1. Sensors and Actuators B 116 (2006) 55–61
Development of an electronic nose for fire detection
Emmanuel Scorsone, Anna Maria Pisanelli, Krishna C. Persaud∗
The University of Manchester, School of Chemical Engineering and Analytical Science, Faraday Building,
P.O. Box 88, Sackville Street, Manchester M60 1QD, UK
Received 11 July 2005; received in revised form 6 October 2005; accepted 7 December 2005
Available online 18 April 2006
Abstract
We report the development of an electronic nose based on an array of eight conducting polymer (CP) sensors for fire detection. Gas
chromatography–mass spectrometry (GC–MS) and Fourier transform infrared (FTIR) spectroscopy analyses of smoke from four test fires adapted
from the EN54 standard and of cigarette smoke, a main source of nuisance in fire detection, were used to identify chemical markers for each type
of smoke. Several sensors were tested individually with markers in order to select suitable sensors for the new array. Initial testing showed a broad
selectivity of the array to five markers from each type of smoke. The electronic nose was also able to discriminate all five fire and non-fire events
as demonstrated by principal component analysis (PCA). The results are promising and suggest that the e-nose technology is a good approach to
reduce false alarms in fire detection.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Electronic nose; Sensor array; Conducting polymers; Fire detection
1. Introduction
Fire detectors such as discrete heat, ionisation or optical fire
detectors are widely used in industrial premises. These detectors
use a single sensor and generally they are not able to discriminate
between real fires and nuisance alarms, caused for example by
aerosols or non-fire particulates. Fire brigades deplore a large
number of false alarms that can be costly in both financial and
human terms [1].
Sensor combinations including gas sensors and specific sig-
nalprocessingalgorithmshavebeenusedtoaddressthisproblem
[2–6]. For instance carbon monoxide sensors have been added to
standard fire detectors since CO is generated in most fires [2,3].
However all other sources of CO such as cigarette smoke or
diesel engines potentially cause false alarms. Another approach
is to measure volatile organic compounds (VOCs) in smoke as
they are produced in large numbers during combustion and can
constitute “fingerprints” of fires [7–10]. The most conventional
tool for VOC analysis is gas chromatography–mass spectrome-
try (GC–MS). Unfortunately this analysis is expensive to carry
out. Also it does not permit real time measurement hence it can-
∗ Corresponding author. Tel.: +44 161 306 4892; fax: +44 161 306 4911.
E-mail address: krishna.persaud@manchester.ac.uk (K.C. Persaud).
not be used as an early detection alarm device. Alternatively
VOCs could be measured at low cost using an “electronic nose
(e-nose)” with a dedicated sensor array. The use of gas sensors
based on metal oxide sensors have been commercialised previ-
ously(SamDetect,EADSRST,Rostock,Germany)fordetection
of fire in the early phase in applications such as tunnels, wood
and paper processing industry, stores, warehouses or silos with
highly combustible or valuable materials, museums, and exhi-
bitions [11]. The sensors in this case consisted of an array of six
chemicalgassensors(MOSthickfilmtechnology),togetherwith
a temperature and humidity sensor, with a power consumption of
8 W. Similarly the KAMINA system also based on metal oxide
technologyhasbeencommercialisedviatheForschungszentrum
Karlsruhe, Germany [12]. The idea of using hybrid sensing tech-
nology has been investigated and reviewed previously [13,14].
In this paper we report the development of an “electronic
nose (e-nose)” for fire detection based on an array of eight
sensors. This work is part of a European funded project investi-
gating practical solutions to reduce the number of false alarms
in large buildings or premises where high concentration of valu-
able items is present. The device could be used for instance in
an aspirating detector configuration. In such configuration air
is continuously aspirated from a large room or several rooms
through a custom-designed pipe layout to a single wall-mounted
enclosure containing the detection system.
0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.snb.2005.12.059

2. 56 E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61
One requirement of the new electronic nose is low power con-
sumption,asitshouldruncontinuouslyandremotelyfrommains
power or with a battery backup. Conducting polymers (CP) sen-
sors made from heterocyclic materials are therefore potentially
suitable as they can operate at room temperature. Also some con-
ducting polymer sensors have shown a good long-term stability,
and they are promising for continuous indoor air monitoring
[15].Thesesensorsshowreversiblechangesinconductancewith
adsorption and subsequent desorption of volatile molecules. The
chemical selectivity of individual polymers is broad, but arrays
of sensors that contain different functional groups on the surface
show different but overlapping responses to different chemical
families [16].
