olfactory systems for medical applications
TRANSCRIPT
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Available online at www.sciencedirect.com
Sensors and Actuators B 130 (2008) 458–465
Olfactory systems for medical applications
A. D’Amico a,b, C. Di Natale a, R. Paolesse d, A. Macagnano c,E. Martinelli a, G. Pennazza a,∗, M. Santonico a, M. Bernabei a,
C. Roscioni e, G. Galluccio e, R. Bono f,E. Finazzi Agro g, S. Rullo h
a Department of Electronic Engineering, University of Rome‘Tor Vergata’, Via del Politecnico 1 – 00133 Roma, Italy
b CNR-IDAC, Department of Production System, Via del Fosso del Cavaliere 100 – 00133 Roma, Rome, Italyc CNR-IMM, Via del Fosso del Cavaliere 100 – 00133 Roma, Rome, Italyd Department of Chemical Science and Technology, University of Rome
‘Tor Vergata’, Via della ricerca scientifica – 00133 Roma, Italye S. Camillo – C. Forlanini Hospital, Piazza C. Forlanini 1 – 00151 Rome, Italy
f IDI (Immacoloata Dermopatic Institute) Hospital, Via dei Monti di creta 104 – 00167 Rome, Italyg PTV (Polyclinic of Tor Vergata) Hospital, Viale Oxford 81 – 00133 Rome, Italy
h Italian Hospital Group, Via Tiburtina 188 – 0012 Guidonia, Rome, Italy
Available online 19 September 2007
bstract
Prevention strategy is nowadays the main way in the complex issue of medical diagnosis. In this wake, performing non-invasive medical testsriented to an early diagnosis is one of the challenges for the promotion of new classes of instruments and for a faster intervention on the patient.
Clinical chemistry is the starting point of this challenge due to the fact that its scope is the quantification of specific compounds found in urinend blood, known to be related to specific pathologies.
This paper will deal with an overview of the electronic nose (EN) applications in medicine and in particular with recent data related to theollowing pathologies: lung cancer, schizophrenia, melanoma.
The design of an appropriate measurement protocol for each of the above mentioned applications is a crucial point of these studies, in order touarantee the representativeness of the measured samples and the reproducibility of the experiments.
The efforts spent so far have produced many stimulant and promising results together with fundamental questions about the real potentialitiesf this technology in medical fields and about the physiological mechanism involved in the modification of the chemical compounds related to thetudied diseases. Results, related to the above-mentioned pathologies, related to the EN application will be illustrated and commented.
2007 Elsevier B.V. All rights reserved.
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eywords: Electronic nose; Non-invasive diagnostic method; Medical applicat
. Introduction
Health-care strategies are currently oriented towards non-nvasive techniques for an early diagnosis. Chemical analysis
eems to be a good answer to accomplish both prevention andon-invasivity, and prevention is a fundamental requirement forn efficient treatment of the disease.∗ Corresponding author. Tel.: +39 06 72 597259; fax: +39 06 20 20519.E-mail address: [email protected] (G. Pennazza).
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925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved.oi:10.1016/j.snb.2007.09.044
The great potentialities of chemistry involved in medicaliagnosis is well-synthesized in the metabolic profile concept,tated by Jellum et al. [1], “If one is able to identify and deter-ine the concentration of all compounds inside the human body,
ncluding high molecular weight as well as low molecular weightubstances, one would probably find that almost every knownisease would result in characteristic changes in the biochemical
omposition of the cells and the body fluids”.Nonetheless the exploitation of ‘metabolic profile’ as sourcef information with diagnostic tasks is strictly connected withhe possible ways of accessing it and with the consequent range
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f suitable instruments used to perform the measurements. Inact, as the metabolic profile is representative of the internalhemistry of human body, its external expression consists ofnumber of biological media: breath, blood, urine, sweat and
kin.These sources of information are only partially exploited by
he current clinical chemistry where only the composition ofuman fluids such as blood and urines is analyzed, and corre-ation between a certain disease and chemical compounds arevailable for a large class of pathologies. Moreover, breath-tests also a well-accepted technique for routine analysis of bacte-ial overgrowth, fat malabsorption, pancreatic and liver functionest, and gastric emptying.
The chemical composition of each of the biological solid oriquid listed above is reflected in its volatile part, and this pro-ess is mediated by the physical and chemical properties of eacholecule. As a consequence the way of access to the individual
ealth state is represented by the volatile organic compoundsVOCs) found in the air surrounding living beings. This air con-ains meaningful information about the internal chemistry of theody and then it can provide a vehicle for the identification ofiseases.
