Logistic prediction model Lung cancer 92 lung cancer, 137 controls 81% [155]Surface acoustic wave GC column Neural network Lung cancer 5 lung cancer, 5 controls 80 % [156]Chemoresistors Carbon black – polymers

compositesDiscriminant analysis Mesathelioma 13 mesathelioma, 13 long term asbesots exposure, 13

controls>80 % [152]


Discriminant analysis Asthma 40 subjects: 10 severe asthma, 10 mild asthma, 20controls

>90% [162]

Quartz microbalances Metalloporphyrins Feed forward neural network Asthma 27 asthma, 24 controls 95 % [160]Chemoresistors Carbon black – polymers

compositesDiscriminant analysis Asthma and chronic

obstructive pulmonarydisease

90 patients in 4 groups Asthma vs. COPD >95% [161]


Discriminant analysis Asthma and chronicobstructive pulmonarydisease

15 patients [163]


Principal components Asthma and chronicobstructive pulmonary

21 fixed asthma, 39 classic asthma, 40 COPD 88% [164]


Discriminant analysis COPD discriminatio of 1-antitrypsin deficiency

10 COPD with 1-antitrypsin deficiency, 23 COPD, 10controls

82% [165]


Principal components Invasive pulmonaryaspergillosis

46 subjects 90% [168]


Support vector machine Pneumonia 25 subjects >80% [169]

chemoresistors Carbon black – polymerscomposites

Partial least squares Pneumonia 400 patienrt in intensive care units Regression of clinical pneumoniascore R2 = 0.81

[170]

Chemoresistors andChemFETs

Metal oxide semiconductors andmetal-FET

Discriminant analysis Ventilator associatedpneumonia

44 patientssample: bronchoalveolar fluid

77% [171]


Discriminant analysis Pulmonary arterialhypertension

22 diseases, 23 control >80% [12]


Discriminant analysis Pulmonary sarcoidosis 31 patients, 25 controls 83% [166]

Quarts microbalance Peptides Discriminant analysis Bacterial infections 96 patients in intensive care unit 98% [167]Chemoresistors Conducting polymers Partial least squares

discriminant analysisTuberculosis 80 tuberculosis, 243 negative

sample: sputum70% [173]


Principal component analysis Various cancers (lung,breast, colorectal,prostate)

177 patients NA [176]

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Table2 (Continued)

Sensor arraytechnology

Sensing materials Classification tool Target disease Recruited people Classification rate Reference


Discriminant analysis Breast cancer 16 benign breast, 13 malignant lesions, 7 controls 86% [177]


Discriminant analysis Gastric cancer 37 gastric cancer, 32 ulcer, 61 less severe conditions 89% [178]

12 chemoresistors Metal oxide semiconductors Principal component analysis,support vector ordinalregression [196]

Diabetes, 192 diabetics in 4 classes according to blood glucosevalue

68% [186]

Chemoresistors Conducting polymers Principal component analysis Diabetes 3 patients3 controls

NA [187]

6 quartz microbalance Peptides Discriminant analysis Uremiachronic renal insufficiency(CRI)chronic renal failure (CRF)

61 CRI/CRF83 uremia30 control

Uremia: 79.5%CRI vs. CRF: 90%control: 100%

[180]

Chemoresistors Organically functionalized Aunanoparticles

Support vector machine Chronic kidney disease 62 >79% [179]

Chemoresistors Metal-oxide semiconductor Principal component analysis Acute liver failure in rats 14 rats with liver failure, 9 healthy rats 94% [185]Chemoresistors Organically functionalized Au

and Pt nanoparticles,functionalized CNT

Discriminant analysis Parkinson disease 19 rats 95% [181]

Chemoresistors Polycyclic aromatic hydrocarbonfunctionalized CNT

Discriminant analysis Multiple sclerosis 34 multiple sclerosis, 17 healthy >80% [177]


Discriminant analysis Alzheimer and Parkinsondiseases

57 patients >78% [183]


Discriminant analysis Pregnancy 130 subjects (78 pregnant) 87% [184]

Chemoresistors Metral oxide semiconductors Multiple linear regression Halitosis 49 patients Regression between odor scoreand enose signals R2 = 0.41p<0.0001

[190]

Quartz microbalance Metalloporphyrins Principal components Halitosis 7 subjects 100% [188]Quartz microbalance Lipids, polar GC phase, cellulose Principal component analysis Oral malodor number not available Not available [189]Chemoresistors Metal oxide semiconductors Multiple linear regression Oral malodor 66 subjects characterized with a sensorial oral

malodor scoreRegression coefficient r = 0.81 [191]


Principal components Physical stress 10 healthy undergoing physical excersises Correlation between principalcomponents and exhaled pH

[193]

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et al.,

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review,

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Chim

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In both cases metabolism acceleration and oxidative stress cancontribute to change exhaled VOCs and a first identification of suchalterations have been provided again with a Cyranose sensor array[192,193].

