Analytica Chimica Acta 824 (2014) 1–17

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Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca

Review

Solid-state gas sensors for breath analysis: A review Corrado Di Natale a, * , Roberto Paolesse b , Eugenio Martinelli a , Rosamaria Capuano a a b

Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, Roma 00133, Italy Department 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 the major sensor technologies in the field of breath analysis.  A review of the diseases that could be diagnosed with solid-state sensors.  A discussion about the sampling methods and the critical points in the analysis.

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 November 2013 Received in revised form 10 March 2014 Accepted 12 March 2014 Available online 15 March 2014

The analysis of volatile compounds is an efficient method to appraise information about the chemical composition of liquids and solids. This principle is applied to several practical applications, such as food analysis where many important features (e.g. freshness) can be directly inferred from the analysis of volatile compounds. The same approach can also be applied to a human body where the volatile compounds, collected from the skin, the breath or in the headspace of fluids, might contain information that could be used to diagnose several kinds of diseases. In particular, breath is widely studied and many diseases can be potentially detected from breath analysis. The most fascinating property of breath analysis is the non-invasiveness of the sample collection. Solid-state sensors are considered the natural complement to breath analysis, matching the non-invasiveness with typical sensor features such as low-cost, easiness of use, portability, and the integration with the information networks. Sensors based breath analysis is then expected to dramatically extend the diagnostic capabilities enabling 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 to this purpose. These investigations involve both specific sensors designed to detect individual compounds and non-specific sensors, operated in array configurations, aimed at clustering subjects according to their health conditions. In this paper, the recent significant applications of these sensors to breath analysis are reviewed and discussed. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Gas sensors Sensor arrays Breath analysis Medical diagnosis

Contents 1. 2. 3.

Introduction . . . . . . . . . . . Breath collection methods Selective sensors . . . . . . . 3.1. Nitric oxide . . . . . .

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* Corresponding author. E-mail address: [email protected] (C. Di Natale). http://dx.doi.org/10.1016/j.aca.2014.03.014 0003-2670/ ã 2014 Elsevier B.V. All rights reserved.

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3.2. Acetone . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Methylmercaptane and hydrogen sulfide 3.5. Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Sleep apnea . . . . . . . . . . . . . . . . . . . . . . . 3.7. Analytes in exhaled breath condensate . Gas sensor arrays . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Lung cancer . . . . . . . . . . . . . . . . . . . . . . . 4.2. Other respiratory diseases . . . . . . . . . . . 4.3. Non respiratory diseases . . . . . . . . . . . . 4.4. Conclusions on gas sensor arrays . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Corrado Di Natale is a full professor at the Department of Electronic Engineering of the University of Rome Tor Vergata where he teaches courses on electronicdevices and sensors. His research activity is concerned with the development of chemical and bio-sensors, artificial sensorial systems (olfaction andtaste), and the optical and electronic properties of organic and molecular materials. He authored more than 450 papers on international journals and conferenceproceedings. He chaired the 9th International Symposium on olfaction and elec-tronic nose (Rome, 2002) and Eurosensors XVIII Conference (Rome, 2004) and wasmember of the organizing committee of national and international conferences insensors.

Roberto Paolesse is a full professor of general chemistry at the Department of Chemical Science and Technology of the University of Rome Tor Vergata where he givescourses on general chemistry and supramolecular chemistry. His research interestsinclude the synthesis and reactivity of transition metal complexes with porphyrinsand related macrocycles and the development and application of chemical sensors.He authored more than 400 papers on international journals and conferences. Hewas chairman of the 4th International Conference on Porphyrins and Phtalocyanines(Rome, 2006) and he is a member of the steering committee of International Meetingof Chemical Sensors conferences series.

Eugenio Martinelli is an assistant professor in electronics at the Department ofElectronic Engineering of the University of Rome Tor Vergata. His research activityis concerned with the development of chemical and biological sensors, artificialsensorial systems (olfaction and taste) and their applications, sensor interfaces anddata processing. He authored 120 papers on international journals and conferences.

Rosamaria Capuano has a post-doc position at the Department of Electronic Engineering of the University of Rome Tor Vergata. Her research interests are in the field of chemical sensors and their application for medical diagnosis. She authored 15 papers on international journals and conferences.

1. Introduction Analytical chemistry plays a conspicuous role in medical diagnostics. Advances in this discipline introduced a number of methodologies and instrumentations for the detection of target molecules in fluid, such as urine and blood, which can be sampled with a relatively minimum invasiveness for the subject. Progresses in the analysis of gaseous samples stimulated the investigation of the volatile compounds that are found in the atmosphere surrounding the human body. The breath is surely the most rich and accessible body domain for the collection of endogenous volatile organic compounds (VOC). Therefore, breath analysis is rapidly emerging as a fascinating application field for the modern analytical chemistry. Furthermore, VOC analysis is attractive in medicine because of its absolutely non-invasive character, and it can be applied to any stage of the life. The correlation between VOC and health was well known in the old clinical practices. Indeed, modern medicine takes advantage of the instruments offered by the technological progress, but in the past physicians interacted with the body of the patient using all the senses, olfaction included. Nowadays, the introduction of diagnostics instruments makes less relevant sensorial inspection of the body, and this almost disappeared practice is rather confined to the realm of anecdotes [1].

Since the seventies, the improvement of instrumental analysis of VOC led to reconsider their role in medicine. The seminal paper of Linus Pauling [2] defined the frame for the definition of VOC profile out of a human body. This could be considered the basis for the finding of anomalies that can be connected to specific pathologies. A number of studies appeared in the last decades correlates the presence of VOC in breath to some specific disease. The quest of biomarkers, univocally connected to pathologies, has been thwarted by the fact that many diseases are related to patterns of VOC instead than individual specific compounds. These researches are reassumed by a number of review papers and they will not be further reviewed here [3–5]. An additional burst to the interest in VOCs analysis for medical purposes has been provided by the development and the diffusion of solid-state chemical sensors. The impact of these devices and their possible applications is discussed in this review paper considering the case of breath analysis. Breath is the natural interface for the extraction of VOC from the living bodies. Inhaled air, besides O2 and N2, contains a number of compounds present in the environment. On the contrary, as a result of the respiration, exhaled air is partially depleted of O2, enriched of CO2, and contains the VOC resulting from the interaction with the environment and the metabolic processes.