The perception of volatile compounds through the humanense of smell is called odor. Odor was actively used in the pastor medical diagnosis and relationships between perceived odornd diseases that were found in many kinds of pathologies [2].
In these last three decades a series of instruments such as gashromatography (GC) and/or GC linked with mass spectrome-ry (GC–MS) have been used for the identification of molecules,hich are responsible of typical odors occurring in specificiseases. Actually GC–MS instruments allow rather a net sep-ration and identification of compounds in complex mixtures.his is of course something very similar to human odor per-eption even if, often, some potential indicators of disease areolecules not perceived by the smell.Many of these studies have been performed with in vitro
ultures, and promising results have been obtained in the iden-ification of volatile organic compounds colonizing the humanody. The consequent evolution of the experiment concernedith the study on human substrates such as urine and blood. On
he basis of the achieved results, odor potentialities as diagnosticeans, suggested the use of an electronic nose in medical field,
lso preconized since the 60s [3].The history of artificial olfaction started in the 80s when the
bsence of selectivity, one of the major drawbacks of chemicalensors, was taken into consideration as the basis for a novelnstrument able to provide global information about samples.his is somewhat resembling to the functioning of the humanlfaction with respect to odorants [4].
Artificial olfactory systems, on the basis of a similarity withhe natural olfaction receptors, are developed as arrays of non-elective sensors, characterized by a wide spectrum of sensitivityo many odorants, with a large overlap of responses toward
everal compounds [5].The parallel between the natural and artificial systems is thenarried on at the level of the complex sensory signal processingarried out in the olfactory bulb and cortex, which is mimicked
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y a proper application of multicomponent data analysis, rang-ng from classical statistics to chemometrics and neural networks6].
The application of electronic nose in many different fields,edical diagnosis included, has seen a large development in
hese last two decades, and many experiments performed with aumber of different instruments have been reported in literature7,8].
In order to avoid a possible misunderstanding about the termelectronic nose’, it is important to remark that the uniqueommon features between natural olfaction and this kind ofnstruments consist of an array of non-selective gas-sensors. Onhe other hand, it is worth pointing out that the two systems aretill far away from each other in structure and functions, withgreat superiority of the natural one. The basic point of this
isparity is the difference between the natural receptors and thertificial sensors, obtained on the basis of a large number ofifferent mechanisms.
The fundamental difference just illustrated evidences the dis-inction between artificial and human perception. Odor is theensation of smell as perceived by human olfaction that is nothe same as the image obtained by an electronic nose. Althoughhe term ‘odor measurements’ is usually adopted for electronicoses experiments it is important, especially in the medical field,o keep in mind that these measurements are generally far fromhe odor perception.
The application of electronic noses to the medical field cane divided into two main areas related to in vitro and in vivoeasurements.Among the medical experiments reported in literature, in
itro measurements are mainly oriented to the study of bacteriaultures, which has also shown to be the most investigated mattern this field (almost 50% of electronic nose works in medicine)9,10].
About the rather satisfactory results obtained in micro-rganisms identification, it is worth to remark the large numberf application of these investigations; actually bacteria culturestudies are of prominent importance for environmental issues,ood industry and diagnostic scopes.
Positive results were obtained in this regard about the identi-cation of bacteria responsible for gynecological [11], stomachhelicobacter pilory) [12] and ophthalmic infections [13]. Thesenvestigations triggered the possibility to detect infections inumans. The rapid identification of viral or bacterial origin ofathology is of great importance for a rapid therapy and, mostf all, to avoid the massive use of antibiotics. To this regard,t is important to mention the results obtained in the case ofneumonia diagnoses [14].
Furthermore, the feasibility of medical diagnosis based onome connections between odor and certain diseases is sup-orted by some successful experiment reported in literature withell-trained dogs. The perception of pathologies by dogs was
necdoctically reported in the past, and in the last couple of
ears, some result appeared in literature concerned with theetection of skin [15], bladder [16], lung and breast [17] can-er. These experiences point to the fact that some diseases aressociated to particular classes of volatile compounds.
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In the following of this paper, the application of electronicoses to medical diagnosis is illustrated with particular emphasiso in vivo measurements of skin and breath odors. The diagnosticotentialities of these techniques in the case of lung cancer andchizophrenia will be also discussed with some details.
.1. The case of breath analysis
Breath is fundamentally composed of nitrogen, oxygen, car-on dioxide, water and inert gases. In addition, mixtures ofumerous compounds occur at concentrations in nmol/l–pmol/lppbv–pptv) range. Some of these compounds could be markersf a specific pathology [18]. The number of these compounds isigher than 200, as shown by Pauling et al. in the 70s in one of hisrst work about breath analysis by GC [19]. Experiments per-ormed in the last two decades pointed out a number of volatileubstances probably connected with certain diseases [20].