4.4. Conclusions on gas sensor arrays

A list of applications of electronic noses in breath analysis isgiven in Table 2. For each application the table indicates the sensortechnology and the sensing material, the targeted disease, the sizeof the sample and finally the algorithm used for the classificationand the resulting classification rate. The classification rate isexpressed as the rate of successful identification of the disease. Infew cases the electronic nose data have been used in a regressionmodel to estimate a disease related score. In almost all thesepapers linear algorithms (such as discriminant analysis, PCA, andPLS) have been utilized to identify the disease.

Almost all the listed applications were tested on human breathcollected from diseased and healthy individuals. In someapplications the electronic noses have been tested on animalmodels or on artificial breath obtained mixing the compoundsexpected to be relevant for the target disease.

One of the major drawback of electronic noses is the sometimesexcessive dependence of the class membership estimation on thetotal composition of the sample. In practice, fluctuations ofcompounds unrelated to the targeted disease but sensed by theelectronic nose sensors can easily confound the result. This risk isparticularly important in breath analysis where the backgroundcomposition of the breath is variable both within-day andbetween-day due to different activities and conditions (from thetrivial consequence of food uptake to alterations in the ambientair). The actual influence of background fluctuations has to beevaluated for each separated case; however some recent studiesevidence that in case of COPD both a porphyrin coated QMB sensorarray [194] and a Cyranonose [147] are not affected by changes inthe background composition of the breath.

A final remark about the role of sensor arrays in breath analysiscould be done considering the case of the organically capped NPeven if it can be applied to other sensor technologies. These sensorshave been demonstrated to be almost universal being able todiscriminate different cancers, renal failures and also neurologicaldiseases always with respect to a healthy control population. It ishardly to believe that electronic noses can detect the presence of aparticular disease in a randomly selected individual when differentdiseases, of different gravity but each affecting the breath, arepresent at once.

An interesting change of paradigm with respect to the currentliterature could be introduced considering the changes occurringin a single subject during the transition from a “healthy status”towards pathology. Indeed, one of the most powerful application ofsensors, expected to be small easy to use and low cost, is theirpersonal use as a sort of extension of the natural senses to probethe own body. This could provide a sort of increase of sensitivityabout the changes occurring in the body enabling a morepreventive detection of diseases.

5. Conclusions

The relationship between VOC patterns in breath and somediseases is now quite clearly ascertained but several practicalproblems need to be addressed in order to develop routinely usabledevices. The open problems involve the breath sampling and thesensors features.

Standardized methods of breath sampling are necessary tomake repeatable and comparable the analysis performed ondifferent individuals and in different locations. Issues such as the


composition and the temperature of the inhaled air and theseparation of the breath in dead-space and alveolar air areexamples of the complexity of the problem.

Another often underrated element is the preparation of thesubject in terms of diet and life-style in the hours “immediately”before the analysis. It is worth to consider that the termimmediately before is still rather undefined. A clear quantitativedescription of the decay of food intake effect on breathcomposition is still not available.

In terms of sensors, there is the necessity of selective sensors forfew well recognized markers and for these sensors the mainproblem is just the rejection of confounding components, watervapour above the others.

Sensor arrays are the viable approach in case of VOCs patternalteration rather than a restricted and well-known number of VOC.This is a rather common case in many diseases and in particular incancer where the metabolic changes of tumor cells result in a largevariation in terms of quantity and quality of the emittedcompounds.

Being not related to a single compound sensor arrays are moresubject to the influence of confounding parameters such as co-morbidities and other independent pathologies. Even for sensorsas array elements the main issues are the sensitivity and theselectivity in terms of orthogonality of the sensor signals. To thisregard, an interesting method to increase the current performancecould be the integration of different sensors technologies in orderto increase the amount of captured compounds and theorthogonality of the data of the array.

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solid-state gas sensors for breath analysis: a review

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