Sensors and Actuators B, 18-19 (1994) 211-220

A brief history of electronic noses*

Julian W. Gardner**Centre for Nanotechnology and Microen8ineerin& Department of Engineerin& University of Warwick, Caventy CV4 7AL (UK)

Philip N. BartlettDepartment of Chemistry, University of Southampton, Southampton, S09 5NH (UK)

Abstract

The human nose is still the primary `instrument' used to assess the smell or flavour of various industrial productstoday, despite considerable and sustained attempts to develop new electronic instrumentation capable of mimickingits remarkable ability. In this paper we review the research effort that has been carried out over the past 25years or so to create an electronic nose . In doing so, we first provide a definition for the term electronic nose,and then discuss some of the technologies that have been explored in what is essentially an intelligent chemicalarray sensor system. Finally, we summarize the applications of electronic noses to date and suggest where futureapplications may lie.

1. Introduction

1.1. The human olfactory system and odoursThere are three sensory systems in humans which

contribute to the sensation of flavour . These threechemical senses are olfaction (the sense of smell),gustation (the sense of taste) and the trigeminal sense(responsive to irritant chemical species) . However, smellis the dominant factor in our sensation of flavour andso can often be used alone to profile the flavour ofvarious products. The sense of smell arises from thestimulation of the human olfactory system by odorantmolecules emitted from an object, e.g., a rose. Figure1(a) shows the location of the olfactory system in thehuman nose [1]. The odorant molecules produced bythe rose are drawn up into the nasal cavity and acrossthe olfactory area (epithelium) below the olfactory bulbas shown. Figure 1(b) shows a schematic diagram ofthe olfactory pathway from the epithelium to the brain[2] . First there is a thin aqueous mucus layer into whichextend the olfactory hairs or cilia from olfactory cells .G-receptor binding proteins are located at the surfaceof the cilia and act as chemosensory receptors . Thereare believed to be a relatively small number of receptorproteins (=100) so that the receptor cells have partiallyoverlapping sensitivities. There are about 100 millionolfactory cells (50 million per nostril), which are believedboth' to amplify the signal and generate secondary

*Invited paper."Author to whom correspondence should be addressed .

0925-4005/94/$07 .00 0 1994 Elsevier Sequoia . All rights reservedSSDI 0925-4005(93)01139-U

211

messengers. The messengers control ion channels andthus generate signals that travel down axons from theolfactory nerves to about 5000 glomeruli nodes in theolfactory bulb . These signals are then further processedby about 100 000 mitral cells and then finally sent viaa granular cell layer to the brain . Our understandingof the olfactory process has increased rapidly duringthe past decade [3] and attempts have been made tomodel this process [4], but the precise details are stillunknown. The performance of the human olfactorysystem is rather remarkable . The olfactory receptorcells are believed to have a low sensitivity (t ppm), alow specificity and only live on average for about 22days. Yet the subsequent neural processing enhancessensitivity by about three orders of magnitude, removesdrift and provides discrimination between several thou-sand odours.Odorant molecules are typically hydrophobic and

polar with molecular masses of up to about 300 Da .A simple odour is a single molecule and some examplesare listed in Table 1, together with the type of odourand its olfactory threshold [5]. In reality most naturalsmells, perfumes and flavours are complex mixtures ofchemical species and so contain hundreds, if not thou-sands, of constituents. Often subtle differences in therelative amounts of these constituents determine thesmell of the product. For example, Table 2 shows thesixteen most common classes of flavour constituent ofcoffee [5] . It is then the sensory impact of theseconstituents that is important in olfaction .

212

(a)

OLFACTORY BULB

OLFACTORY AREA

TURBINATE BONES

Chemical structure

DiacetylTmns-2-hexenalGeraniol5-Isopropyl-2-methylpbenolLimoneneCis-4-heptenalOcta-1,5-diene-3-one2-Isobutyl-3-methoxypyrazinea-Terpinethiol

(b)

Fig. 1. (a) Anatomy of the human olfactory system [1] and (b)schematic diagram of the olfactory pathway [2] .