The development of the e-nose involved several steps includ-
ing: chemical analysis of smoke from common fires generated in
a purpose built benchtop fire cabinet in order to identify chemi-
cal markers, development of a new manufacturing procedure for
CP sensor arrays that is fairly simple and allows low cost pro-
duction, testing individual sensors with the identified markers
in order to select a set of suitable sensors for smoke detection,
and finally testing the new array with small scale test fires.
2. Experimental
2.1. Electronic nose and data acquisition
The prototype e-nose incorporated a microprocessor-based
system that measured sensor resistance changes. A micro-
controller collected real-time data from resistance interrogation
circuitry and controled a multiplexer, sequentially selecting dif-
ferent sensors from the array. Data were transferred to a host PC
via an RS232 serial communication protocol. Custom software
enabled collection and storage of data and provided immediate
analysis. A dedicated sampling system was built that included a
small pump, and filters to protect the pump and the sensors from
particulate materials in smoke.
2.2. Sensor array fabrication
The sensor array is shown in Fig. 1. The substrate used for
supporting the sensors is a 125 ␮m thick polyimide sheet bearing
eight pairs of interdigitated gold electrodes. Gold was deposited
by a chemical vapour deposition process onto the substrate. A
Cr layer was used to promote adhesion between the Au and
polyimide surfaces. A 15 ␮m thick patterned polyimide film was
used to encapsulate the gold tracks outside the sensor areas.
Eight conducting polymers, derivatives of polypyrrole and poly-
2,5-di(2-thienyl)pyrrole, were deposited electrochemically onto
the pairs of interdigitated electrodes (IDE). The thickness of
the polymer films was controlled by the electropolymerisation
charge passed [17] and is estimated to be approximately 3 ␮m
thick for the eight CP films. The films have a nodular structure
as shown by atomic force microscopy imaging (AFM), with
nodules diameter ranging typically from 150 to 400 nm (Fig. 2).
The substrate could be connected directly to printed circuit board
via standard flat flexible cable connectors.
Fig. 1. Sensor array (conducting polymers on polyimide substrate) used in the
e-nose. (a) Schematic diagram showing electrode layout; (b) photograph of final
array.
2.3. Chemical analysis and response time of electronic nose
Smoke was analysed using a portable Fourier transform
infrared spectrometer GASMET DX-4000 (Ansyco, Germany)
and a model Saturn 2200 gas chromatograph–mass spectrometer
(Varian, USA) equipped with a splitless injector, 30 m ZB1701
capillary column (Phenomenex, UK) and a flame ionisation
detector. Samples for GC–MS analysis were collected using
Fig. 2. AFM image of a polypyrrole-based CP sensor deposited onto polyimide
substrate.

3. E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61 57
Fig. 3. Experimental arrangement for characterisation of smoke and for assessing the response time of the e-nose.
solid phase micro-extraction (SPME) fibres. SPME fibres were
obtained from Supelco (UK) and have a 75 ␮m thick stationary
phase made of CarboxenTM/Polydimethylsiloxane.
The experimental set-up used for the chemical analysis of
smoke is illustrated in Fig. 3. The fire cabinet was designed
and built at the FH Nordostniedersachsen (Germany) and con-
sists of a 70 cm (h) × 70 cm (w) × 60 cm (d) metal cabinet fitted
with two 15 cm internal diameter exhaust pipes. Smoke could
be transferred by aspiration from the fire cabinet to one of the
exhaust pipes (shutter closed) via a 19 mm through hole in the
cabinet and then to the FTIR instrument through a 2 cm inter-
nal diameter pipe. The other exhaust pipe was equipped with
an extraction fan and was used to flush the cabinet after a fire
experiment had been carried out. Sample collection for GC–MS
analysis was carried out by inserting the SPME fibres through a
septum into the 2 cm pipe. Prior to each fire the FTIR spectrom-
eter was autocalibrated using dry nitrogen. The fire cabinet was
thoroughly cleaned between fires to avoid cross-contamination.
