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 2005Available 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. Gaschromatography–mass spectrometry (GC–MS) and Fourier transform infrared (FTIR) spectroscopy analyses of smoke from four test fires adaptedfrom the EN54 standard and of cigarette smoke, a main source of nuisance in fire detection, were used to identify chemical markers for each typeof smoke. Several sensors were tested individually with markers in order to select suitable sensors for the new array. Initial testing showed a broadselectivity 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 eventsas demonstrated by principal component analysis (PCA). The results are promising and suggest that the e-nose technology is a good approach tor©

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educe false alarms in fire detection.2006 Elsevier B.V. All rights reserved.

eywords: Electronic nose; Sensor array; Conducting polymers; Fire detection

. Introduction

Fire detectors such as discrete heat, ionisation or optical fireetectors are widely used in industrial premises. These detectorsse a single sensor and generally they are not able to discriminateetween real fires and nuisance alarms, caused for example byerosols or non-fire particulates. Fire brigades deplore a largeumber of false alarms that can be costly in both financial anduman terms [1].

Sensor combinations including gas sensors and specific sig-al processing algorithms have been used to address this problem2–6]. For instance carbon monoxide sensors have been added totandard fire detectors since CO is generated in most fires [2,3].owever all other sources of CO such as cigarette smoke oriesel engines potentially cause false alarms. Another approachs to measure volatile organic compounds (VOCs) in smoke ashey are produced in large numbers during combustion and canonstitute “fingerprints” of fires [7–10]. The most conventionalool for VOC analysis is gas chromatography–mass spectrome-ry (GC–MS). Unfortunately this analysis is expensive to carryut. Also it does not permit real time measurement hence it can-

not be used as an early detection alarm device. AlternativelyVOCs could be measured at low cost using an “electronic nose(e-nose)” with a dedicated sensor array. The use of gas sensorsbased on metal oxide sensors have been commercialised previ-ously (SamDetect, EADS RST, Rostock, Germany) for detectionof fire in the early phase in applications such as tunnels, woodand paper processing industry, stores, warehouses or silos withhighly combustible or valuable materials, museums, and exhi-bitions [11]. The sensors in this case consisted of an array of sixchemical gas sensors (MOS thick film technology), together witha temperature and humidity sensor, with a power consumption of8 W. Similarly the KAMINA system also based on metal oxidetechnology has been commercialised via the ForschungszentrumKarlsruhe, 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 “electronicnose (e-nose)” for fire detection based on an array of eightsensors. This work is part of a European funded project investi-gating practical solutions to reduce the number of false alarmsin large buildings or premises where high concentration of valu-able items is present. The device could be used for instance in

∗ Corresponding author. Tel.: +44 161 306 4892; fax: +44 161 306 4911.E-mail address: krishna.persaud@manchester.ac.uk (K.C. Persaud).

an aspirating detector configuration. In such configuration airis continuously aspirated from a large room or several roomsthrough a custom-designed pipe layout to a single wall-mountedenclosure containing the detection system.

925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved.oi:10.1016/j.snb.2005.12.059

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, as it should run continuously and remotely from mainspower or with a battery backup. Conducting polymers (CP) sen-sors made from heterocyclic materials are therefore potentiallysuitable 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]. These sensors show reversible changes in conductance withadsorption and subsequent desorption of volatile molecules. Thechemical selectivity of individual polymers is broad, but arraysof sensors that contain different functional groups on the surfaceshow different but overlapping responses to different chemicalfamilies [16].

The development of the e-nose involved several steps includ-ing: chemical analysis of smoke from common fires generated ina purpose built benchtop fire cabinet in order to identify chemi-cal markers, development of a new manufacturing procedure forCP sensor arrays that is fairly simple and allows low cost pro-duction, testing individual sensors with the identified markersin 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

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Fig. 1. Sensor array (conducting polymers on polyimide substrate) used in thee-nose. (a) Schematic diagram showing electrode layout; (b) photograph of finalarray.

2.3. Chemical analysis and response time of electronic nose

Smoke was analysed using a portable Fourier transforminfrared spectrometer GASMET DX-4000 (Ansyco, Germany)and a model Saturn 2200 gas chromatograph–mass spectrometer(Varian, USA) equipped with a splitless injector, 30 m ZB1701capillary column (Phenomenex, UK) and a flame ionisationdetector. Samples for GC–MS analysis were collected using

Fig. 2. AFM image of a polypyrrole-based CP sensor deposited onto polyimides

The prototype e-nose incorporated a microprocessor-basedystem that measured sensor resistance changes. A micro-ontroller collected real-time data from resistance interrogationircuitry and controled a multiplexer, sequentially selecting dif-erent sensors from the array. Data were transferred to a host PCia an RS232 serial communication protocol. Custom softwarenabled collection and storage of data and provided immediatenalysis. A dedicated sampling system was built that included amall pump, and filters to protect the pump and the sensors fromarticulate materials in smoke.

