ELECTRONIC NOSES APPLICATIONS AND TECHNOLOGIES
Medical diagnosis with electronic nosesDi Natale, C.1,2; Pennazza, G.1; Martinelli, E.1; Santonico, M.1; Macagnano, A.2; Paolesse, R.2,3; D’Amico, A.1,2
1
Department of Electronic Engineering, University of Rome “Tor Vergata”,
Via del Politecnico 1; 00133 Roma; Italy, Fax: +39 (0)6 2020 519, Email: dinatale@uniroma2.it
2CNR-IMM,
Via del Fosso del Cavaliere, 00133, Roma; Italy.
3Department of Chemical Science and Technology, University of Rome
“Tor Vergata”, Via della Ricerca Scientifica; 00133 Roma; Italy
Received November 05, 2004. In final form June 1st , 2005
Abstract
Electronic noses are arrays of partially selective chemical sensors. These systems
appeared at the end of the eighties as technological attempts to mimic some
of the functions of natural olfaction. Although they were introduced as a sort
of olfaction model they were soon applied to the qualitative analysis in a number
of different fields disciplines such as food analysis, environmental control
and industrial processes. Among these fields also medicine was considered, in
particular taking into account the diagnostic role that olfaction plays in many
traditional medicines. In this paper the use of electronic noses in medicine
is discussed with a particular emphasis on the possible use of this technology
in telemedicine.
Resumen
Las narices electrónicas son dispositivos que forman
parte de ciertos sensores químicos que pueden operan bajo condiciones
de relativa selectividad. Estos dispositivos aparecen durante la década
de los 80, como un intento de emular las funciones del olfato natural. Estos
sistemas fueron rápidamente adaptados para el análisis cualitativo
en una serie de procesos relacionados con el análisis de alimentos,
control ambiental y procesos industriales. En el campo de la medicina se las
empleó como dispositivo de olfato, en el proceso del diagnóstico.
En este trabajo se discute el empleo de las narices electrónicas en
la llamada “ telemedicina”.
Introduction
The concept that pathologies may alter the chemical composition of human bodies
is at the basis of the clinical chemistry. Up to now, the search for correlation
between the concentrations of selected bio-chemical compounds in human liquids
(mostly urine and blood) and pathologies driven the research in clinical chemistry
and it resulted in many currently available analytical techniques for pathologies
diagnosis.
Although most of the activities were concerned with the analysis in corporeal
liquids, years ago some researches pointed out that also the atmosphere surrounding
the living bodies may contain information about the clinical status of individuals.
This was well known in the past when physicians based most of their diagnosis
upon their senses. Among them olfaction played a non negligible role in the
process of diagnosis formation.
Analytical studies evidenced the presence of typical compounds in the atmosphere
immediately outside the human body and in the breath.
The chemical identity of many volatile constituents of body fluids (such as
urine, serum and blood) has been described in the diagnosis of several diseases.
Among them diabetes, respiratory viral infections and renal insufficiency deserve
to be quoted. In particular, the volatile organic compounds present in expired
breath may give a potential source of information about general metabolic conditions
and, in particular, on lung physiology. Expired air contains a number of volatile
organic constituents which are close to be in equilibrium with a number of compartments
within the lung and may arise from endogenous or absorbed volatile substances
present in the blood. In addiction, certain substances in lung air may be in
equilibrium with alveolar fluid or lining material.
The advent of chemical sensors and the electronic nose technology in the last
decade opened the opportunity to reconsider these seminal studies in order to
look for some forms of novel diagnostic tools considering the chemical information
contained in the volatiles secreted outside the human body.
In particular, electronic noses have been applied in many different fields.
Recently, also the medical field was taken into consideration and the application
of Electronic Nose to detect diseases has been proposed [1].
Electronic
Nose Technology
In the eighties the absence of selectivity, one of the major drawbacks of chemical
sensors, was taken into consideration as the basis for a novel instrument able
to provide global information about samples. This is somewhat resembling what
the human olfaction do with odorants [2]. These instruments are basically arrays
of non-selective sensors. Sensors are characterised by a wide spectrum of sensitivity
to many odorants, with a large overlap of response towards several compounds.
This fundamental characteristic of artificial sensors is similar to that found
in natural olfaction receptors [3]. This similarity is the basis on which artificial
olfaction systems are developed. The sensors response is not univocally correlated
with the concentration of a single compound, but rather, it is a sort of combination
of the whole chemical information contained in a sample. The performance of
natural olfaction at molecular discrimination and recognition results from a
complex sensory signal processing carried out in the olfactory bulb and cortex.