1.2. Machine olfactionThe human nose is still the primary `instrument' used

in many industries to evaluate the smell or flavour ofproducts such as perfumes (cosmetics, soaps, etc .),foodstuffs (fish, meat, cheese, etc .) and beverages (beer,whisky, coffee, etc .). This is a costly process, becausetrained panels of experts are required who can onlywork for relatively short periods of time . The physi-

TABLE 1. Some common simple odours (from ref. 5)

cochemical properties of the products are also measuredusing conventional analytical equipment, such as gaschromatography and gas chromatography-mass spec-trometry. These (mechanical) methods are not onlytime-consuming but the results are often inadequate .For example, there are some key flavour constituentsof beer that are below the detection limits of most gaschromatographs [6]. Moreover, the relationship betweenthe physicochemical properties of the odorant moleculesand their sensory impact is still unclear, in spite of aconsiderable amount of research effort [7] . Consequentlythere is enormous demand for an electronic instrumentthat can mimic the human sense of smell and providelow-cost and rapid sensory information.

The earliest work on the development of an instru-ment specifically to detect odours probably dates backto Moncrieff in 1961 [8] . This was really a mechanicalnose and the first electronic noses were reported byWilkens and Hatman [9] in 1964 (redox reactions ofodorants at an electrode), Buck et al. [10] (modulationof conductivity by odorants) and Dravieks and Trotter[11] (modulation of contact potential by odorants), bothin 1965. However, arguably, the concept of an electronicnose as an intelligent chemical array sensor system forodour classification did not really emerge until nearly20 years later from publications by Persaud and Dodd(1982) at Warwick University in the UK [12] and Ikegamiet al . (1985, 1987) at Hitachi in Japan [13, 14] . Theterm `electronic nose' appeared around the late 1980s,when it was specifically used at a conference in 1987[15] . Then in 1989 a session at a NATO AdvancedWorkshop on Chemosensory Information Processingwas dedicated to the topic of artificial olfaction [3] andthe design of an artificial olfactory system was furtherestablished [16] . Finally, the first conference dedicatedto the topic of electronic noses was held in 1990 [17] .

The term electronic nose has yet to be defined andthus enter into common parlance, as at present thereare various synonyms such as artificial nose, mechanicalnose, odour-sensing system . Therefore, for the purposeof this review paper, we use the following definition :

Odour type

Threshold (ppb in water)

off-flavour of beer 500green leaves 316rose 290thyme 86lemons 10off-flavour of white fish 0.04off-flavour of butter 0.01green peppers 0.002grapefruit

0.00002

TABLE 2. Constituents of an example of a complex odour : coffee(from ref. 5)

An electronic nose is an instrument, which comprises anarray of electronic chemical sensors with partial specificityand an appropriate pattern-recognition system, capable ofrecognising simple or complex odours.

This definition restricts the term electronic nose tothose types of intelligent chemical array sensor systemsor chemical sensoric array devices (ChemSADs) thatare specifically used to sense odorant molecules in ananalogue to the human nose . However, the architectureof an electronic nose also applies in the field of gassensing for the detection of individual components ormixtures of gases/vapours. There was considerable ac-tivity in this related area in the 1970s and 1980s, andinterested readers are referred to two recent reviews[18, 19] .This paper continues in Section 2 with a review of

the different technologies and techniques that havebeen used to design electronic noses . This is followedby a discussion of the applications that have beenreported, commercial instrumentation available todayand the outlook for the future .

2. Electronic nose technology

2.1. SensorsFigure 2 shows the generic architecture of an elec-

tronic nose. An odour j is presented to the activematerial of a sensor i, which converts a chemical inputinto an electrical signal. There are many types of sensorthat have been developed to detect specific gases andvapours since the 1970s, as illustrated in Table 3 .However, the requirement for the sensors in an elec-tronic nose is that they have a partial sensitivity, i.e .,that they can respond broadly to a range or class of

213

gases rather than to a specific one . Of course, this isthe opposite of the ideal gas sensor, which shouldrespond to only one gas, e.g., methane, and provide aunique output. However, with an electronic nose, likethe human nose, we wish to identify many odours thatmay contain hundreds of individual chemical compo-nents. Thus we need a sensor which can generalize atthe molecular level . Fortuitously many of these gassensors lack the specificity originally hoped for and sothey can be exploited as components within an electronicnose. Table 4 lists the types of multisensor arrays thathave been used so far, together with the number ofsensing elements and where the work originated.