.2. Sensor array fabrication

The sensor array is shown in Fig. 1. The substrate used forupporting the sensors is a 125 �m thick polyimide sheet bearingight pairs of interdigitated gold electrodes. Gold was depositedy a chemical vapour deposition process onto the substrate. Ar layer was used to promote adhesion between the Au andolyimide surfaces. A 15 �m thick patterned polyimide film wassed to encapsulate the gold tracks outside the sensor areas.ight conducting polymers, derivatives of polypyrrole and poly-,5-di(2-thienyl)pyrrole, were deposited electrochemically ontohe pairs of interdigitated electrodes (IDE). The thickness ofhe polymer films was controlled by the electropolymerisationharge passed [17] and is estimated to be approximately 3 �mhick for the eight CP films. The films have a nodular structures shown by atomic force microscopy imaging (AFM), withodules diameter ranging typically from 150 to 400 nm (Fig. 2).he substrate could be connected directly to printed circuit boardia standard flat flexible cable connectors.

ubstrate.

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 wereobtained from Supelco (UK) and have a 75 �m thick stationaryphase made of CarboxenTM/Polydimethylsiloxane.

The experimental set-up used for the chemical analysis ofsmoke is illustrated in Fig. 3. The fire cabinet was designedand built at the FH Nordostniedersachsen (Germany) and con-sists of a 70 cm (h) × 70 cm (w) × 60 cm (d) metal cabinet fittedwith two 15 cm internal diameter exhaust pipes. Smoke couldbe transferred by aspiration from the fire cabinet to one of theexhaust pipes (shutter closed) via a 19 mm through hole in thecabinet and then to the FTIR instrument through a 2 cm inter-nal diameter pipe. The other exhaust pipe was equipped withan extraction fan and was used to flush the cabinet after a fireexperiment had been carried out. Sample collection for GC–MSanalysis was carried out by inserting the SPME fibres through aseptum into the 2 cm pipe. Prior to each fire the FTIR spectrom-eter was autocalibrated using dry nitrogen. The fire cabinet wasthoroughly cleaned between fires to avoid cross-contamination.

The opportunity was taken while the chemical analysis wasundertaken 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 acommercial optical fire detector (Titanus Supersens from Wag-ner GmbH, Germany). At this time the chemical compositionof gases emitted from different fires was still unknown and fourCP sensors that were not yet optimised for fire detection wereuaibadsta

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generated by diluting a headspace of known concentration withclean air using calibrated mass flow controllers and introducedinto a 100 mL glass container. The inlet of the e-nose was con-nected to the glass container from where the chemical vapourswere sampled. The concentration of 10 ppm was chosen becausethe FTIR analysis showed previously that the concentration ofmost VOCs in the fire cabinet for the test fires under study waswithin the range 1–20 ppm.

2.5. Electronic nose measurements

Fig. 4 shows the experimental set-up for measuring andanalysing smoke odours in the laboratory. For wood, cotton,cigarette and paper the burning materials were placed in a glasstube inside the oven where smoke was generated (arrangementA). For flaming polyurethane, the oven was replaced by a 1 Lglass vessel (arrangement B). The advantage of using this newexperimental set-up instead of the fire cabinet used for thechemical analysis was that smoke remained confined into glasstubes that could be cleaned thoroughly more easily before eachnew fire to remove odours from the previous fire. All measure-ments were taken relative to ambient air and the electronic nosecould be interrogated to ambient air or smoke by switchingvalve V1.

CP sensors based on polypyrrole materials are highly sen-sitive to polar molecules and therefore to water vapour. Water

sed. The CO sensor module, e-nose and optical detector wererranged in series along a 2 cm internal diameter pipes as shownn Fig. 3. Aspiration of smoke from the fire cabinet was providedy the optical detector unit. Distances between the CO sensornd the FTIR instrument, the electronic nose and the opticaletector were sufficiently short so that the time difference formoke to reach each measuring equipment, due to slight varia-ion in the flows provided by the FTIR analyser and the built-inspiration fan of the optical detector, was negligible.