The correlation between VOCs and diseases seems to be dueo the fact that several organs contribute to the composition ofreath, which is expected to be rich in information coming fromiochemical processes and their alteration induced by patholo-ies [21].
Depending on the origin, biological processes inside humanody or on the environment surrounding it, VOCs can bendogenous or exogenous, with a different informative content,elated to different scopes. Exogenous molecules, especiallyalogenated organic compounds, may be analyzed for environ-ental or expositional issues to determine compound uptake
nd elimination into the body. But to monitor metabolic orathological processes endogenous substances are the mostnteresting.
The relationship between certain compounds and the associ-ted pathology is sometimes justified by a well-known biologicalrocess, while in other cases anomalous abundances are onlybserved without a precise explanation.
In different experiments there has been described a list of setsf VOCs that seem to have strong link with complex diseasesuch as liver diseases [22], heart failure [23], breast cancer [24]nd schizophrenia [25].
The list of biomarkers reported below, although not exhaus-ive, is related to a series of works found in literature. Each ofhe reported compounds is associated to certain diseases.
.1.1. Ethane and pentanePeroxidative activity [26] seems to be correlated with the
resence of these two hydrocarbons; actually hydrocarbons aretable end products of lipid peroxidation. Because of their lowolubility in blood, their exhalation into breath is an indicationf the progress of oxidative stress damage [27]. Consideringhat protein oxidation and bacterial metabolism are other poten-ial sources of hydrocarbons in the body, there could be someroblems of interpretation, although these processes seem noto interfere with the breath-test analysis [28].
.1.2. IsopreneIt can be seen as cholesterol synthesis indicator [29], and it
an give a measure of oxidative damage of the fluid lining of
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he lung [30]. On this basis cystic fibrosis [31] is a pathologyelated to this compound.
.1.3. AcetoneIts concentration is rather at high levels in patients with
uncontrolled) diabetes mellitus [32].
.1.4. Ethanol and methanolThese are indicators of alcohol addiction and of potential
ource of endogenous short chain alcohols in the intestinal bac-erial flora [33].
.1.5. Sulphur compoundsA high concentration of these molecules is likely related to
iver diseases. Compounds like ethyl mercaptane, dimethylsul-de or dimethyldisulfide are responsible for the characteristicdor in the breath of cirrhotic patients. Under normal condi-ions, the concentrations of sulphur-containing compounds inuman blood and breath are rather low.
.1.6. AminesThe Amines are typical products of putrefaction processes.
haracteristic odor of uremic breath is due to elevated concen-rations of dimethylamine and trimethylamine [34].
.1.7. AmmoniaIt is abundant in the breath of uremic patients and in cases
f severe kidney failures [35].
Some of the reported pathologies can be diagnosed byonventional techniques, without a particular attention to theeasurements timing but for certain diseases, such as lung or
rostate or skin tumors, an early detection is dramatically neces-ary and the studies of alternative and non-invasive techniquesould open the way to a deeper comprehension of the pathologytself for instance in terms of its dynamic development.
In the following some of these pathologies such as lung can-er, melanoma, prostate and bladder cancer, schizophrenia isllustrated on the basis of recent research achievements.
.2. Lung cancer
In these last two decades a noteworthy research work haseen oriented to the identification of some particular VOCs asarkers of lung cancer; in these studies it turned out that it
eems not possible to find any specific marker but rather someompounds occurring in anomalous concentration in the breathf lung cancer patients [36,37].
One of the pioneers in this research activity was Phillips,ho designed a Breath Collecting Apparatus (BCA) to sam-le 10 L of breath during a 5 min breathing of the patient [38].
he measurements of the collected samples were performedith a GC, and in one of the works [39], Phillips obtainedpercentage of correct identification of lung cancer cases of1%.
d Actuators B 130 (2008) 458–465 461
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In his last publications on Chest [40] he provided a list of8 VOCs considered determinant for the identification of thisisease, because of their anomalous abundance. Among theseompounds isoprene, benzene and four derivates of benzene (o-oluidine and aniline among the others) were found. It is wortho note that the presence of benzene in human breath is still notully explained and it deserves more attention.
On this basis it is clear that a possible identification of theisease is achievable by the application of the pattern recognitionechniques for the analysis of more compounds, rather than onhe individuation of a specific biomarker.