The opportunity was taken while the chemical analysis was
undertaken to assess the response time of the e-nose to var-
ious fires compared with that of a metal oxide CO sensor
(MGSM3003 from Microsens Products, Switzerland) and of a
commercial optical fire detector (Titanus Supersens from Wag-
ner GmbH, Germany). At this time the chemical composition
of gases emitted from different fires was still unknown and four
CP sensors that were not yet optimised for fire detection were
used. The CO sensor module, e-nose and optical detector were
arranged in series along a 2 cm internal diameter pipes as shown
in Fig. 3. Aspiration of smoke from the fire cabinet was provided
by the optical detector unit. Distances between the CO sensor
and the FTIR instrument, the electronic nose and the optical
detector were sufficiently short so that the time difference for
smoke to reach each measuring equipment, due to slight varia-
tion in the flows provided by the FTIR analyser and the built-in
aspiration fan of the optical detector, was negligible.
2.4. CP sensors selection
To select and optimise sensors for fire detection, a number
of CP sensors were individually interrogated with 10 ppm of a
marker from each test fire and nuisance. Chemical vapours were
generated by diluting a headspace of known concentration with
clean air using calibrated mass flow controllers and introduced
into a 100 mL glass container. The inlet of the e-nose was con-
nected to the glass container from where the chemical vapours
were sampled. The concentration of 10 ppm was chosen because
the FTIR analysis showed previously that the concentration of
most VOCs in the fire cabinet for the test fires under study was
within the range 1–20 ppm.
2.5. Electronic nose measurements
Fig. 4 shows the experimental set-up for measuring and
analysing smoke odours in the laboratory. For wood, cotton,
cigarette and paper the burning materials were placed in a glass
tube inside the oven where smoke was generated (arrangement
A). For flaming polyurethane, the oven was replaced by a 1 L
glass vessel (arrangement B). The advantage of using this new
experimental set-up instead of the fire cabinet used for the
chemical analysis was that smoke remained confined into glass
tubes that could be cleaned thoroughly more easily before each
new fire to remove odours from the previous fire. All measure-
ments were taken relative to ambient air and the electronic nose
could be interrogated to ambient air or smoke by switching
valve V1.
CP sensors based on polypyrrole materials are highly sen-
sitive to polar molecules and therefore to water vapour. Water
Fig. 4. Experimental arrangement for odour measurements.

4. 58 E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61
molecules have a better affinity with the conducting polymer
surfaces than other less polar molecules and in the presence of
a high level of humidity background the response to other com-
pounds can be masked. In order to minimise the effect of water
on the response of the CP sensors a 1 m long glass tube was
fitted between the oven and the e-nose inlet to allow some of
the water molecules from the fire to condense before reaching
the electronic nose. In the experimental conditions, the relative
humidity was measured at different locations inside the glass
pipes when a smouldering cotton fire was performed (measure-
ment taken 15 min after the beginning of the fire). The relative
humidity at the outlet of the glass tube located in the oven was
40.3% and the relative humidity at the inlet of the e-nose was
35.5%, for 35.1% relative humidity in the laboratory. All mea-
surements are relative to ambient air, hence in this case only a
0.4% relative humidity increase is seen by the sensors, which
has little effect on the sensors response. No attempt was made to
remove completely water from the system as water is a combus-
tion product and in order to keep representative samples water
should be taken into account in measurements. These conditions
are similar to a system where the electronic nose is embedded in
an aspiration smoke detector where some of the water molecules
would condense in the aspiration pipes before reaching the
detector.
2.6. Test fires for chemical analysis
The five test fires under study in this paper are smouldering
cotton, smouldering wood, smouldering paper, tobacco smoke
and flaming plastic. Wood, paper, cotton, polyurethane fires
were adapted from EN54 standard test fires. Cigarette smoke
was used because it is a common source of nuisance in fire
detection. For cotton, one wick 18 cm long (approximately
0.17 g) was used and ignited using a butane lighter. For wood
a 1 cm × 1 cm × 2 cm beech wood stick was placed on a hot-
plate that was heated at approximately 320 ◦C. For paper sixteen
sheets approximately 5 cm × 5 cm of printing paper (90 g m−3)
were stacked together and placed on a hotplate that was heated at
approximately 320 ◦C. For tobacco half a cigarette was used and
ignited using a butane lighter. For plastic a 4 cm × 2 cm × 40 cm
pieceofpolyurethanefoam(densityca.20 kg m−3)wasusedand
ignited using a butane lighter. The soft polyurethane foam did
not contain fire retardant additives.