In the same way, most of the features of the artificial olfaction are revealed
after a proper application of multicomponent data analysis ranging from classical
statistics, chemometrics to neural networks [4]. The development of electronic
noses has become a well consolidated field of research and several examples
of these arrays have been widely reported in the literature. Different instruments
are also commercially available on the market [5].
It has to be remarked that in spite of the widely accepted term “electronic
nose”, current devices are still far from the structure and functions
of natural olfaction senses. The only point in common between artificial and
natural system is the strategy of measurement that is largely based on arrays
of non-selective sensors. The concept underlying electronic nose systems has
been demonstrated to be independent of the particular sensor mechanism, indeed
during the last decade almost all the available sensor technologies have been
utilised for electronic noses. Clearly, current sensors are very different from
natural receptors. These dissimilarities make the perception of electronic nose
very different from that of natural olfaction but, in spite of these, several
examples of correlations between sensorial analysis and electronic nose have
been demonstrated.
The
“LibraNose” Project
The LibraNose Project was started at the University of Rome Tor Vergata in 1994.
The first protoype of LibraNose appeared in 1995 as an array of Thickness Shear
Mode Resonators (TSMR) also known in literature as Quartz Microbalance sensors.
The chemical sensitivity is provided by a molecular film of pyrrolic macrocycles
(mostly metalloporphyrins and similar compounds). In the current configuration
eight sensors are used [6].
Metalloporphyrins are well known for their important role played in many biological
processes. In particular, solid state metalloporphyrins show a large selectivity
which can be oriented towards different classes of species by changing the molecular
constituents: pyrrolic ring, central metal atom and peripheral substituents
[7].
Furthermore, metalloporphyrins, due to their organometallic nature, are particularly
eligible for artificial olfaction, indeed most of the odorous compounds are
excellent ligands for metal ions. Although very little is known about the structures
of the olfactory receptor proteins, it may be argued that many of the receptors
are likely to contain metal ions at their active sites [8].
Medical
issues for electronic nose applications in medicine
In recent years, the medical field was taken into consideration as a possible
application area for Electronic Noses. In this context the attention of some
researchers was oriented to the identification of infections in living tissues
[9, 10, 11]. On these basis the detection of bacteria related to gynaecological
infections [12] and the helicobacter pilory [13] were verified with in-vitro
analysis.
Besides the analysis of bacteria cultures growing in controlled media, the analysis
of the odour of human body was also attempted. In particular the odour of the
body surface and the breath was particularly interesting.
Body
odour
Human body odour is the result of the combined action of skin gland, sectating
organic compounds, whose regulation is subdied to human hormonal control, and
bacterial populations localized at skin surfaces, which live metabolising and
transforming organic compounds that they are able to absorb from their external
environment. Any variation of this equilibrium is then expected to modify both
the nature and the amount of volatile compounds forming the skin odour. It is
now widely accepted that some pathologies may result in typical skin odour alteration.
Among these pathologies of great interest is the case of schizophrenia. Several
years ago a typical skin odour of individuals affected by schizophrenia was
observed [14]. This feature was related to some physiological modification of
internal methabolism resulting in the production of some compounds responsible
of the characteristic smell. A fatty acid derivative, the trans-3-methyl-2-hexenoic
acid was later reported as the major responsible of this characteristic odour.
For this reason this compound was proposed as a marker for the diagnosis of
schizophrenia, but this hypothesis was not later confirmed by gas chromatography-mass
spectrometry (GC-MS) analysis [15] basically because of the large variability
of the concentration among individuals and also due to the fact that an absolute
relation between the presence of the compound and the occurrence of the disease
was not observed. This original finding did not result in a practical diagnostic
tool of schizophrenia.
The application of electronic nose to the analysis of skin odour was investigated
[16], these results were the first attempt to start an investigation aimed at
detecting the schizophrenia.
Also the application of dogs as “diagnostic tools” was investigated.
Recently positive results appeared in the literature concerned with the detection
of skin [17] and bladder [18] diseases. These studies show that diseases can
be detected from volatile compounds and that a specific training can teach dogs
to recognize typical odours such as many other tasks for dogs. Nonetheless,
since canine perception is based on more than olfaction it is not completely
clear that, in these cases, odour is a sufficient source of information for
diagnosis.