The authors of this article have been studying the possibil-ty of lung cancer diagnosis by electronic nose analysis of thereath, since 1999. Experiments were carried out with the elec-ronic nose developed at the University of Rome Tor Vergata41]. This instrument is based on eight quartz microbalancesoated by different metalloporphyrins [42].
In the first work performed in the period 1999–000 and published in 2003 [43], the target was to test theapability of the electronic nose to correctly classify groups ofubjects.
A total number of 42 volunteers, affected by various formsf lung cancer, have been recruited at the C. Forlanini Hospi-al in Rome. Thirty-five of them were hospitalised waiting forurgical treatments. Nine patients have been checked after a sur-ical removal of the tumor mass from lung. Two patients wereeasured before and after the surgical operation. Eighteen vol-
nteers have been recruited among the medical and nurse stafff the hospital, as reference.
Three different classes were considered (lung cancer dis-ased, reference group and post-surgery patients).
With this classification scheme a PLS-DA model was built.esults indicate that 100% of the lung cancer patients were cor-
ectly classified. 94% of the controls were correctly identified
nd 6% of them have been classified as belonging to the post-urgery group. It is worth mentioning the data related to the twoatients who were measured twice, before and after surgery.he data migration from the class of lung cancer diseases to thebodp
ig. 2. General overview of the sampling apparatus for breath collection used in the sung cancer study. A decisive improvement is represented by the three-ways valve us
ontrols and (3) post-surgery. Arrows indicate the two patients measured beforend after the surgical treatment (figure extracted by [43]).
lass of controls is well evident. The score plot of this model iseported in Fig. 1.
In the last experiment, performed during 2004, the main con-ribution has been the introduction of a new breath samplingrotocol able to separate the different parts of each single breath;ctually collected breath was very likely containing compoundsn equilibrium with the whole respiratory tract, from mouth toungs. Nonetheless, it was expected that the air coming fromhe deep lung was prevailing in the last portion of a singlereath. To remove the upper part of breath a simple, efficientnd reproducible sampling procedure was adopted. The ‘totalreath’ expired by the individual, entering the valve, finds only
ne way out. By this procedure the first part of the breath, theead space, goes to fill a first bag of fixed volume. This com-letely filled bag results in a closed set, so the second part ofecond experiment (2004) performed with the ‘Tor Vergata’ electronic nose fored for the breath separation in dead space volume and alveolar breath.
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Table 1Confusion matrix of the PLS-DA model built on data collected by the ‘TorVergata’ electronic nose in the C. Forlanini hospital during the lung cancerexperiments campaign
Control Patients Percentage of correctclassifications (%)
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he model is oriented to distinguish between two classes: controls and patients.
he breath, the alveolar one, goes to open the second way fillingnother bag, then used for the measurement. Fig. 2 shows anverview of the measurement set up.
This investigation involved a group of 58 volunteers. Allf them were requested to perform a bronchoscopy as a con-equence of anomalous chest X-rays. Patients were controlledmmediately before the bronchoscopy. The record of cases wass following: 22 patients affected by lung cancer (no-small cellancer diagnosed for 20 of them) and 12 affected by diverse lungathologies.
Classification was performed with discriminant analysisolved by partial least squares cross-validated by “leave-one-ut” technique. Two models have been calculated in Matlabrogramming environment.
The first model considered the discrimination between theroup of control subjects and the group of patients. Cross-alidated results provided 88% of correct identification; theelated confusion matrix is shown in Table 1. One hundred per-ent of control subject were correctly identified while only 79%f patients were correctly attributed.
The second model aimed at identifying the three classesf subjects. It has to be noted that also in case of negativeeports some lesion of the lungs were found and these subjectsere addressed to a bronchoscopy examination as a conse-uence of an X-ray detection of a localized damage in the lungissue.
The performance of the cross-validated model gave an 81%f correct classification, but in this case a distinction betweenhe related percentages of each class is crucial. As for the pre-ious models, no error in control identification was foreseen,ung cancer identification was estimated at 82% and only 41%f negative cases were identified as peculiarly different fromhe other two classes. The results in terms of identification per-ormance may be considered as less strong with respect to therevious where complete identifications were found. Nonethe-ess, it has to be considered that the identification rate of lungancer affected subjects is estimated to be more than 80%. Theoss of general performance is mostly due to the class of neg-tive bronchoscopies. Indeed these subjects had some seriousung lesions, justifying a bronchoscopy, due to a wide range ofauses.
.3. The case of skin analysis
Many biochemical processes taking place on the skin surfaceesult in the production of a number of VOCs whose concentra-ion can provide information about alterations due to different
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athologies. These reactions are originated by skin gland activ-ties and bacterial population.