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In order to be excreted through the breath, the metabolic products are broken down to volatile molecules that can be carried through the blood and lymphatic vessels to the lung tissues, where they can be exchanged with air [6]. Besides, an important portion of VOC in breath is produced by airway/pulmonary metabolism thus reflecting lung physiology and pathophysiology. It is then reasonable to suppose that alterations of metabolic processes can alter the composition of the exhaled breath, and a careful scrutiny of the breath could trace back the presence of pathologies. These investigations are of particular interest in the wide field of cancer research. The accelerated metabolism of tumour cells may likely produce VOC patterns that can differ in terms of quantity and quality from those released by healthy subjects. Consequently, a pattern of detectable VOC arising from living tissues could provide a signature of fundamental biological processes including cell proliferation, growth arrest and cell death [6]. The application of these clear and straightforward concepts may be practically hampered by the fact that myriads of molecular species are contained in breath and only few of them are interesting for diagnosis. Furthermore, such molecules naturally occur at a very low concentration and the alteration is still in very low concentration range (typically lesser than ppb). Exhaled breath is composed of nitrogen, oxygen, carbon dioxide, carbon monoxide, nitric oxide, water vapour, and a mixture of VOC such as hydrocarbons, alcohols, terpenes, aldheydes, and other nonvolatile molecules only detectable in breath condensate (EBC) such as isoprostanes and cytokines [7–9]. In general it is possible to distinguish in the breath two different portions 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 the inspired air saturated of water vapour; additionally it contains VOC produced both in the nasal cavity and in the upper airways. The last portion of deeply exhaled breath (alveolar air) is essentially the headspace of the pulmonary tissue and it can also be considered as the headspace of the blood circulating in the body. Alveolar air is influenced by VOC exchange across capillary membrane between blood and alveoli: the volatile compound diffuses from the compartment with the higher vapour pressure to the lower, until the equilibrium between the two compartments is reached. This phenomenon is ruled by the Henry partition coefficient, which determines for each VOC the relative concentration in the blood– breath interface [6,10,11]. Gaseous and capillary VOC equilibrate rapidly in the pulmonary alveoli. The process varies with the phase of respiration. During the inspiratory phase, environmental VOCs are in equilibrium with pulmonary venous blood, while during the expiratory phase, pulmonary arterial blood is in equilibrium with VOC in alveolar breath. This effect has been evidenced by the correlation of sensor array signals with the changes of pulmonary arterial pressure [12]. Volatile compounds originate from systemic and metabolic processes (endogenous VOC) and from exogenous sources. Each of these groups of compounds contains information useful for different scopes. 2. Breath collection methods Exhaled breath contains thousands of endogenous VOC in low concentration ranging from mmol L 1 to fmol L 1 [13]. A recent review identifed 1764 human related VOC, 874 of which are found in exhaled breath [14]. The variability of analytical results obtained in different studies depends on the absence of a standard procedure for breath sampling and the related analytical techniques. This is probably also due also to impurities contamination from inspired air or to dilution with dead-space air. Therefore, sampling procedures are of

3

outmost importance in breath analysis [15]. Two main aspects have to be taken into account for an effective breath sampling procedure: the portion of exhaled breath to be analyzed (total breath, including dead space air, or alveolar portion, and the sampling technique. The collection of the total breath is the most direct sampling. It does not require any additional apparatus and the subjects are only requested to deeply breathe into the collecting system. Drawbacks of total breath sampling are the dilution and contamination effects due to the contribution of the dead space. On the other hand, in the alveolar breath the endogenous VOC are more concentrated. The correct separation of alveolar breath from the total breath requires particular care. The simplest method is the “time-controlled sampling” where the separation is automatically done at some pre-specified time after the start of expiration. This technique obvioulsy suffers a big variability due to wide differences of individual dead-space volumes (depending among the other variables on weight and height) and different breathing maneuvers. Controlled alveolar sampling, by means of the measure of expired CO2 concentration, is instead an effective method to sample blood-borne volatile biomarkers [16]. Another aspect to consider is that breath sampling can be performed for a single breath or for multiple breath cycles. Although sampling a single breath is simple, its composition may show a large variability among individuals due to very dissimilar modes and depth of breathing. Multiple breath technique seems to overcome these problems providing a larger reproducibility [17]. The presence of contaminants in inspired air is another important problem for breath analysis. In addition, it is important also to consider the storage condition as possible confounding factor. To this regard, polyvinyl fluoride is a diffused material for breath collection. However bags made of polyvinyl fluoride may release N,N-dimethylacetamide and phenol [18]; septa often release carbon disulfide and sometimes other compounds like 3-methyl-pentane; breath samples could be also contaminated by plasticizer VOC of tubings and valves (e.g. 2,2,4-trimethyl-1,3pentanediol diisobutyrate [CAS: 6846-50-0] or pentanoic acid, 2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester) [17,19]. Eventually, the concentrations of VOC in inhaled air (e.g. that originating from room air or ventilation systems) should be determined as the presence of substances at high concentrations may influence the concentrations of endogenous compounds. In particular, if concentrations increase, the correlations between blood and breath levels will be decreased [19]. There are two viable solutions to such problems: 1. Pure air breathing. By this method, subjects breathe pure air for a

fixed time before measurements [20]. This approach is more theoretical than actual, since it is quite impossible to eliminate VOC from air previously inspired. Moreover several exogenous compounds absorbed from the organism require several hours or days for a complete washout from the body [21]. 2. Subtraction of the air background from the breath signal. In this method environmental air and exhaled breath are collected. Both of them are analyzed and the VOC concentration in alveolar air is given from the ‘alveolar gradient’. Preconcentration of the sample is a frequently used technique to increase the concentration of relevant compounds. For this scope the exhaled breath is absorbed into a solid phase material and then, in order to be analyzed, it is desorbed at high temperature. Sorbent materials have to be chosen considering the different parameters that can influence the analysis. Ideal sorbent material should be both ‘strong’ enough to retain sample analytes but also able to efficiently release them during the thermal desorption.

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Furthermore, the sorbent is required to be thermally stable and chemically inert. Finally, it should also be hydrophobic, in order to be independent from the changes of humidity [22,23]. Sorbent materials generally used in breath analysis provide good thermal stability. Porous polymers like Tenax11 are stable up to 350  C while 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 volatile compounds while sorbents. The latter group of compounds is better captured by carbon molecular sieves based materials. Sorbent tubes are also useful to store and deliver collected breath samples. For instance, Tenax filled sorbent tubes can retain asthma related VOC up to two weeks [24]. The above mentioned sampling techniques are based on the accumulation of the volatile compounds collected in multiple breaths. Interestingly, some analytical techniques such as proton transfer reaction mass-spectrometry (PTR-MS) [25] and selected flow tube ion mass-spectrometry (SIFT-MS) [26] have been demonstrated to be sufficently sensitive to measure the exhaled volatiles in a single breath allowing for a real-time monitoring of the breath composition. An alternative to direct breath sampling is the exhaled breath condensate (EBC) [27]. EBC is produced by cooling the exhaled breath by means of a Peltier cell. The resulting fluid mainly contains water, VOC solubles in water, and a number of nonvolatile compounds such as low-molecular weight metabolites, eicosanoids, hydrogen peroxide, leukotrienes, isoprostanes, cytokines and prostaglandines [28,29]. The sampling of EBC is still non-invasive: subjects inspire through a mouthpiece connected to a non re-breathing valve. Breath flows into the collection device that is immersed in a cooling cuff, where the breath condensates in an appropriate vial [30]. Reference analytical techniques including NMR spectroscopy and MS have been applied to EBC analysis [31–34]. Finally, it is important to consider the influence of life-styles and food intake in the breath composition. In practical studies, the subjects are conditioned to follow a common dietary and hygenic procedure. However these protocols are rather empiric because a quantitative description of the decay in breath of foods and other additives is largely unknown. Some researches point out that particular lifestyles can permanently influence the breath composition. Besides the obvious cases, e.g. tobacco consumption, breath composition alterations have been found in subjects following particular diets such as a gluten-free diet [35]. Spices rich diets (like turmeric dietary in Indian cuisine), besides providing an obvious smells has also been found to increase the level of hydrogen in breath [36]. 3. Selective sensors Only a restricted number of VOC or gases is known to be correlated with the presence of specific diseases. These compounds can be actually considered as disease markers, and their detection requires selective sensors. Possible confounding compounds are those present in breath. Particularly important are the major breath components whose oscillation in concentration can easily hide the target molecules. Among them, an insidious compound is water. With respect to other applications, water vapour in breath analysis is obviously quite large (RH  80%), but it is also rather stable. Then besides using sensors totally insensitive to water vapour it is possible to exploit the fact that the situation of zero sensitivity is also achieved in saturation conditions. This condition can be reached by saturating the breath sample with water vapour. However, it is straightforward that a sensor surface saturated with water molecules hardly can preserve the binding conditions for the target molecules. For this reason, sensors for