2.7. Test fires for electronic nose measurements
For odour measurements the same fuel materials described in
the section above were used. Cotton, paper or wood were placed
in the glass tube inside the oven that was heated to 300 ◦C. Mea-
surements were begun 15 min after the oven was switched on.
For cigarette half a cigarette was ignited using a butane lighter
and placed in the glass tube inside the oven (oven off). For
polyurethane a piece of foam was ignited using a butane lighter
and placed in the glass vessel. In this case measurements were
taken when all the foam was consumed. All measurements were
performed with the dilution hole open (50% ambient air:50%
smoke).
Fig. 5. Example of gas and VOCs measurements obtained for smouldering wood
(a, fire starts; b, detection limit of optical detector; c, “fire alarm” according to
optical detector’s manufacturer).
3. Results and discussions
3.1. Response time of the electronic nose
The temporal responses of the CO sensor, the FTIR analyser
(for the five most abundant detected species), and four CP sen-
sors to smouldering wood were measured simultaneously, and
the resulting traces are shown in Fig. 5. The times when the
optical detector started to detect particulates (line b) and when
it raised a fire alarm (line c) are also indicated on the graphs.
The CP sensors started to respond approximately 350 s after
the fire began in the fire cabinet. This corresponds also to the
time when the five chemical species were detected by the FTIR
analyser, and when the first particulates were detected by the
optical detector. CO was detected after approximately 200 s. A
fire alarm is raised by the optical detector when the detection sig-
nal goes above a threshold set by the manufacturer. In this case
the fire alarm was raised by the optical detector approximately
600 s after wood was ignited in the fire cabinet. At this time
the amplitudes of response of the two most sensitive CP sen-
sors were significantly high, with typical resistance decreases of
−0.8 and −2%. Although CO is the fastest sensor to respond
to this particular fire, the results show that at the time when
the commercial optical detector raises an alarm, VOCs are also
present in measurable amounts in the pipe and that the electronic
nose respond significantly to these chemicals.
3.2. Identification of possible markers
The gas chromatograms for each test fire are showed in Fig. 6.
The presence of over a hundred chemical species in smoke for
each fire was confirmed.
The large differences observed between all chromatograms
shows that chemical compositions are sufficiently distinct to

5. E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61 59
Fig. 6. Gas chromatograms of smoke from test fires and nuisance.
allow identification of each fire or non-fire event. Using the data
from the GC–MS analysis together with the FTIR measurements
a number of potential markers were identified (Table 1). Each
marker is a chemical compound that is unique to smoke from one
type of fire, for the fires under study. Identification was achieved
by comparing the lists of the 80 most abundant VOCs identified
by GC–MS analysis and the 30 most abundant gases and VOCs
identified by the FTIR analyser, which were drawn for each fire.
It is worthwhile mentioning that the quantities of VOCs detected
by GC–MS analysis are dependent upon the selective affinity of
the absorptive layer of the SPME fibre with the volatile chemi-
cals. In other words the absorptive layer may be more efficient
in trapping some chemical species than others, the earliest being
not necessarily the most abundant species present in the sample.
Consequently this method of indentification of the most abun-
dant species by GC–MS was only approximate. Unfortunately
because of time constraints no quantitative analysis of markers
could be carried out by GC–MS analysis.
Table 1
Markers for test fires
Test fires Markers
Smouldering cotton Furfural, 2-(3H) Furanone
Smouldering wood Hexanoic acid, guaiacol and heptanoic acid
Smouldering paper Acetohydroxamic acid, hydrochloric acid
Cigarette smoke Pyridine, limonene, nicotine, isoprene, ammonia
Flaming polyurethane Benzonitrile, NO, NO2, N2O, HCN
Fig. 7. CP sensor response to 10 ppm hexanoic acid.