The starting point for the study of the skin behavior fromither the odor point of view or chemical pattern, is the descrip-ion of the basic pattern of bacteria colonization of a healthyuman skin. The resident aerobic flora consist of Gram-positiveocci of staphylococcus and micrococcus, and a variety of Gram-ositive rods, mainly corynebacterium. The main anaerobicesidents are propionibacteria, which are localized in the fol-icles of the sebaceous glands of adults. The microbial florasually localized on the skin appears to have several functions,he most important of which is probably the defense actiongainst pathogenic bacterial and micotic infections.
An important measurand originated by skin is the sweat.weat is sterile and mostly odorless by its own when secreted.he main role of the bacteria in the odor formation appears
o be the breakage of the precursor–odorant complex and theleavage of the covalent bonds holding the acid molecules to therecursors [44].
In the following of this section two experiences will be pre-ented. The first will regard schizophrenia identification by theeasure of sweat, the second is related to the study of melanoma
y the analysis of skin headspace.
.3.1. SchizophreniaDiagnosis of schizophrenia is normally elaborated on the
asis of a protocol that takes into account a series of simul-aneous behavioral, emotional and cognitive disturbances [45].onsidering that the difference between schizophrenia and men-
al disorder is not still so clear, due to the number of similarityetween these two diseases, it is of course very important tond out another kind of diagnostic criteria, with objective anduantitative evaluations.
The observation of certain chemical processes has shown aechanism which could be exploited with diagnostic task: in
chizophrenic patients a faulty gene that codes for the enzymeopamine-b-hydroxylase operates an excessive biosynthesis ofyrosine (aminoacid precursor of dopamine), and this excessiveynthesis generate a high level of dopamine. This mechanismesults in a general increase of trans-3-methyl-hexenoic acid,hich is a product of the auto-oxidation of dopamine excess
46]. On this basis trans-3-methyl-hexenoic acid could be useds marker of schizophrenia, as proposed in a work several yearsgo [47], in which its presence was associated to a particulardor found out in schizophrenic individuals. These possibilities,lthough not confirmed by GC studies, has been investigatedgain in a work about breath analysis [25].
In 2001 deep research work was performed by the authors ofhis paper, who involved three classes of individuals in a clinic ofome: schizophrenic and psychopathic patients, and a controlroup of individuals. This study was oriented to the identifi-ation of schizophrenia by the study of VOCs in sweat [48].
he sampling was performed with a cotton compress appliedor 30 min on the skin. One half of each compress was then ana-yzed with the electronic nose and the other one with the GC–
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A. D’Amico et al. / Sensors and Actuators B 130 (2008) 458–465 463
Fig. 3. Score plot of the PLS-DA model built on the data obtained by GC–MSaop
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Nevi were the highest numbers of lesions while the melanomawere 10. It is worth to remark that one of the case diagnosed as
nd electronic nose in the experiment about schizophrenia diagnosis by sweatdor analysis. The three classes of controls, mental disorder and schizophrenicatient seem to be discriminated.
GC–MS analysis has revealed that trans-3-methyl-hexenoiccid is not only present in schizophrenic patient sweat, but inhe control group samples too. On this basis trans-3-methyl-exenoic acid seems to be not a reliable marker, even if it isresent in high concentrations in the schizophrenic individuals.
multivariate data analysis model, built on the data fusion ofhe two instruments (GC–MS and electronic nose), has given an0% correct classification of schizophrenia cases with respecto the healthy ones. The score plot of this model is reported inig. 3.
.3.2. MelanomaThe chemical composition of the skin headspace is proved to
e rather different for each individual, so it is possible to findnd describe a personal fingerprint characteristic of each person.n this basis any biological alteration of the skin could result in
n alteration of this particular chemical pattern.As example, melanoma is a tumoral form of the skin, origi-
ated by melanocitary cells modification. Surgical removal is theost efficient intervention to oppose this disease, particularly at
arly stage. Actually, in order to obtain an early diagnosis, tech-ological research has directed its efforts towards techniquesf visual investigation. The most simple of them is the der-atoscopy, while the more complex exam of epiluminescence
s currently the most used. Considering this situation, the possi-ility of diagnosis by means of the VOCs analysis is of coursen interesting task. A demonstration of the feasibility of suchproject has good results obtained by a study performed withell-trained dogs, which used olfaction for the melanoma iden-
ification [15].The authors of the present work have investigated this possi-
ility by means of an electronic nose developed at the UniversityTor Vergata’. This measurements campaign has been performedt the ‘Istituto Dermopatico dell’Immacolata’ (IDI) in Rome49]. A total number of 40 patients have been involved. Each of
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ig. 4. Comparison between the electronic nose patterns related to the sameatient before (figure on the left) and after (figure on the right) a successfulurgical operation.