.4. CP sensors selection

To select and optimise sensors for fire detection, a numberf CP sensors were individually interrogated with 10 ppm of aarker from each test fire and nuisance. Chemical vapours were

Fig. 4. Experimental arrangement for odour measurements.

58 E. Scorsone et al. / Sensors and Actuators B 116 (2006) 55–61

molecules have a better affinity with the conducting polymersurfaces than other less polar molecules and in the presence ofa high level of humidity background the response to other com-pounds can be masked. In order to minimise the effect of wateron the response of the CP sensors a 1 m long glass tube wasfitted between the oven and the e-nose inlet to allow some ofthe water molecules from the fire to condense before reachingthe electronic nose. In the experimental conditions, the relativehumidity was measured at different locations inside the glasspipes when a smouldering cotton fire was performed (measure-ment taken 15 min after the beginning of the fire). The relativehumidity at the outlet of the glass tube located in the oven was40.3% and the relative humidity at the inlet of the e-nose was35.5%, for 35.1% relative humidity in the laboratory. All mea-surements are relative to ambient air, hence in this case only a0.4% relative humidity increase is seen by the sensors, whichhas little effect on the sensors response. No attempt was made toremove completely water from the system as water is a combus-tion product and in order to keep representative samples watershould be taken into account in measurements. These conditionsare similar to a system where the electronic nose is embedded inan aspiration smoke detector where some of the water moleculeswould condense in the aspiration pipes before reaching thedetector.

2.6. Test fires for chemical analysis

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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 tooptical 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, andthe resulting traces are shown in Fig. 5. The times when theoptical detector started to detect particulates (line b) and whenit raised a fire alarm (line c) are also indicated on the graphs.The CP sensors started to respond approximately 350 s afterthe fire began in the fire cabinet. This corresponds also to thetime when the five chemical species were detected by the FTIRanalyser, and when the first particulates were detected by theoptical detector. CO was detected after approximately 200 s. Afire alarm is raised by the optical detector when the detection sig-nal goes above a threshold set by the manufacturer. In this casethe fire alarm was raised by the optical detector approximately600 s after wood was ignited in the fire cabinet. At this timethe 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 respondto this particular fire, the results show that at the time whenthe commercial optical detector raises an alarm, VOCs are alsopresent in measurable amounts in the pipe and that the electronicnose respond significantly to these chemicals.

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The five test fires under study in this paper are smoulderingotton, smouldering wood, smouldering paper, tobacco smokend flaming plastic. Wood, paper, cotton, polyurethane firesere adapted from EN54 standard test fires. Cigarette smokeas used because it is a common source of nuisance in fireetection. For cotton, one wick 18 cm long (approximately.17 g) was used and ignited using a butane lighter. For wood1 cm × 1 cm × 2 cm beech wood stick was placed on a hot-

late that was heated at approximately 320 ◦C. For paper sixteenheets approximately 5 cm × 5 cm of printing paper (90 g m−3)ere stacked together and placed on a hotplate that was heated at

pproximately 320 ◦C. For tobacco half a cigarette was used andgnited using a butane lighter. For plastic a 4 cm × 2 cm × 40 cmiece of polyurethane foam (density ca. 20 kg m−3) was used andgnited using a butane lighter. The soft polyurethane foam didot contain fire retardant additives.

.7. Test fires for electronic nose measurements

For odour measurements the same fuel materials described inhe section above were used. Cotton, paper or wood were placedn the glass tube inside the oven that was heated to 300 ◦C. Mea-urements were begun 15 min after the oven was switched on.or cigarette half a cigarette was ignited using a butane lighternd placed in the glass tube inside the oven (oven off). Forolyurethane a piece of foam was ignited using a butane lighternd placed in the glass vessel. In this case measurements wereaken when all the foam was consumed. All measurements wereerformed with the dilution hole open (50% ambient air:50%moke).

.2. Identification of possible markers

The gas chromatograms for each test fire are showed in Fig. 6.he presence of over a hundred chemical species in smoke forach fire was confirmed.

The large differences observed between all chromatogramshows that chemical compositions are sufficiently distinct to

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 datafrom the GC–MS analysis together with the FTIR measurementsa number of potential markers were identified (Table 1). Eachmarker is a chemical compound that is unique to smoke from onetype of fire, for the fires under study. Identification was achievedby comparing the lists of the 80 most abundant VOCs identifiedby GC–MS analysis and the 30 most abundant gases and VOCsidentified by the FTIR analyser, which were drawn for each fire.It is worthwhile mentioning that the quantities of VOCs detectedby GC–MS analysis are dependent upon the selective affinity ofthe absorptive layer of the SPME fibre with the volatile chemi-cals. In other words the absorptive layer may be more efficientin trapping some chemical species than others, the earliest beingnot 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. Unfortunatelybecause of time constraints no quantitative analysis of markerscould be carried out by GC–MS analysis.