Considerable research effort has been directed tothe use of inorganic semiconducting materials such asoxides and catalytic metals. With oxide materials, theodorant molecules react with chemisorbed oxygen spe-cies and thus modulate the conductivity . These devicesoperate at elevated temperatures (e.g ., 100-600 °C) andthick-film versions are commercially available, see Fig .3(a) and (b) [20] . They are quite sensitive to combustiblematerials (0.1-100 ppm) such as alcohols, but aregenerally poor at detecting sulphur- or nitrogen-basedodours. Integrated thin-film versions have been madewith a lower power consumption per device, but theseare generally more expensive to make and tend tosuffer from poor stability . Although there are someoxide materials that show a good specificity to certainodours (e.g., TiO2/Ru for trimethylamine [21]), thereare several potential advantages to employing organicmaterials in electronic noses. First, there is a muchwider choice of materials, but more importantly func-tional groups that interact with different classes ofodorant molecules can be built into the active material .The two main examples are conducting polymers, suchas poly(pyrrole) and poly(aniline), and biological lipidcoatings, see Fig . 3(c) and (d) [20] . These sensors canbe operated close to room temperature (20-60 °C) andhave a sensitivity of typically 0 .1-100 ppm (conductingpolymer chemoresistors and SAW devices) . Moreover,organic materials tend to be easier to process thanoxides; methods used can be electrochemical (elec-troactive polymers), spin-coating (polymers), screen-printing (phthalocyanines) and Langmuir-Blodgett(lipid coatings, phthalocyanines) .

2.2. Signal preparationThe individual sensors i within the electronic nose

produce a time-dependent electrical signal V1(t) inresponse to an odour j. The rise and decay time ofthe sensor signal will depend upon one or more of thefollowing parameters :

(1) the flow delivery system that carries the odourfrom the source to the sensor array, e.g., the flowprofile and type of carrier gas ;

Class of chemical species Number in class

Furans 108Pyrazines 79Pyrroles 74Ketones 70Phenols 44Hydrocarbons 31Esters 30Aldehydes 28Oxazoles 28Thiazoles 27Thiophenes 26Amines 21Acids 20Alcohols 19Pyridines 13Thiols 13

214

INPUTODOUR(j)

Active material

ACTIVE

MATERIALSENSOR

SENSOR

PROCESSOR

Sintered metal oxideCatalytic metalLipid layersPhthalocyaninesConducting polymerElectrochemicalCatalytic gateOrganic semiconductors

Fig. 2. Generic architecture of an electronic nose.

TABLE 3. Common sensors for detecting gases and vapours

ACTIVE

MATERIALSENSOR

SENSOR

PROCESSOR

ACTIVE

MATERIALSENSOR

SENSOR

PROCESSOR

Sensor type

(2) the nature of the odour, e.g., type, concentration ;(3) the reaction kinetics of the odour and the active

material ;(4) the diffusion of the odour within the active

material ;(5) the nature of the sensing material, e.g., physical

structure, porosity, thermal time-constant;(6) the nature of the substrate supporting the active

material, e.g., thermal conductivity, acoustic impedance ;(7) ambient conditions, e .g., temperature of active

material, carrier gas, humidity, pressure .To date no use has been made of the transient

information in the sensor signal by appropriate pro-cessing. A variety of steady-state models have beenused to process odour and gas sensor signals, as il-lustrated in Table 5. There is some evidence that therelative and fractional difference models help com-

chemoresistorthermal, e .g., pellistoracoustic, e.g., piezoelectric/SAWchemoresistorchemoresistorpotentiometric/amperometricpotentiometric, e.g., Pd-MOSFEToptical (e.g., IR absorption)

Train

KNOWLEDGE

BASE

ik cr...y

A

PARC

ENGINE

Tat

Typical target gases

OUTPUTPREDICTOR(Class j )

combustible gasescombustible gasesorganicsNO., H2, NH3NH3, alcoholsNH3, CO, CH3CH2OHcombustible gasesCH4, CO2, NO,

pensate for the temperature sensitivity of the sensor .Moreover, a fractional difference model (conductance)linearizes the mechanism that generates a concentrationdependence in metal oxide chemoresistors [35] . Log-arithmic analysis can be used to linearize a highly non-linear concentration dependency .