3.3. Sensors selection
The selection of conducting polymer materials for the sensor
array fabrication was made using the knowledge of the mark-
ers listed in Table 1 and our expertise in CP sensors. A number
of pre-selected sensors were individually interrogated with one
marker from each test fire. Eight sensors were then selected
based upon their high sensitivity to markers and their stability,
and upon the ability of the array to discriminate between the
various markers. The amplitude of response of the selected sen-
sors was typically in the range 1–10% change in resistance when
exposed to 10 ppm vapours. A typical response of a polypyrrole-
based sensor is shown in Fig. 7 for hexanoic acid. Response
patterns of markers were generated by averaging 100 data points
recorded every seconds with each sensor followed by normalisa-
tion so that the sum of responses of the eight sensors equals 100.
The ‘fingerprints’ obtained for five markers using the final array
are shown in Fig. 8. Distinct response patterns were obtained
for each markers, highlighting the broad selectivity of the
array.
3.4. Odour measurement results
The five test fires were generated as described in Section 2.7
and the electronic nose was interrogated for 180 s with each
type of smoke. Data was collected at 1 s intervals. An exam-
ple of temporal response is shown for smouldering wood in
Fig. 9. Averaged patterns of the test fires were obtained using
for each sensor the 100 data points selected between the two
cursors as shown in Fig. 9. Distinct response patterns or “fin-
gerprints” are observed for each fire (Fig. 10), which confirms
Fig. 8. Normalised response of e-nose to one marker from each test fire and
nuisance.

6. 60 E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61
Fig. 9. Example of time response of the e-nose to smoke emitted from smoul-
dering wood.
the broad selectivity of the sensor array to the various chemical
components of smoke. The patterns of the test fires are generally
different from the patterns of the markers. However some simi-
larities are noticeable between certain patterns (Figs. 8 and 10).
In particular for both ammonia and cigarette smoke sensors 3
and 5 produce a high positive response and for both furfural
and smouldering cotton sensors 1 and 7 produce a high nega-
tive response. When differences in patterns of fires and markers
are observed this is because when exposed to smoke the sensors
respond to a large variety of chemicals, not only the markers.
Generally, the sensors respond in a cumulative way to a large
amount of VOCs whereas in some cases sensors may respond
predominantly to a chemical species that is not a marker. Each
test fire was repeated 10 times over a period of 1 month. Fur-
thermore the odour of a headspace of 1% n-butanol in water was
measured five times (headspace kept at 20 ◦C) and used as stan-
dard. The 55 data sets were analysed using principal component
analysis (PCA). This linear unsupervised method is useful for a
graphic visualization of the differences of the data as the eight
dimensional information (from eight sensors) can be reduced
into two dimensions and then displayed in a two dimensions plot.
The PCA plot of the five test fires is shown in Fig. 11. The two
first principal components PC1 and PC2 accounted for 95.29%
of the total variance. It can be seen that all five fires are well dif-
ferentiated from each other. Although some clusters (paper and
polyurethane) almost overlapped cigarette smoke could well be
distinguished from all other fires. This is a significant result as
cigarette is one main source of nuisance in fire detection. This
can be explained by the presence of a large amount of ammonia
and amines in cigarette smoke [18] which are normaly easily
detected with polypyrrole-based sensors [19–23]. Especially in
Fig. 10. Normalised response of e-nose to each test fire and nuisance.
Fig. 11. PCA score plot for the test fires and nuisance.
our case two sensors of the array were selected because of their
high sensitivity to ammonia, which amongst the fires under study
was a marker for cigarette smoke. The typical range of change
in resistance of all sensors when exposed to smoke from wood,
paper, polyurethane and cotton fires were in the range 1–10%
but for cigarette changes of up to 25% was observed for these
two sensors. The good separation of cotton may be explained by
a high content of furfural in smoke from smouldering cotton but,
as explained earlier on, a quantitative analysis would be required
to confirm this result.
4. Conclusions
Smoke from four types of common fires and from cigarette
were analysed by GC–MS. The results showed that chemical
compositions are sufficiently distinct to allow identification of
each fire or non-fire event. Unfortunately GC–MS is too slow
and too expensive to be used as an advanced smoke detector.