he examined cases was analyzed by epiluminescence, and alsohistological reference was available for the patients subject to
he surgical removal. A sub-group of seven persons was alsoeasured twice, before and after the intervention.The sampler utilized for the measurements consisted of a
tainless steel cylinder with a 4 cm diameter. An important stepf the sampling protocol was the differential strategy, consistingf a double measurement: one on the lesion and the other innother portion of skin close to the lesion area, and used aseference. The final data used for the analysis considered the
ig. 5. Comparison between the electronic nose patterns related to the nevusf the same patient before (figure on the left) and after (figure on the right) auccessful surgical operation. The first diagnosis indicated a possible melanoma,ut it was not confirmed by the istology. The electronic nose confirmed theegative diagnosis before and after the operation.
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elanoma resulted as negative, by the histological report, andas classified as negative by the electronic nose too.A promising result was obtained by the comparison between
he two patterns of a patient recorded before and after the surgicalreatment. In Fig. 4 it is possible to observe a case in whichhe patterns before and after the removal of melanoma are veryifferent, while this difference disappears in Fig. 5 where thewo patterns of the false positive case are reported.
A PLS-DA model has been also built in a Matlab envi-onment, with the leave-one-out criterion as cross validationethod. This model was oriented to distinguish between nevi
nd melanoma. The obtained percentage of correct classifica-ion was 87%, rather large and encouraging if compared withhe other used diagnostic methods mentioned above. It is impor-ant to underline that the cases of melanoma are quarter of theotal number of analyzed cases, so that there could be obtained
correct classification of 90% for nevi and 70% of correctlassification with melanoma.
. Conclusions
In this paper an overview of the electronic nose applicationsn medicine has been presented, with a particular attention toome in vivo experiments.
A particular consideration has been given to breath and skinnalysis, with a number of experiences reported in literaturebout findings on correlations between alteration of odor com-osition and certain diseases. These two particular cases haveeen completed with the presentation of three experiences of theuthors.
Further efforts have to be spent in the direction of a deeperomprehension of the biological mechanisms from which thebserved alterations are originated. Besides, sensors technologysed for these works has to be improved in sensitivity and ineproducibility in order to reach a technical condition of superiorerformances and to get, as a possible consequence, a bettercceptation in behalf of the medical world.
These two needs can be satisfied undertaking large scalexperiments taking place in different hospital structures, in ordero enlarge the number of recorded cases and through the ENptimization. Moreover the support of traditional diagnosticnstruments is still a must for the correct interpretation of thechievable results.
eferences
[1] E. Jellum, O. Stokke, L. Eldjam, Application of gas chromatography, massspectrometry and computer methods in clinical biochemistry, Anal. Chem.46 (1973) 1099–1166.
[2] M. Smith, L.G. Smith, B. Levinson, The use of smell in differential diag-nosis, Lancet 2 (1982) 1452–1453.
[3] C. Roscioni, G. De Ritis, On the possibilities to using odors as a diagnostictest of disease (preliminary note), Ann. Ist Carlo Forlanini 28 (4) (1968)
457–461.[4] K. Persaud, G. Dodds, Analysis of discrimination mechanisms in the mam-malian olfactory system using a model nose, Nature 299 (1982) 352–355.
[5] B. Malnic, J. Hirono, T. Sato, L. Buck, Combinatorial receptor codes forodors, Cell 96 (1999) 713–723.
[
uators B 130 (2008) 458–465
[6] E.L. Hines, E. Llobet, J.W. Gardner, Electronic noses: review of signalprocessing techniques, IEE Proc. Circuits Devices Syst. 146 (1996) 297–310.
[7] T. Nagle, S. Schiffmann, J. Gardner, T. Pearce (Eds.), Handbook of MachineOlfaction, John Wiley and Sons, Weinheim, Germany, 2003.
[8] A.P.F. Turner, N. Magan, Electronic noses and disease diagnostic, Nat. Rev.Microbiol. 2 (2004) 161–166.
[9] J.W. Gardner, M. Craven, C. Dow, E.L. Hines, The prediction of bacteriatype and culture growth phase by an electronic nose with a multi-layerperceptron network, Meas. Sci. Technol. 9 (1998) 120–127.