breath analysis are often matched with some method for water vapour rejection. Carbon dioxide is another major component of breath that can interfere with the measurement of disease related compound. CO2 can be adequately monitored with pH indicators embedded in suitable membranes [37,38]. In the following section some recent case studies relating to the most investigated VOC and gases are illustrated. A more extended list of sensors focused on the detection of specific gases in breath is provided in Table 1. The table is organized according to the target volatile compound. For each item the table indicates the sensor technology and the sensitive material, if the sensor has been tested on human samples, the limit of detection and if the sensor necessitates of particular sample treatment to improve the detection. 3.1. Nitric oxide Fractional exhaled nitric oxide (FENO) is associated to the presence of inflammatory conditions of the airways, like asthma, and bronchiectasis among the others [39]. A concentration below 25 ppb is considered normal, and a concentration above 50 ppb reveals an airway inflammation [40]. FENO measurement is a noninvasive, standardized, and validated technique for assessing airway inflammation in patients with asthma [41] with or without nasal polyposis [42]. FENO is also allegedely correlated with other diseases of the respiratory tract and in particular with chronic obstructive pulmonary disease (COPD) [43]. Currently used methods to evaluate NO in breath are mainly based on the chemiluminescent reaction with NO of either ozone or luminol [44]. These instruments can reach limits of detection of the order of 2 ppb with a resolution of 1 ppb. A number of commercial, in many cases even portable, FENO detectors are currently available and their extensive use provided more evidences to the supposed relationship between the concentration of FENO and asthma. However, the level of nitric oxide does not univocally diagnose asthma. As shown in Fig. 1 FENO is rather constant among healthy subject but a large dispersion is observed among asthma patients. The averages between the two groups are different but the two distributions are somewhat overlapped. Eventually, FENO is currently the easiest to measure and the only clinically approved surrogate marker of airway inflammation. It is useful to monitor the evolution of asthma rather than to diagnose the disease and for assessing the effect of pharmacological treatment and treatment withdrawal in patients with asthma [45]. Ozone-based chemiluminescence detectors are approved by the US Food and Drug Administration to monitor the inflammation processes in asthma patients [46]. However, besides the complexity of the measurement principles these detectors are affected by some drawbacks such as the necessity of frequent calibrations, the technical maintenance, the generation and destruction of ozone, and the use of high voltage. For these reasons, a generation of alternative sensors is devised to make more affordable and easy the monitoring of airways inflammatory diseases. The detection of FENO rises a twofold sets of problems, the first is the large sensitivity and the low noise that are necessary to reach the ppb detection limit; the second is obviously the selectivity. Chemoresistors made of metal-oxide semiconductors are good candidates to reach the necessary sensitivity but their selectivity is rather limited. However, materials formed by mixtures of n- and ptype oxides (such as WO3 and Cr2O3) shown interesting performance being able to detect NO down to 18 ppb and maintaining the detection even in the presence of 20 ppm of carbon monoxide [47]. The removal of CO2 from the sample allowed a chemoresistor made of chemically functionalized carbon nanotube to achieve a detection limit of 5 ppb [48].

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Table 1 Specific sensors. Primary target

Sensor technology

Sensing materials

Human tests

Limit of detection

Sample treatment

Reference

Volatile sulphur compounds Volatile sulphur compounds Volatile sulphur compounds Acetone Acetone Acetone Acetone Acetone

Colorimetry

Iodine

Yes

0.05 mg L1 of H2S

No

[89]

Chemiresistor

Gold nanoparticles decorated polyaniline Monoamine oxidase A and optical oxygen sensor Si:WO3 Chitosan In2O3 and Pt–In2O3 Hemitubes of Pt–WO3 Cavity ringdown spectroscopy

No

1 mM of H2S and CH3SH No

[88]

Yes

200 ppb

No

[90]

Yes No No No Yes

20 ppb 0.1 ppm 80%

[169]

Pneumonia

400 patienrt in intensive care units

Ventilator associated pneumonia Pulmonary arterial hypertension Pulmonary sarcoidosis

44 patients sample: bronchoalveolar fluid 22 diseases, 23 control

Regression of clinical pneumonia [170] score R2 = 0.81 77% [171]

Bacterial infections Tuberculosis

[12]

31 patients, 25 controls

83%

[166]

96 patients in intensive care unit 80 tuberculosis, 243 negative sample: sputum 177 patients

98% 70%

[167] [173]

NA

[176] 13

Various cancers (lung, breast, colorectal, prostate)

>80%

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Sensor array technology

14

Table2 (Continued) Sensing materials

Classification tool

Target disease

Recruited people

Classification rate

Reference

Chemoresistors

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

Organically functionalized Au and Pt nanoparticles Organically functionalized Au and Pt nanoparticles Metal oxide semiconductors

Diabetes,

192 diabetics in 4 classes according to blood glucose 68% value

[186]

Chemoresistors

Conducting polymers

Principal component analysis, support vector ordinal regression [196] Principal component analysis

6 quartz microbalance

Peptides

Discriminant analysis

Chemoresistors

Chemoresistors

Organically functionalized Au nanoparticles Metal-oxide semiconductor Organically functionalized Au and Pt nanoparticles, functionalized CNT Polycyclic aromatic hydrocarbon functionalized CNT Organically functionalized Au and Pt nanoparticles Carbon black – polymers composites Metral oxide semiconductors

Quartz microbalance Quartz microbalance Chemoresistors Chemoresistors

Chemoresistors

Chemoresistors Chemoresistors

Chemoresistors Chemoresistors Chemoresistors

Diabetes

Support vector machine

3 patients 3 controls 61 CRI/CRF Uremia chronic renal insufficiency 83 uremia 30 control (CRI) chronic renal failure (CRF) Chronic kidney disease 62

NA

[187]

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

[180]

>79%

[179]

Principal component analysis Discriminant analysis

Acute liver failure in rats Parkinson disease

14 rats with liver failure, 9 healthy rats 19 rats

94% 95%

[185] [181]

Discriminant analysis

Multiple sclerosis

34 multiple sclerosis, 17 healthy

>80%

[177]

Discriminant analysis

57 patients

>78%

[183]

Discriminant analysis

Alzheimer and Parkinson diseases Pregnancy

130 subjects (78 pregnant)

87%

[184]

Multiple linear regression

Halitosis

49 patients

[190]

Metalloporphyrins Lipids, polar GC phase, cellulose Metal oxide semiconductors

Principal components Principal component analysis Multiple linear regression

Halitosis Oral malodor Oral malodor

Carbon black – polymers composites

Principal components

Physical stress

7 subjects number not available 66 subjects characterized with a sensorial oral malodor score 10 healthy undergoing physical excersises

Regression between odor score and enose signals R2 = 0.41 p < 0.0001 100% Not available Regression coefficient r = 0.81 Correlation between principal components and exhaled pH

[188] [189] [191] [193]