Table 1Markers for test fires

Test fires Markers

Smouldering cotton Furfural, 2-(3H) FuranoneSmouldering wood Hexanoic acid, guaiacol and heptanoic acidSmouldering paper Acetohydroxamic acid, hydrochloric acidCF

Fig. 7. CP sensor response to 10 ppm hexanoic acid.

3.3. Sensors selection

The selection of conducting polymer materials for the sensorarray fabrication was made using the knowledge of the mark-ers listed in Table 1 and our expertise in CP sensors. A numberof pre-selected sensors were individually interrogated with onemarker from each test fire. Eight sensors were then selectedbased upon their high sensitivity to markers and their stability,and upon the ability of the array to discriminate between thevarious markers. The amplitude of response of the selected sen-sors was typically in the range 1–10% change in resistance whenexposed to 10 ppm vapours. A typical response of a polypyrrole-based sensor is shown in Fig. 7 for hexanoic acid. Responsepatterns of markers were generated by averaging 100 data pointsrecorded 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 arrayare shown in Fig. 8. Distinct response patterns were obtainedfor each markers, highlighting the broad selectivity of thearray.

3.4. Odour measurement results

The five test fires were generated as described in Section 2.7and the electronic nose was interrogated for 180 s with eachtype of smoke. Data was collected at 1 s intervals. An exam-pFfcg

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igarette smoke Pyridine, limonene, nicotine, isoprene, ammonialaming polyurethane Benzonitrile, NO, NO2, N2O, HCN

le of temporal response is shown for smouldering wood inig. 9. Averaged patterns of the test fires were obtained usingor each sensor the 100 data points selected between the twoursors as shown in Fig. 9. Distinct response patterns or “fin-erprints” are observed for each fire (Fig. 10), which confirms

ig. 8. Normalised response of e-nose to one marker from each test fire anduisance.

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 chemicalcomponents of smoke. The patterns of the test fires are generallydifferent 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 3and 5 produce a high positive response and for both furfuraland smouldering cotton sensors 1 and 7 produce a high nega-tive response. When differences in patterns of fires and markersare observed this is because when exposed to smoke the sensorsrespond to a large variety of chemicals, not only the markers.Generally, the sensors respond in a cumulative way to a largeamount of VOCs whereas in some cases sensors may respondpredominantly to a chemical species that is not a marker. Eachtest fire was repeated 10 times over a period of 1 month. Fur-thermore the odour of a headspace of 1% n-butanol in water wasmeasured five times (headspace kept at 20 ◦C) and used as stan-dard. The 55 data sets were analysed using principal componentanalysis (PCA). This linear unsupervised method is useful for agraphic visualization of the differences of the data as the eightdimensional information (from eight sensors) can be reducedinto two dimensions and then displayed in a two dimensions plot.The PCA plot of the five test fires is shown in Fig. 11. The twofirst 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 andpolyurethane) almost overlapped cigarette smoke could well bedccad

Fig. 11. PCA score plot for the test fires and nuisance.

our case two sensors of the array were selected because of theirhigh sensitivity to ammonia, which amongst the fires under studywas a marker for cigarette smoke. The typical range of changein 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 thesetwo sensors. The good separation of cotton may be explained bya high content of furfural in smoke from smouldering cotton but,as explained earlier on, a quantitative analysis would be requiredto confirm this result.

4. Conclusions

Smoke from four types of common fires and from cigarettewere analysed by GC–MS. The results showed that chemicalcompositions are sufficiently distinct to allow identification ofeach fire or non-fire event. Unfortunately GC–MS is too slowand too expensive to be used as an advanced smoke detector.Nevertheless, data from this analysis and FTIR analysis enabledidentification of markers that helped selecting CP sensors for thefabrication of a new sensor array with broad selectivity to vari-ous types of smokes. Results showed that the sensor array wasable to discriminate fires from cigarette smoke, a main source offalse alarms in fire detection. Further work will include testingthe long-term stability of the sensors during continuous mon-itoring of a building. Further development is also required indfAbitiata