Nevertheless, data from this analysis and FTIR analysis enabled
identification of markers that helped selecting CP sensors for the
fabrication of a new sensor array with broad selectivity to vari-
ous types of smokes. Results showed that the sensor array was
able to discriminate fires from cigarette smoke, a main source of
false alarms in fire detection. Further work will include testing
the long-term stability of the sensors during continuous mon-
itoring of a building. Further development is also required in
data processing to allow fast decision making, between fire or
false alarms, without compromising early detection of a fire.
Also in protective equipment, self-diagnostics of the sensor via-
bility and performances would be essential to ensure that the
instrument does not miss any fire. This is still an early stage in
the development of this e-nose but the results are very promis-
ing. They suggest that the electronic nose technology is a good
approach toward reducing false alarms in fire detection, and also
that conducting polymer sensors are potentially suitable for this
application.
Acknowledgements
This work was financially supported by the European Com-
mission in the frame of the project ‘Intelligent Modular Multi
Sensor Networked False Alarm Free Fire Detection System’
– IMOS – (IST 2001-38404). We are especially grateful for
the cooperation of CSEM (CH), General Paper Recycling Real
Estate and Hotel Company S.A. (GR), Microsens Products

7. E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61 61
SA (CH), OPTIS (F), WAGNER Alarm- und Sicherungssys-
teme GmbH (D), Tetide S.R.L. (I), Ungarisches Brandschutz-
und Sicherheitstechnisches Labor GmbH (HG), University of
L¨uneberg (D).
References
[1] S.L. Rose-Peherson, R.E. Shaffer, S.J. Hart, F.W. Williams, D.T. Got-
tuk, B.D. Strehlen, A. Hill, Multi-criteria fire detection systems using
a probabilistic neural network, Sens. Actuators, B: Chem. 69 (2000)
325–335.
[2] D.T. Gottuk, M.J. Peatross, R.J. Roby1, C.L. Beyler, Advanced fire
detection using multi-signature alarm algorithms, Fire Safety J. 37 (4)
(2002) 381–394.
[3] B. Peat, Fire detection without smoke, Phys. World 6 (1993) 23–25.
[4] B.C.B.C. Hagen, J.A. Milke, The use of gaseous fire signatures as a
mean to detect fires, Fire Safety J. 34 (1) (2000) 55–67.
[5] T. Amamoto, K. Tanaka, K. Takahata, S. Matsuura, T. Seiyama, A fire
detection experiment in a wooden house by SnO2 semiconductor gas
sensors, Sens. Actuators, B: Chem. 1 (1–6) (1990) 226–230.
[6] S. Aggarwal, V. Motevalli, Investigation of an approach to fuel inden-
tification for non-flaming sources using light-scattering and ionization
smoke detector reponse, Fire Safety J. 29 (1997) 99–112.
[7] G.A. Eiceman, A. Tarassov, P.A. Funk, S.E. Hughs, E.G. Nazarov,
R.A. Miller, Discrimination of combustion fuel sources using gas
chromatography–planar field asymmetric-waveform ion mobility spec-
trometry, J. Sep. Sci. 26 (2003) 585–593.
[8] C.C. Austin, D. Wang, D.J. Ecobichon, G. Dussault, Characterization of
volatile organic compounds in smoke at experimental fires, J. Toxicol.
Environ. Health-Part A 63 (3) (2001) 191–206.
[9] C.C. Austin, D. Wang, D.J. Ecobichon, G. Dussault, Characterization
of volatile organic compounds at municipal structural fires, J. Toxicol.
Environ. Health-Part A 63 (2001) 437–458.
[10] M. Rothberg, A. Heloma, J. Svinhufvud, E. Kahkonen, K. Reijula, Mea-
surement and analysis of nicotine and other VOCs in indoor air as an
indicator of passive smoking, Ann. Occup. Hyg. 42 (2) (1998) 129–134.
[11] H.J. Keding, L. Heckt, G. Borcherding, SamDetect, EIN intelligeter
Brand- und Gefahrstoffsensor AUBE ’99, in: Proceedings of the 11th
International Conference on Automatic Fire Detection, Duisburg, Ger-
many, March 16–18, 1999, pp. 226–236.