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In both cases metabolism acceleration and oxidative stress can contribute to change exhaled VOCs and a first identification of such alterations 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 is given in Table 2. For each application the table indicates the sensor technology and the sensing material, the targeted disease, the size of the sample and finally the algorithm used for the classification and the resulting classification rate. The classification rate is expressed as the rate of successful identification of the disease. In few cases the electronic nose data have been used in a regression model to estimate a disease related score. In almost all these papers linear algorithms (such as discriminant analysis, PCA, and PLS) have been utilized to identify the disease. Almost all the listed applications were tested on human breath collected from diseased and healthy individuals. In some applications the electronic noses have been tested on animal models or on artificial breath obtained mixing the compounds expected to be relevant for the target disease. One of the major drawback of electronic noses is the sometimes excessive dependence of the class membership estimation on the total composition of the sample. In practice, fluctuations of compounds unrelated to the targeted disease but sensed by the electronic nose sensors can easily confound the result. This risk is particularly important in breath analysis where the background composition of the breath is variable both within-day and between-day due to different activities and conditions (from the trivial consequence of food uptake to alterations in the ambient air). The actual influence of background fluctuations has to be evaluated for each separated case; however some recent studies evidence that in case of COPD both a porphyrin coated QMB sensor array [194] and a Cyranonose [147] are not affected by changes in the background composition of the breath. A final remark about the role of sensor arrays in breath analysis could be done considering the case of the organically capped NP even if it can be applied to other sensor technologies. These sensors have been demonstrated to be almost universal being able to discriminate different cancers, renal failures and also neurological diseases always with respect to a healthy control population. It is hardly to believe that electronic noses can detect the presence of a particular disease in a randomly selected individual when different diseases, of different gravity but each affecting the breath, are present at once. An interesting change of paradigm with respect to the current literature could be introduced considering the changes occurring in a single subject during the transition from a “healthy status” towards pathology. Indeed, one of the most powerful application of sensors, expected to be small easy to use and low cost, is their personal use as a sort of extension of the natural senses to probe the own body. This could provide a sort of increase of sensitivity about the changes occurring in the body enabling a more preventive detection of diseases. 5. Conclusions The relationship between VOC patterns in breath and some diseases is now quite clearly ascertained but several practical problems need to be addressed in order to develop routinely usable devices. The open problems involve the breath sampling and the sensors features. Standardized methods of breath sampling are necessary to make repeatable and comparable the analysis performed on different individuals and in different locations. Issues such as the

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composition and the temperature of the inhaled air and the separation of the breath in dead-space and alveolar air are examples of the complexity of the problem. Another often underrated element is the preparation of the subject in terms of diet and life-style in the hours “immediately” before the analysis. It is worth to consider that the term immediately before is still rather undefined. A clear quantitative description of the decay of food intake effect on breath composition is still not available. In terms of sensors, there is the necessity of selective sensors for few well recognized markers and for these sensors the main problem is just the rejection of confounding components, water vapour above the others. Sensor arrays are the viable approach in case of VOCs pattern alteration rather than a restricted and well-known number of VOC. This is a rather common case in many diseases and in particular in cancer where the metabolic changes of tumor cells result in a large variation in terms of quantity and quality of the emitted compounds. Being not related to a single compound sensor arrays are more subject to the influence of confounding parameters such as comorbidities and other independent pathologies. Even for sensors as array elements the main issues are the sensitivity and the selectivity in terms of orthogonality of the sensor signals. To this regard, an interesting method to increase the current performance could be the integration of different sensors technologies in order to increase the amount of captured compounds and the orthogonality of the data of the array. References [1] M. Smith, The Lancet 25 (1982) 1452. [2] L. Pauling, A. Robinson, R. Teranishi, P. Cary, Proceedings of the National Academy of Sciences of the United States of America 68 (1971) 2374–2376. [3] W. Miekisch, J. Schubert, G. Noeldge-Schomburg, Clinica Chimica Acta 347 (2007) 25–39. [4] M. Shirasu, K. Tohuara, The Journal of Biochemistry 150 (2011) 257–266. [5] J. Dummer, M. Storer, M. Swanney, M. McEwan, A. Scott-Thomas, S. Bhandari, S. Chambers, R. Dweik, M. Epton, Trends in Analytical Chemistry 30 (2011) 960–967. [6] H. Haick, Y. Broza, P. Mochalski, V. Ruszanyi, A. Amann, Chemical Society Reviews (2014), doi:10.1039/c3cs60329f. [7] M. Barreto, M.P. Villa, C. Olita, S. Martella, G. Ciabattoni, P. Montuschi, Chest 135 (2009) 66. [8] V. Lucidi, G. Ciabattoni, S. BElla, P.J. Barnes, P. Montuschi, Free Radical Biology & Medicine 45 (2008) 913. [9] P. Montuschi, P.J. Barnes, G. Ciabattoni, Methods in Molecular Biology 594 (2010) 73. [10] P. Mochalski, J. King, A. Kupferthaler, K. Unterkofler, H. Hinterhuber, A. Amann, International Journal of Toxicology 31 (3) (2012) 267–275. [11] P. Mochalski, J. King, A. Kupferthaler, K. Unterkofler, H. Hinterhuber, A. Amann, Journal of Breath Research 5 (2011) 46010. [12] S. Cohen-Kaminsky, M. Nakhleh, F. perros, D. Montani, B. Girerd, G. Garcia, G. Somonneau, H. Haick, M. Humbert, American Journal of Respiratory and Critical Care Medicine 188 (2013) 756–759. [13] W. Filipiak, V. Ruzsanyi, P. Mochalski, A. Filipiak, A. Bajtarevic, C. Ager, H. Denz, W. Hilbe, H. Jamnig, M. Hackl, A. Dzien, A. Amann, Journal of Breath Research 6 (2012) 036008. [14] B. de Lacy Costello, A. Amann, H. Al-Kateb, C. Flynn, W. Filipiak, N. Ratcliffe, Journal of Breath Research 8 (2014) 014001. [15] W. Miekisch, S. Kischkel, A. Sawacki, T. Liebau, M. Mieth, K. Schubert, Journal of Breath Research 2 (2008) 026007. [16] T. Birken, I. Schubert, W. Miekisch, Technology and Health Care 14 (2006) 499–506. [17] A. Amann, W. Miekisch, J. Pleil, European Respiratory Monograph 49 (2010) 96–114.  [18] P. Mochalskia, B. Wzorek, I. Sliwka, A. Amann, Journal of Chromatography B 877 (2009) 189–196. [19] S. Sehnert, L. Jiang, J. Burdick, T. Risby, Biomarkers 7 (2002) 174–187. [20] T. Risby, S. Sehnert, Free Radical Biology & Medicine 27 (1999) 1182–1192. [21] T. Risby, S. Solga, Applied Physics B 85 (2006) 421–426. [22] A. Sunesson, C. Nilsson, B. Andersson, Journal of Chromatography A 699 (1995) 203–214. [23] E. Woolfenden, Journal of Chromatography A 1217 (2010) 2685–2694. [24] M.P. Van Der Schee, N. Fens, P. Brinkman, L.D.J. Bos, M.D. Angelo, T.M.E. Nijsen, R. Raabe, H.H. Knobel, T.J. Vink, P.J. Sterk, Journal of Breath Research 7 (2013) 016002.