10] T.D. Gibson, O. Prosser, J.N. Hulbert, R. Marshall, P. Corcoran, P. Lowery,E.A. Ruck-Keene, S. Heron, Detection and simultaneous identification ofmicro-organisms from headspace samples using an electronic nose, Sens.Actuators B 44 (1997) 413–422.
11] S. Chendiok, B.A. Crawley, B.A. Oppenheim, P.R. Chadwick, S. Higgins,K.C. Persaud, Screening for bacterial vaginosis: a novel application ofartificial nose, J. Clin. Pathol. 50 (1997) 731–790.
12] A.K. Pavolu, N. Magan, D. Sharp, J. Brown, H. Barr, A.P.F. Turner, Anintelligent rapid odour recognition model in discrimination of Helicobacterpylori and other gastroesophageal isolates in vitro, Biosen. Bioelectron. 15(2000) 333–342.
13] P. Boilot, E.L. Hines, J.W. Gardner, R. Pitt, S. John, J. Mitchell, D.W.Morgan, Classification of bacteria responsible for ENT and eye infectionsusing the cyranose system, IEEE Sens. J. 2 (2002) 247–253.
14] W. Hanson III, H. Steinberger, The use of a novel electronic nose to diagnosethe presence of intrapulmonary infection, Anesthesiology 87 (1997) 269.
15] D. Pickel, G. Manucy, D. Walker, S. Hall, J. Walker, Evidece for canineolfactory detection of melanoma, Appl. Anim. Behav. Sci. 89 (2004)107–116.
16] C. Willis, S. Church, C. Guest, A. Cook, N. McCarthy, A. Bransbury, M.Church, J. Church, Olfactory detection of human bladder cancer dogs: proofof principle study, Brit. Med. J. 329 (2004) 712–718.
17] M. McCulloch, T. Jezierski, M. Broffman, A. Hubbard, K. Turner, T.Janecki, Diagnostic accuracy of canine scent detection in early- and late-stage lung and breast cancers, Integr. Cancer Ther. 5 (2006) 1–10.
18] M. Phillips, J. Herrera, S. Krishnan, M. Zain, J. Greenberg, R. Cataneo,Variation in volatile organic compounds in the breath of normal humans,J. Chromatogr. Biomed. Appl. 729 (1999) 75–88.
19] L. Pauling, A.B. Robinson, R. Teranishi, P. Cary, Quantitative analysis ofurine vapour and breath by gas–liquid partition chromatography, Proc. Natl.Acad. Sci. U.S.A. 68 (1971) 2374–2376.
20] M. Phillips, Breath test in medicine, Sci. Am. 267 (1992) 74–79.21] W. Miekisch, J.K. Schubert, G. Noeldge-Schomburg, Diagnostic potential
of breath analysis—focus on volatile organic compounds, Clin. Chim. Acta347 (2004) 25–39.
22] S.S. Sehnert, L. Jiang, J.F. Burdick, T.H. Risby, Breath biomarkers fordetection of human liver diseases: preliminary study, Biomarkers 7 (2002)174–187.
23] M. Phillips, R.N. Cataneo, J. Greenberg, R. Grodman, M. Salazar, Breathmarkers of oxidative stress in patients with unstable angina, Heart Dis. 5(2003) 95–99.
24] M. Phillips, R. Cataneo, B. Ditkoff, Volatile markers of breast cancer in thebreath, Breast J. 9 (2003) 184–191.
25] M. Phillips, M. Sabas, J. Greenberg, Increased pentane and carbon disul-phide in the breath of patients with schizophrenia, J. Clin. Pathol. 46 (1993)861–864.
26] E. Aghdassi, J.P. Allard, Breath alkanes as a marker of oxidative stressin different clinical conditions, Free Radic. Biol. Med. 28 (2000) 880–886.
27] T.H. Risby, S.S. Sehnert, Clinical application of breath biomarkersof oxidative stress status, Free Radic. Biol. Med. 27 (1999) 1182–1192.
28] C.M. Kneepkens, G. Lepage, C.C. Roy, The potential of the hydrocarbon
breath test as a measure of lipid peroxidation, Free Radic. Biol. Med. 17(1994) 127–160.29] B.G. Stone, T.J. Besse, W.C. Duane, C.D. Evans, E.G. DeMaster, Effectof regulating cholesterol biosynthesis on breath isoprene excretion in men,Lipids 28 (1993) 705–708.
d Act
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[
A. D’Amico et al. / Sensors an
30] M.W. Foster, L. Jiang, P.T. Stetkiewicz, T.H. Risby, Breath isoprene: tempo-ral changes in respiratory output after exposure to ozone, J. Appl. Physiol.80 (1996) 706–710.