Breath
odour
In the past years, many studies appeared on breath analysis by combined gas
chromatography-mass spectroscopy (GC-MS). They revealed the importance of some
classes of volatile in certain diseases. As an example mercaptans and aliphatic
acids were found in the breath of patients with cirrhosis of the liver while
dimethyl- and trimethylamines were found in the breath of uremic patients.
Breath from lung cancer patients was also examined with the same technique.
Evidence of different volatile patterns occurring in diseased individuals has
been claimed and the presence of a number of compounds whose presence may be
correlated to the lung cancer was found. These compounds are mostly some alkanes
(hexane and methylpentane among the others) and benzene derivatives such as
o-toluidine and aniline [19]. Although these studies did not result in a diagnostic
method due to the complex analysis carried out by GC-MS equipment they demonstrated
that the chemistry of breath contains information about the presence of cancer
in the lungs.
Also the possibility to detect tuberculosis was studied. The use of electronic
nose resulted to the detection of tuberculosis in sputum after an incubation
period of 6 hours with an enzyme lipase [20]. Although interesting, this procedure
is still not viable for on-line applications, specially for those required by
telemedicine applications. Another approach aimed at detecting tuberculosis
was followed applying an ion-mobility-spectrometer with an electronic nose oriented
data analysis [21]. First results have shown the possibility of a rapid identification
of bacterial cultures.
A selected study: lung
cancer
In here the results of an investigation aimed at studying the possibility to
identify lung cancer affected individuals from the analysis of their breath,
is illustrated [22].
A total number of forty-two volunteers, all affected by various forms of lung
cancer, have been recruited at the C. Forlanini Hospital in Rome. Thirty-five
of them were hospitalised waiting for a surgical treatment. Nine patients have
been checked after a surgical removal of a tumour mass from the lung. Two patients
were measured before and after surgical operations. Eighteen volunteers have
been recruited as reference among the medical and nurse staff of the hospital.
Each subject was required to follow the same diet and the same procedure for
mouth hygiene. Measurements have been performed in the morning before any food
in-take. Individuals were required to deeply breath in a sterile bag (volume
of about 4 L). The samples of breath were then immediately analysed with the
electronic nose. All measurements were performed on-site. Each subject was measured
twice, and the average measurement was used in the data analysis. In order to
minimise the influence of possible instrumental drift, the measurement sequence
was randomised. The whole experiment lasted five weeks. Post-surgical patients
were checked about one month after the operation, in the occasion of a periodical
control at the hospital site.
The data analysis here presented has been performed by using Partial Least Squares-Discriminant
Analysis (PLS-DA). Three different classes were considered (lung cancer diseased,
reference group, and post-surgery patients).
In this study we were concerned with testing the capability of the electronic
nose to correctly classify the groups of subjects. The simplest technique that
can be used for the scope is the linear discriminant analysis. The use of Partial
Least Squares (PLS) helps avoiding the drawbacks due to sensors correlation.
Furthermore, PLS provides a clasification of the sensors data in latent variables
that can be plotted to provide a visual representation of the classification
properties.
As in any supervised classification techniques, the classes have to be chosen
a-priori. The natural choice for the samples in our experiment was to choose
three classes including the patients with lung cancer, the healthy subjects,
and the patients after the surgery. With this classification scheme a PLS-DA
model has been built. The best fitting model included 5 latent variables. Data
have been linearly normalised and then autoscaled (zero mean and unitary variance).
Figure 1 shows the PLS-DA score plot of the first two latent
variables. In the plot about 92% of the total variance of the data is represented.
As it can be seen, a clear separation between the data related to patients with
lung cancer and the other samples is observed. On the other hand, the samples
related to post-surgery and healthy reference show some overlap.
A numerical evaluation of the classification properties can be obtained by considering
the cross-validation of the PLS-DA method according to a “leave-one-out”
technique. The results indicates that 100% of the lung cancer patients have
been correctly classified. 94% of the controls has been correctly classified
and 6% of them has been classified as belonging to the post-surgery group. Concerning
the samples of the post-surgery group 44% have been classified as belonging
to an autonomous class, while 56% as been classified as healthy controls.
It is worth mentioning the data related to the two patients measured twice,
before and after surgery. In figure 1 the migration of the
data points from the class of lung cancer diseases from the healthy controls
is illustrated.
Fig.
1. PLS-DA score plot of the first two latent variables (74% and 18% of explained
variance respectively). Classes are labeled as 1: lung cancer, 2: controls,
3: pust-surgery. Arrows indicate the two patients measured before and after
the surgical treatment.
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