16

C. Di Natale et al. / Analytica Chimica Acta 824 (2014) 1–17

[25] J. Herbig, M. Muller, S. Schallart, T. Titzmann, M. Graus, A. Hansel, Journal of Breath Research 3 (2009) 027004. [26] A. Diskin, P. Spanel, D. Smith, Physiological Measurement 24 (2003) 107. [27] I. Horvath, J. Hunt, P. Barnes, K. Alving, European Respiratory Journal 26 (2005) 523–548. [28] G. Mutlu, K. Garey, R. Robbins, L. Danziger, I. Rubinstein, American Journal of Respiratory and Critical Care Medicine 164 (2001) 731–737. [29] M. Malerba, P. Montuschi, Current Medicinal Chemistry 19 (2012) 187. [30] P. Montuschi, P.J. Barnes, Trends of Pharmacological Science 23 (2002) 232– 237. [31] C. Carpenter, P. Price, B. Christman, Chest 114 (1998) 1653–1659. [32] P. Montuschi, Journal of Chromatography B, Analytical Technologies in the Biomedical and Life Sciences 877 (2009) 1272–1280. [33] A. Motta, D. Paris, D. Melck, G. de Laurentiis, M. Maniscalco, M. Sofia, P. Montuschi, European Respiratory Journal 39 (2012) 498–500. [34] G. de Laurentis, D. Paris, D. Melck, P. Montuschi, A. Bianco, M. Sofia, A. Motta, Journal of Proteome Research 12 (2013) 1502. [35] A. Baranska, E. Tigchelaar, A. Smolinska, J. Dallinga, E. Moonen, J. Deckens, C. Wijmenga, A. Zhernakova, F. Van Schooten, Journal of Breath Research 7 (2013) 037104. [36] A. Shimouchi, K. Nose, M. Takaoka, H. Hayashi, T. Kondo, Digestive Disease and Sciences 54 (2009) 1725–1729. [37] A. Mills, A. Lepre, L. Wild, Sensors and Actuators B 38 (1997) 419–425. [38] J. Fernandez-Sanchez, R. Cannas, S. Spichiger, R. Steiger, U. Spichiger-Keller, Sensors and Actuators B 128 (2007) 145–153. [39] S. Kharitonov, P. Barnes, Chest 130 (2006) 1541e6. [40] I.D. Pavord, D.E. Shaw, P.G. Gibson, D.R. Taylor, The Lancet 372 (2008) 1017. [41] R. Dweik, P. Boggs, S. Erzurum, C. Irvin, M. Leigh, J. Lundberg, A. Olin, A. Plummer, D. Taylor, American Journal of Respiratory and Critical Care Medicine 184 (2011) 602–615. [42] J. Galli, P. Montuschi, G. Passali, M. Laruffa, Otolaryngology Head and Neck Surgery 147 (2012) 351–356. [43] A. Gelb, P. Barnes, S. George, F. Ricciardolo, G. DiMaria, N. Zamel, Journal of Breath Research 6 (2012) 047101. [44] J. Robinson, M. Bollinger, J. Birks, Analytical Chemistry 71 (1999) 5131–5136. [45] P. Montuschi, C. Mondino, P. Koch, G. Ciabattoni, P. Barnes, G. Baviera, Chest 132 (2007) 1876–1881. [46] P. Silkoff, M. Carlson, T. Bourke, R. Katial, E. Ogren, S. Szefler, Journal of Allergy and Clinical Immunology 114 (2004) 1241–1256. [47] C. Sun, G. Maduraiveeran, P. Dutta, Sensors and Actuators B 186 (2013) 117– 125. [48] O. Kuzmych, B. Allen, A. Star, Nanotechnology 18 (2007) 37502. [49] D. Menzies, A. Nair, B. Lipworth, Chest 131 (2007) 410–414. [50] S. Prakash Mondal, P. Dutta, G. Hunter, B. Ward, D. Laskowsi, Sensors and Actuators B 158 (2011) 292–298. [51] S. Pantalei, E. Zampetti, A. Bearzotti, F. De Cesare, A. Macagnano, Sensors and Actuators B 179 (2013) 87–94. [52] C. Roller, K. Namjou, J.D. Jeffers, M. Camp, A. Mock, P.J. McCann, J. Grego, Applied Optics 41 (2002) 6018. [53] K. Namjou, C. Roller, T. Reich, J. Jeffers, G. McMillen, P. McCann, M. Camp, Applied Physics B 85 (2006) 427–435. [54] M. McCurdy, Y. Bakhirkin, F. Tittel, Applied Physics B 85 (2006) 445–452. [55] C.H. Deng, J. Zhang, X.F. Yu, W. Zhang, X.M. Zhang, Journal of Chromatography B 810 (2004) 269–275. [56] M. Righettoni, A. Tricoli, S. Pratsinis, Chemistry of Materials 22 (2010) 3152– 3157. [57] K. Schwarz, W. Filipiak, A. Amann, Journal of Breath Research 3 (2009) 027002. [58] M. Righettoni, A. Schmid, A. Amann, S. Pratsinis, Journal of Breath Research 7 (2013) 037110. [59] J. King, A. Kupferthaler, K. Unterkofler, H. Koc, S. Teschl, G. Teschl, W. Miekisch, J. Schubert, H. Hinterhuber, A. Amann, Journal of Breath Research 3 (2009) 027006. [60] J.K. Schubert, W.P.E. Muller, A. Benzing, K. Geiger, Intensive Care Medicine 24 (1998) 415–421. [61] L.T. McGrath, R. Patrick, B. Silke, European Journal of Heart Failure 3 (2001) 423–427. [62] A. Bajtarevic, C. Ager, M. Pienz, M. Klieber, K. Schwarz, M. Ligor, T. Ligor, W. Filipiak, H. Denz, M. Fiegl, W. Hilbe, W. Weiss, P. Lukas, H. Jamnig, M. Hackl, A. Haidenberger, B. Buszewski, W. Miekisch, J.A. Schubert. Amann, BMC Cancer 9 (2009) 348. [63] I. Kohl, J. Dunkl, J. Herbig, M. Hubalek, H. Fiegl, M. Daniaux, Gynäkologische Onkologie 6 (2012) 21. [64] D. Fuchs, H. Jamnig, P. Heininger, M. Klieber, S. Schroecksnadel, M. Fiegl, M. Hackl, H. Denz, A. Amann, Journal of Breath Research 6 (2012) 027101. [65] J. King, P. Mochalski, A. Kupferthaler, K. Unetrkofler, H. Koc, W. Filipiak, S. Teschl, H. Hinterhuber, A. Amann, Physiological Measurement 31 (2010) 1169–1184. [66] J. King, H. Koc, K. Unterkofler, G. Teschl, S. Teschl, P. Mochalski, M. Hinterhuber, A. Amann, Volatile Biomarkers: Non-invasive Diagnosis in Physiology and Medicine, in: A. Amann, D. Smith (Eds.), Elsevier, Amsterdam, 2013, pp. 27–46. [67] S. Choi, I. lee, B. Jang, W. Ryu, C. Park, I. Kim, Analytical Chemistry 85 (2012) 1792–1796. [68] J. Shin, S. Choi, D. Youn, I. Kim, Journal of Electroceramics 29 (2012) 106–116. [69] G. Neri, A. Bonavita, G. Micali, N. Donato, IEEE Sensors Journal 10 (2010) 131–136.