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istinguished from all other fires. This is a significant result asigarette is one main source of nuisance in fire detection. Thisan be explained by the presence of a large amount of ammoniand amines in cigarette smoke [18] which are normaly easilyetected with polypyrrole-based sensors [19–23]. Especially in

Fig. 10. Normalised response of e-nose to each test fire and nuisance.

ata processing to allow fast decision making, between fire oralse alarms, without compromising early detection of a fire.lso in protective equipment, self-diagnostics of the sensor via-ility and performances would be essential to ensure that thenstrument does not miss any fire. This is still an early stage inhe development of this e-nose but the results are very promis-ng. They suggest that the electronic nose technology is a goodpproach toward reducing false alarms in fire detection, and alsohat conducting polymer sensors are potentially suitable for thispplication.

cknowledgements

This work was financially supported by the European Com-ission in the frame of the project ‘Intelligent Modular Multiensor Networked False Alarm Free Fire Detection System’IMOS – (IST 2001-38404). We are especially grateful for

he cooperation of CSEM (CH), General Paper Recycling Realstate and Hotel Company S.A. (GR), Microsens Products

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 ofLuneberg (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 usinga 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 firedetection 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 gassensors, 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 ionizationsmoke 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 gaschromatography–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 ofvolatile organic compounds in smoke at experimental fires, J. Toxicol.

[

[

[

[

[

[15] K.C. Persaud, A.M. Pisanelli, S. Szyszko, M. Reichlb, G. Horner,W. Rakow, H.J. Keding, H. Heinrijk Wessels, A smart gas sensorfor monitoring environmental changes in closed systems: results fromthe 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 diffusionlength of the doping process of electronically conducting polypyrrolefilms, 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 airexposure, 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, Studyon the NH3-gas sensitive properties and sensitive mechanism of polypyr-role, Sens. Actuators B 66 (2000) 280–282.

[21] G. Gustafsson, I. Lundstrom, B. Liedberg, C.R. Wu, O.I. Wennerstrom,The interaction between ammonia and poly(pyrrole), Synth. Met. 31(1998) 163–179.

[22] I. Lahdesmaki, W.W. Kubiak, A. Lewenstam, A. Ivaska, Interferences ina polypyrrole-based amperometric ammonia sensor, Talanta 52 (2000)269–275.

[23] I. Lahdesmaki, A. Lewenstam, A. Ivaska, A polypyrrole-based ampero-metric ammonia sensor, Talanta 52 (1996) 125–134.

Biographies

Em1nfdthe

KtfftadpwSio

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 anindicator of passive smoking, Ann. Occup. Hyg. 42 (2) (1998) 129–134.

11] H.J. Keding, L. Heckt, G. Borcherding, SamDetect, EIN intelligeterBrand- und Gefahrstoffsensor AUBE ’99, in: Proceedings of the 11thInternational 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 offire gases with a gas sensor microarray AUBE ’01, in: Proceedings ofthe 12th International Conference on Automatic Fire Detection, NationalInstitute of Standards and Technology, Gaithersburg, MD, March 26–28,2001, p. 416.

13] G. Pfister, Multisensor/multicriteria fire detection: a new trend rapidlybecomes 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 detectionsystem using a probabilistic neural network, Fire Technol. 39 (2003)147–171.

mmanuel Scorsone received a diploma in Physical and Chemical Measure-ents from the Institut Universitaire de Technologie in Lannion (France) in

997, a BSc (Hons) in Chemistry and Instrumentation from Glasgow Caledo-ian University in 1999, and PhD in Instrumentation and Analytical Sciencerom UMIST in 2002 where the main field of his scientific work was theesign of remote optical chemical alarm detectors. He has been a postdoc-oral researcher at the University of Manchester between 2002 and 2005, andis main research interest is the development of new chemical sensors fornvironmental monitoring.

rishna C. Persaud received his BSc (Hons) degree in Biochemistry fromhe University of Newcastle-upon-Tyne, UK; MSc in Molecular Enzymologyrom University of Warwick, UK in 1977; PhD in Olfactory Biochemistryrom the University of Warwick, UK in 1980 and has been a Fellow ofhe Royal Society of Chemistry since 2002. He has research interests in therea of olfaction from physiology to chemistry and has been involved in theevelopment of gas sensor arrays for sensing odours based on conductingolymers. He has worked in olfactory research in Italy and the USA, andas appointed as Lecturer, Department of Instrumentation and Analyticalcience, University of Manchester Institute of Science and Technology, UK

n 1988; as Senior Lecturer in 1992, and is currently a professor in the Schoolf Chemical Engineering and Analytical Science.