[12] M. Harms, J. Goschnick, R. Young, Early detection and distinction of
fire gases with a gas sensor microarray AUBE ’01, in: Proceedings of
the 12th International Conference on Automatic Fire Detection, National
Institute of Standards and Technology, Gaithersburg, MD, March 26–28,
2001, p. 416.
[13] G. Pfister, Multisensor/multicriteria fire detection: a new trend rapidly
becomes state of the Art, Fire Technol. Sec. Quart. 33 (2) (1997) 115.
[14] S.L. Rose-Pehrsson, S.J. Hart, T.T. Street, F.W. Williams, M.H. Ham-
mond, J.T. Wong, D.T. Gottuk, M.T. Wright, Early warning fire detection
system using a probabilistic neural network, Fire Technol. 39 (2003)
147–171.
[15] K.C. Persaud, A.M. Pisanelli, S. Szyszko, M. Reichlb, G. Horner,
W. Rakow, H.J. Keding, H. Heinrijk Wessels, A smart gas sensor
for monitoring environmental changes in closed systems: results from
the MIR space station, Sens. Actuators, B: Chem. 55 (2–3) (1999)
118–126.
[16] K.C. Persaud, Polymers for chemical sensing, Mater. Today 8 (4) (2005)
38–44.
[17] G. Garcia-Belmonte, Effect of electrode morphology on the diffusion
length of the doping process of electronically conducting polypyrrole
films, Electrochem. Commun. 5 (3) (2003) 236–240.
[18] K.R.R. Mahanama, J.M. Daisey, Volatile N-nitrosamines in environmen-
tal tobacco smoke: sampling, analysis, emission factors, and indoor air
exposure, Environ. Sci. Technol. 30 (1996) 1477–1484.
[19] Q. Ameer, S.B. Adeloju, Polypyrrole-based electronic noses for envi-
ronmental and industrial analysis, Sens. Actuators B 106 (2) (2000)
541–552.
[20] J. Yadong, W. Tao, W. Zhiming, L. Dan, C. Xiangdong, X. Dan, Study
on the NH3-gas sensitive properties and sensitive mechanism of polypyr-
role, Sens. Actuators B 66 (2000) 280–282.
[21] G. Gustafsson, I. Lundstr¨om, B. Liedberg, C.R. Wu, O.I. Wennerstr¨om,
The interaction between ammonia and poly(pyrrole), Synth. Met. 31
(1998) 163–179.
[22] I. L¨ahdesm¨aki, W.W. Kubiak, A. Lewenstam, A. Ivaska, Interferences in
a polypyrrole-based amperometric ammonia sensor, Talanta 52 (2000)
269–275.
[23] I. L¨ahdesm¨aki, A. Lewenstam, A. Ivaska, A polypyrrole-based ampero-
metric ammonia sensor, Talanta 52 (1996) 125–134.
Biographies
Emmanuel Scorsone received a diploma in Physical and Chemical Measure-
ments from the Institut Universitaire de Technologie in Lannion (France) in
1997, a BSc (Hons) in Chemistry and Instrumentation from Glasgow Caledo-
nian University in 1999, and PhD in Instrumentation and Analytical Science
from UMIST in 2002 where the main field of his scientific work was the
design of remote optical chemical alarm detectors. He has been a postdoc-
toral researcher at the University of Manchester between 2002 and 2005, and
his main research interest is the development of new chemical sensors for
environmental monitoring.
Krishna C. Persaud received his BSc (Hons) degree in Biochemistry from
the University of Newcastle-upon-Tyne, UK; MSc in Molecular Enzymology
from University of Warwick, UK in 1977; PhD in Olfactory Biochemistry
from the University of Warwick, UK in 1980 and has been a Fellow of
the Royal Society of Chemistry since 2002. He has research interests in the
area of olfaction from physiology to chemistry and has been involved in the
development of gas sensor arrays for sensing odours based on conducting
polymers. He has worked in olfactory research in Italy and the USA, and
was appointed as Lecturer, Department of Instrumentation and Analytical
Science, University of Manchester Institute of Science and Technology, UK
in 1988; as Senior Lecturer in 1992, and is currently a professor in the School
of Chemical Engineering and Analytical Science.