[70] J. Shin, S. Choi, I. Lee, D. Youn, C. Park, J. Lee, H. Tuller, I. Kim, Advanced Functional Materials 23 (2013) 2357–2367. [71] D. Poli, P. Carbognani, M. Corradi, M. Goldoni, O. Acampa, B. Balbi, L. Bianchi, M. Rusca, A. Mutti, Respiratory Research 6 (2005) 71. [72] M. Righettoni, A. Tricoli, S. Gass, A. Schmid, A. Amann, S. Pratsinis, Analytica Chimica Acta 738 (2012) 69–75. [73] T. Nasutionet, I. Nainggolan, S. Hutagalung, K. Ahmad, Z. Ahmad, Sensors and Actuators B 177 (2013) 522–528. [74] A. Worrall, J. Bernstein, A. Angelopoulos, Talanta 112 (2013) 26–30. [75] C. Wang, A. Mbi, M. Sheperd, IEEE Sensors Journal 10 (2010) 54–63. [76] S. DuBois, S. Eng, R. Bhattacharya, S. Rulyak, T. Hubbard, D. Putnam, D. Kearney, Digestive Diseases and Sciences 50 (2005) 1780–1784. [77] D. Kearney, T. Hubbard, D. Putnam, Digestive Diseases and Sciences 47 (2002) 2523–2530. [78] U. Lachish, S. Rotter, E. Adler, U. El-Hanany, Review of Scientific Instruments 58 (1987) 923–927. [79] J. Manne, O. Sukhorukov, W. Jager, J. Tulip, Applied Optics 45 (2006) 9230– 9237. [80] N. Amornthammarong, J. Jakmunee, J. Li, P. Dasgupta, Analytical Chemistry 77 (2005) 1890–1896. [81] K. Toda, J. Li, P. Dasgupta, Analytical Chemistry 78 (2006) 7284–7291. [82] H. Ishida, T. Satou, K. Tsuji, N. Kawashima, H. Takemura, Y. Kosaki, S. Shiratori, T. Aglshi, Bio-Medical Materials and Engineering 18 (2008) 99–106. [83] P. Gouma, K. Kalyanasundaram, X. Yun, IEEE Sensors Journal 10 (2010) 49–53. [84] S. Chen, L. Zieve, V. Mahadevan, Journal of Laboratory and Clinical Medicine 75 (1970) 628–635. [85] S. Studer, J. Orens, I. Rosas, J. Krishnan, K. Cope, S. Yang, Journal of Heart and Lung Transplantation 20 (2001) 1158–1166. [86] J. Tonzetich, P. Carpenter, Archives of Oral Biology 16 (1971) 599–607. [87] J. Tonzetich, K. Yaegaki, J.M. Coil, Journal of Dental Research 64 (1986) 786. [88] C. Liu, K. Hayashi, K. Toko, Sensors and Actuators B 161 (2002) 504–509. [89] N. Alagirisamy, S. Hardas, S. Jayaraman, Analytica Chimica Acta 661 (2010) 97–102. [90] K. Mitsubayashi, T. Minamide, K. Otsuka, H. Kudo, H. Saito, Analytica Chimica Acta 573 (2006) 75–80. [91] M. Corradi, P. Gergelova, A. Mutti, European Respiratory Monograph 49 (2010) 140–151. [92] D. Smith, P. Spanel, Analyst 120 (2007) 390–396. [93] V. Skrupskii, Clinical Laboratory Diagnostics 4 (1995) 35–38. [94] W. Cao, Y. Duan, Critical Reviews in Analytical Chemistry 37 (2007) 3. [95] K. Mitsubayashi, H. Matsunaga, G. Nishio, S. Toda, Y. Nakanishi, H. Saito, M. Ogawa, K. Otsuka, Sensors and Actuators B 108 (2005) 660–664. [96] K. Mitsubayashi, H. Matsunaga, G. Nishio, S. Toda, Y. Nakanishi, Biosensors and Bioelectronics 20 (2005) 1573. [97] B. Hok, A. Bluckert, G. Sandberg, Sensors and Actuators A 52 (1996) 81–85. [98] A. Tetelin, C. Pellet, C. Laville, G. N’Kaoua, Sensors and Actuators B 91 (2003) 211–218. [99] H. Liu, W. Huang, Y. Chen, C. Lu, J. Huang, IEEE Sensors Journal 13 (2013) 2737– 2741. [100] F. Hoffmeyer, M. Raulf-Heimsoth, T. Bruening, Current Opinion in Allergy and Clinical Immunology 9 (2009) 16–22. [101] New Perspectives in Monitoring Lung Inflammation: Analysis of Exhaled Breath Condensate, in: P. Montuschi (Ed.), CRC Press, Boca Raton, FL, USA, 2005. [102] P. Fabian, J. McDevitt, W. DeHaan, R. Fung, B. Cowling, K. Chan, G. Leung, D. Milton, PLoS One 3 (2008) e2691. [103] P. Tam, N. Hie, N. Chien, A. Le, M. Tuan, Journal of Immunological Methods 350 (2009) 118–124. [104] D. Li, J. Wang, R. Wang, Y. Li, D. Abi-Ghanem, L. Berghman, B. Hargis, H. Lu, Biosensors and Bioelectronics 26 (2011) 4146–4154. [105] E. Stern, J. Klemic, D. Routenberg, P. Wyrembak, D. Turner-Evans, A. Hamilton, D. LaVan, T. Fahmy, M. Reed, Nature 445 (2007) 519–522. [106] J. Shen, Z. Wang, Y. Xu, Q. Wu, X. Chen, X. Li, L. Ji, M. Li, M. Yao, X. Guo, T. Zhu, Nano Letters 12 (2012) 3722–3730. [107] F. Shen, J. Wang, Z. Xu, Y. Wu, Q. Chen, X. Li, X. Jie, L. Li, M. Yao, X. Guo, T. Zhu, Nano Letters 12 (2012) 3722–3730. [108] J. Driskell, C. Jones, M. Tompkins, R. Tripp, Analyst 136 (2011) 3083. [109] E. McFarland, A. Yup, S. Joshi, S. Lovell, J. Stahler, C. Finnerty, B. Pope, J. Newman, V. Crews, In 21st Annual Clinical Virology Symposium, May 8–11 Clearwater (FL, USA), 2005). [110] A. Bikov, B. Antus, G. Losonczy, I. Horvath, European Respiratory Monograph 49 (2010) 173–182. [111] B. Kang, H. Wang, F. Ren, B. Gila, C. Abernathy, S. Pearton, D. Dennis, J. Johnson, P. Rajagopal, J. Roberts, E. Piner, K. Linthicum, Electrochemical and Solid-State Letters 11 (2008) J19–J21. [112] B. Chu, B. Kang, C. Chang, F. Ren, IEEE Sensors Journal 10 (2010) 64–70. [113] E. Marek, J. Volke, I. Hwener, P. Platen, K. Muchenhoff, W. Marek, Journal of Breath Research 4 (2010) 017105. [114] S. Kharitonov, P. Barnes, American Journal of Respiratory and Critical Care Medicine 163 (2001) 1693–1722. [115] P. Montuschi, Clinica Chimica Acta 356 (2005) 22–34. [116] E. Marek, K. Muckenhoff, H. Streckert, G. Becher, W. Marek, Pneumologie 62 (2008) 541–547. [117] D. Anh, W. Olthuis, P. Bergveld, Sensors and Actuators B 111 (2005) 494–499. [118] J. Wiedemair, H. Van Dorp, W. Olthuis, A. Van der Berg, Electrophoresis 33 (2012) 3181–3196.