31] L.T. McGrath, R. Patrick, P. Mallon, L. Dowey, B. Silke, W. Norwood, etal., Breath isoprene during acute respiratory exacerbation in cystic fibrosis,Eur. Respir. J. 16 (2000) 1065–1069.
32] H.E. Lebovitz, Diabetic ketoacidosis, Lancet 345 (1995) 767–772.33] K. Cope, T. Risby, A.M. Diehl, Increased gastrointestinal ethanol pro-
duction in obese mice: implications for fatty liver disease pathogenesis,Gastroenterology 119 (2000) 1340–1347.
34] M.L. Simenhoff, J.F. Burke, J.J. Saukkonen, A.T. Ordinario, R. Doty,Biochemical profile or uremic breath, N. Engl. J. Med. 297 (1977) 132–135.
35] S. Davies, P. Spanel, D. Smith, Quantitative analysis of ammonia on thebreath of patients in end-stage renal failure, Kidney Int. 52 (1997) 223–228.
36] H.J. O’Neil, S.M. Gordon, M.H. O’Neil, R.D. Gibbons, J.P. Szidon, Acomputerized classification technique for screening for the presence ofbreath biomarkers in lung cancer, Clin. Chem. 34 (1988) 1613–1618.
37] G. Preti, J. Labows, J. Kostelic, S. Aldinger, R. Daniele, Analysis oflung air from patients with brochogenic carcinoma and controls using gaschromatography–mass spectrometer, J. Chromatogr. 432 (1988) 1.
38] M. Phillips, J. Greenberg, Methods for the collection and analysis of volatilecompounds in the breath, J. Chromatogr. 564 (1991) 242–249.
39] M. Phillips, K. Gleeson, J.M.B. Hughes, J. Greenberg, R.N. Cataneo, L.
Baker, et al., Volatile organic compounds in breath as markers of lungcancer: a cross-sectional study, Lancet 353 (1999) 1930–1933.40] M. Phillips, R.N. Cataneo, A.R.C. Cummin, A.J. Gagliardi, K. Gleeson,J. Greenberg, R.A. Maxfield, W.N. Rom, Detection of lung cancer withvolatile markers in the breath, Chest 123 (2003) 2115–2123.
[
uators B 130 (2008) 458–465 465
41] A. D’Amico, C. Di Natale, A. Macagnano, F. Davide, A. Mantini, E.Tarizzo, R. Paolesse, T. Boschi, Technology and tools for mimicking olfac-tion: status of the Rome Tor Vergata electronic nose, Biosens. Bioelectron.13 (1998) 711–721.
42] A. D’Amico, C. Di Natale, R. Paolesse, A. Mantini, A. Macagnano, Metal-loporphyrins as basic material for volatile sensitive sensors, Sens. ActuatorsB 65 (2000) 209–215.
43] C. Di Natale, A. Macagnano, E. Martinelli, R. Paolesse, G. D’Arcangelo,C. Roscioni, A. Finazzi-Agro, A. D’Amico, Lung cancer identification bythe analysis of breath by means of an array of non-selective gas sensors,Biosens. Bioelectron. 18 (2003) 1209–1218.
44] X. Zeng, L. Leyden, H. Lawley, K. Sawano, I. Nohara, G. Preti, Analysisof characteristic odors from human male axillae, J. Chem. Ecol. 17 (1991)1469.
45] A. Sawa, S.H. Snyder, Schizophrenia: diverse approaches to a complexdisease, Science 296 (2002) 692.
46] K. Smith, G.F. Thomspon, H.D. Koster, Sweat in schizophrenic patients:identification of the odorous substance, Science 166 (1969) 398.
47] K. Smith, J. Sines, Demonstration of a peculiar odor in the sweat ofschizophrenic patients, Arch. Gen. Psychiatry 212 (1960) 184.
48] C. Di Natale, R. Paolesse, G. D’Arcangelo, P. Comandini, G. Pennazza,E. Martinelli, S. Rullo, M.C. Roscioni, C. Roscioni, A. Finazzi-Agro, A.D’Amico, Identification of schizophrenic patients by examination of bodyodor using gas chromatography–mass spectrometry and a cross selective
gas sensor array, Med. Sci. Monit. 11 (2005) 366–375.49] A. D’Amico, R. Bono, G. Pennazza, M. Santonico, G. Mantini, M. Bern-abei, M. Zarlenga, C. Roscioni, E. Martinelli, R. Paolesse, C. Di Natale,Identification of melanoma with a gas sensor array, Skin Res. Technol.(2007) doi:10.1111/j1600-0846.2007.00284.x.