C. Di Natale et al. / Analytica Chimica Acta 824 (2014) 1–17 [119] M. Komkova, E. Karyakina, F. Marken, A. Karyakin, Analytical Chemistry 85 (2013) 2574–2577. [120] S. Zaromb, J. Stetter, Sensors and Actuators 6 (1984) 225–244. [121] X. Wong, S. Yee, P. Carey, Sensors and Actuators B 20 (1994) 217–224. [122] L. Buck, R. Axel, Cell 65 (1991) 175–187. [123] G. Sicard, A. Holley, Brain Research 292 (1984) 283–291. [124] E. Hallem, M. Ho, J. Carlson, Cell 125 (2006) 143–160. [125] B. Malnic, J. Hirono, T. Sato, L. Buck, Cell 96 (1999) 713–723. [126] K. Persaud, G. Dodds, Nature 299 (1982) 352–353. [127] A. Turner, N. Magan, Nature Reviews Microbiology 2 (2004) 161–166. [128] C. Wheelock, V. Goss, D. Balgoma, B. Nicholas, J. Brandsma, P. Skipp, S. Snowden, D. Burg, A. D‘Amico, I. Horvath, A. Chaiboonchoe, H. Ahmed, S. Ballerau, C. Rossios, K. Chung, P. Montuschi, S. Fowler, A. Adcock, A. Postle, S. Dahlen, A. Rowe, P. Sterk, C. Auffray, R. Djukanovic, European Respiratory Journal 42 (2013) 802–825. [129] P. Jurs, G. Bakken, H. McClelland, Chemical Reviews 100 (2000) 2649–2678. [130] A. Hierlemann, R. Gutierrez-Osuna, Chemical Reviews 108 (2008) 563–613. [131] S. Marco, A. Gutierrez-Galvez, IEEE Sensors Journal 12 (2012) 3189–3214. [132] G.G. Preti, J. Labows, J. Kostelic, S. Aldinger, R. Daniele, Journal of Chromatography: Biomedical Applications 432 (1988) 1–11. [133] M. Phillips, K. Gleeson, J. Hughes, J. Greenberg, R. Cataneo, L. Baker, W. McVay, The Lancet 353 (1999) 1930–1935. [134] M. Phillips, N. Altford, J. Austin, R. Cameron, R. Kloss, M. Maxfield, A. Rashid, W. Rom, P. Schmitt, J. Wai, Clinica Chimica Acta 393 (2008) 76–84. [135] G. Peng, U. Tisch, O. Adams, M. Hakim, N. Shehada, Y. Broza, S. Billan, R. Abdah-Bortnyak, A. Kuten, H. Haick, Nature Nanotechnology 4 (2009) 669– 673. [136] G. Pennazza, M. Santonico, E. Martinelli, A. D’Amico, C. Di Natale, European Respiratory Monograph 49 (2010) 115–129. [137] M. Hakim, Y. Broza, O. Barash, N. Peled, M. Phillips, A. Amann, H. Haick, Chemical Reviews 112 (2012) 5949–5966. [138] C. Di Natale, A. Macagnano, E. Martinelli, R. Paolesse, G. D’Arcangelo, C. Roscioni, A. Finazzi-Agrò, A. D’Amico, Biosensors and Bioelectronics 18 (2003) 1209–1218. [139] C. Di Natale, R. Paolesse, A. D’Amico, Sensors and Actuators B 121 (2007) 238– 246. [140] A. D’Amico, G. Pennazza, M. Santonico, E. Martinelli, C. Roscioni, G. Galluccio, R. Paolesse, C. Di Natale, Lung Cancer 68 (2010) 170–176. [141] M. Santonico, G. Lucantoni, G. Pennazza, R. Capuano, G. Galluccio, C. Roscioni, G. La Delfa, D. Consoli, E. Martinelli, R. Paolesse, C. Di Natale, A. D’Amico, Lung Cancer 77 (2012) 46–50. [142] O. Barash, N. Peled, F. Hirsch, H. Haick, Small 5 (2009) 2618–2624. [143] J. Haick, Journal of Physics D: Applied Physics 40 (2007) 7173–7186. [144] M. Abrahams, J. Grate, Sensors and Actuators B 3 (1991) 85. [145] Y. Broza, R. Kremer, U. Tisch, A. Gevorkan, A. Shiban, L. Best, H. Haick, Nanomedicine: Nanotechnology, Biology and Medicine 9 (2013) 15–21. [146] M. Lonergan, E. Severin, B. Doleman, S. Beaber, R. Grubbs, N. Lewis, Chemistry of Materials 8 (1996) 2298–2312. [147] M. Bofan, N. Mores, M. BAron, M. Dabrowska, S. Valente, M. Schmid, A. Trovè, S. Conforto, G. Zini, P. Cattani, L. Fuso, A. Mautone, C. Mondino, G. Pagliari, T. D‘Alessio, P. Montuschi, Journal of Breath Research 7 (2013) 017103. [148] P. Montuschi, P.J. Barnes, Drug Discovery Today 16 (2011) 1084–1091. [149] P. Montuschi, Frontiers in Pharmacology 1 (2010) 131. [150] R. Machado, D. Laskowski, O. Deffenderfer, T. Burch, S. Zheng, P. Mazzone, T. Mekhail, C. Jennings, J. Stoller, J. Pyle, J. Duncan, E. Dweik, S. Erzurum, American Journal of Respiratory and Critical Care Medicine 171 (2005) 1286– 1291. [151] S. Dragonieri, J. Annema, R. Schot, M. Van der Schee, A. Spanevello, P. Carratù, O. Reesta, K. Rabe, P. Sterk, Lung Cancer 64 (2009) 166–170. [152] S. Dragonieri, M. Van der Schee, T. Massaro, N. Schiavulli, P. Brinkman, A. Pinca, P. Carratù, A. Spanevello, O. Resta, M. Musti, P. Sterk, Lung Cancer 326 (2012) 331. [153] N. Rakow, K. Suslick, Nature 406 (2000) 710–713. [154] P. Mazzone, J. Hammel, R. Dweik, J. Na, C. Czich, D. Laskowski, T. Mekhail, Thorax 62 (2007) 565–568. [155] P. Mazzone, X. Wang, Y. Xu, T. Mekhail, M. Beukemann, J. Na, J. Meling, K. Suslick, M. Sasidhar, Journal of Thoracic Oncology 7 (2012) 137–142. [156] X. Chen, M. Cao, Y. Li, W. Hu, P. Wang, K. Ying, H. Pan, Measurement Science and Technology 16 (2005) 1535–1546. [157] H. Ulmer, J. Mitrovics, U. Weimar, W. Göpel, Sensors and Actuators B 65 (2000) 79–81. [158] C. Di Natale, G. Olafsdottir, S. Einarsson, E. Martinelli, R. Paolesse, A. D‘Amico, Sensors and Actuators B 77 (2001) 572–578. [159] M. Santonico, G. Pennazza, R. Capuano, C. Falconi, T. Vink, H. Knobel, M. Van der Schee, P. Sterk, P. Montuschi, A. D‘Amico, Sensors and Actuators B 173 (2012) 555–561. [160] P. Montuschi, M. Santonico, C. Mondino, G. Pennazza, G. Mantini, E. Martinelli, R. Capuano, G. Ciabattoni, R. Paolesse, C. Di Natale, P. Barnes, A. D‘Amico, Chest 137 (2010) 790–796.

17

[161] N. Fens, A. Zwindermann, M. Van der Schee, S. de Nijs, E. Dijkers, A. Roldaan, D. Cheung, E. Bel, P. Sterk, American Journal of Respiratory and Critical Care Medicine 180 (2009) 1076–1082. [162] S. Dragonieri, R. SChot, B. Mertens, S. Le Cessie, S. Gauw, A. Spanevello, O. Resta, N. Willard, T. Vink, K. Rabe, E. Bel, P. Sterk, Journal of Allergy and Clinical Immunology 120 (2007) 856–862. [163] H. Biller, O. Holz, H. Windt, W. Koch, M. Müller, R. Jörres, N. Krug, A. Hohlfeld, Respiratory Medicine (2011) 1–12. [164] N. Fens, A. Roldaan, M. van der Schee, R. Boksem, A. Zwinderman, E. Bel, P. Sterk, Clinical and Experimental Allergy 41 (2011) 1371–1378. [165] A. Hattesohl, R. Jorres, H. Dressel, S. Schmid, C. Vogelmeier, T. Greulich, S. Noeske, R. Bals, A. Koczulla, Respirology 16 (2011) 1258–1264. [166] S. Dragonieri, P. Brinkman, E. Mouw, A. Zwinderman, P. Carratù, O. Resta, P. Sterk, R. Jonkers, Respiratory Medicine 107 (2013) 1073–1078. [167] C. Shih, Y. Lin, K. Lee, P. Chien, P. Drake, Sensors and Actuators B 148 (2010) 153–157. [168] K. de Heer, M. Van der Schee, K. Zwindermann, I. Van der Berk, C. Visser, R. Van Oers, P. Sterk, Journal of Clinical Microbiology 51 (2013) 1490–1495. [169] N. Hockstein, E. Thaler, D. Torigian, O. Miller, W. Hanson, The Laryngoscope 114 (2004) 1701–1705. [170] W. Hanson, E. Thaler, Anesthesiology 102 (2005) 63–68. [171] L. Humphreys, R. Orme, P. Moore, N. Charaklias, N. Sahgal, M. Pont, N. Magan, N. Stone, C. Kendall, European Journal of Clinical Investigation 41 (2010) 52– 58. [172] M. Phillips, R. Cataneo, R. Condos, G. Ring Erickson, J. Greenberg, V. La Bombardi, M. Munawar, O. Tietje, Tuberculosis 87 (2007) 44–52. [173] A. Kolk, M. Hoelscher, L. Maboko, J. Jonga, S. Kuijeper, M. Cauchi, C. Bessant, S. Va Beers, R. Dutta, T. Gibson, K. Reither, Journal of Clinical Microbiology 48 (2010) 4235–4238. [174] D. Kovacs, A. Bikov, G. Lozonczy, G. Murakozy, I. Horvath, Journal of Breath Research 7 (2013) 017117. [175] M. Phillips, R. Cataneo, B. Ditkoff, P. Fisher, J. Greenberg, R. Gunawardena, S. Kwon, O. Tietje, C. Wong, Breast Cancer Research and Treatment 99 (2006) 19–21. [176] G. Peng, M. Hakim, Y. Broza, S. Billan, R. Abdah-Bortnyak, A. Kuten, U. Tisch, H. Haick, British Journal of Cancer 103 (2010) 542–551. [177] G. Shuster, Z. Gallimadi, A. Reiss, E. Dovgolevsky, S. Billan, R. Abdah-Bortynak, A. Kuten, A. Engel, A. Shiban, U. Tisch, H. Haick, Breast Cancer Research and Treatment 126 (2011) 791–796. [178] Z. Xu, Y. Broza, R. Ionescu, U. Tisch, L. Ding, H. Liu, Q. Song, Y. Pan, F. Xiong, K. Gu, Z. Chen, M. Leja, H. Haick, British Journal of Cancer 108 (2013) 941–950. [179] O. Marom, F. Nakhoul, U. Tisch, A. Shiban, Z. Abassi, H. Haick, Nanomedicine 7 (2012) 639–650. [180] Y. Lin, H. Guo, Y. Chang, M. Kao, H. Wang, R. Hong, Sensors and Actuators B 76 (2001) 177. [181] U. Tisch, Y. Aluf, R. Ionescu, M. Nakleh, R. Bassal, N. Axelroad, D. Robertmann, Y. Tessler, J. Finberg, H. Haick, ACS Chemical Neuroscience 3 (2012) 161–166. [182] R. Ionescu, Y. Broza, H. Shaltieli, D. Sadeh, Y. Zilberman, X. Feng, L. GlassMarmor, I. Lejbkowicz, K. Müllen, A. Miller, H. Haick, ACS Chemical Neuroscience 2 (2011) 687–693. [183] U. Tisch, I. Schlesinger, R. Ionescu, M. Nassar, N. Axelroad, D. Robertman, Y. Tessler, F. Azar, A. Marmur, J. Aharon-Peretz, H. Haick, Nanomedicine 8 (2013) 43–56. [184] H. Haick, M. Hakim, M. Patrascu, C. Levenberg, N. Shehada, F. Nakhoul, Z. Abassi, ACS Nano 3 (2009) 1258–1266. [185] K. Wlodzimirow, A. Abu-Hanna, M. Schultz, M. Maas, L. Bos, P. Sterk, H. Knobel, R. Soers, R. Chamuleau, Biosensors and Bioelectronics 53 (2014) 129– 134. [186] D. Guo, D. Zhang, L. Zhang, G. Lu, Sensors and Actuators B 173 (2012) 106–113. [187] J. Yu, H. Byun, M. So, J. Huh, Sensors and Actuators B 108 (2005) 305–308. [188] G. Pennazza, E. Marchetti, M. Santonico, G. Mantini, S. Mummolo, G. Marzo, R. Paolesse, A. D‘Amico, C. Di Natale, Journal of Breath Research 2 (2008) 017009. [189] J. Ito, T. Nakamoto, H. Uematsu, Sensors and Actuators B 99 (2004) 431–436. [190] M. Tanaka, H. Anguri, A. Nonaka, K. Kataoka, J. Kita, S. Shizukuishi, Journal of Dental Research 83 (2004) 317–321. [191] A. Nonaka, M. Tanaka, H. ANguri, H. Nagata, J. Kita, S. Shizukuishi, Oral Disease 11 (2005) 35–36. [192] A. Bikov, J. Pako, D. Kovacs, L. Tamasi, Z. Lazar, J. Rigo, G. Lozonczy, I. Horvath, Biomarkers 16 (2011) 476–484. [193] A. Bikov, Z. Lazar, K. Schandl, B. Antus, G. Lozonczy, I. Horvath, Acta Physiologica Hungarica 98 (2011) 321–328. [194] R. Antonelli, G. Pennazza, S. Scarlata, M. Santonico, M. Petriaggi, D. Chiurco, C. Pedone, A. D‘Amico, PLoS One 7 (2012) e45396. [195] N. Strachan, F. Nicholson, International Journal of Food Science and Technology 27 (1992) 261. [196] A. Smola, B. Schollkopf, Statistics and Computing 14 (2004) 199–222.