The response from an array made up of n sensorsis a vector X, which can be written as

Figure 4(a) shows a general representation of sensorspace (n = 3) with the response vectors for three samplesof odour j and odour j' sketched out . The scale of theindividual axes, x j, is sometimes normalized (see Table5) so that the output from the individual sensors liesin the range [0, 1]. This procedure simply conditionsthe data so that all elements in the response vectors

TABLE 4. Main types of gas array sensors used in electronicnoses

are of the same magnitude and, thereby, either reducescomputational errors in a chemometric classifier orprepares the data for the appropriate input space ina neural classifier. Normalizing the sensor output is ineffect setting the gain and should not enhance dis-crimination of different odours . It is also possible tonormalize across the whole array of sensors . This setsthe length of all response vectors to unity and putsthe vectors on the surface of a unit hypersphere asshown in Fig . 4(b) . This method is particularly usefulwhen the sample concentration is of no interest butfine discrimination is required . The normalization pro-cess ameliorates any experimental variation in the sam-ple concentration and has been used in classifyingresponses from metal oxide and polymer arrays . How-ever, the method increases the noise in weaker signals .

2.3. Pattern-recognition techniquesThe response vectors generated by the sensor array

are then analysed using a pattern-recognition (PARC)engine. In most cases there are two stages used in thepattern-recognition process . First, the output of thesensor array is trained by the PARC method usingmathematical rules that relate the output from a knownodour to a set of descriptors (k classes) held in aknowledge base, see Fig. 2. This process is known assupervised learning. Then the response from an un-known odour is tested against the knowledge base andthe predicted class membership is given . A variety oftechniques have been adopted from the field ofchemometrics and applied to the output from odour-sensing arrays. Some methods are parametric, that is

they rely upon a known probability distribution of thevariables (e.g., discriminant analysis or partial leastsquares), while others are non-parametric and thusapply more generally, such as principal-component anal-ysis. Table 6 lists some of the supervised learning PARCmethods that have been applied to the responses fromodour-sensing arrays. Some of these are linear tech-niques and assume that the response vectors are welldescribed in Euclidean space. In general this is nottrue unless the sensor outputs have been linearized orthe odour concentration is low, so that the chemicalresponse is naturally linear. In these cases good resultshave been achieved from linear PARC methods (seeTable 6). More recently, supervised learning artificialneural-networking techniques (e.g., back-propagation)have been used. These methods are quite attractivebecause they can handle non-linear data, they aretolerant to sensor drift or noise (=10%) and tend toproduce lower predictive error rates than chemometrictechniques. Moreover, the artificial neural network (e.g.,a three-layer back-propagation network) is attractiveas it, to a certain extent, mimics the olfactory system[38], Fig . 5. Methods are now being explored whichallow the use of fuzzy membership functions (e.g.,learning vector quantization) to predict class mem-bership. This again has a parallel with the way thehuman nose classifies odours in subjective terms .There are also PARC methods that do not need a

separate training stage but learn to discriminate betweenthe response vectors automatically . These are unsu-pervised learning methods and are closer to the waythe brain works. The principal methods used so farare listed in Table 7 .

3. Current status of electronic nose technology

3.1. ApplicationsA considerable number of applications of electronic

noses have been reported. The most commonly reportedapplication is their use to classify the smell (or flavour)of various beverages or foodstuffs . In some cases thissimply grades the samples and in others tests forfreshness. Figure 6 shows some typical results : (a) aplot (radar plot) of the response vectors for variouscoffee beans [22]; (b) the discrimination of the responsesfrom tainted lager beers using a cluster analysis [29] ;and (c) the classification of odour classes using principal-component analysis [24]. Table 8 summarizes some ofthe different applications of electronic noses that havebeen reported to date .

3.2. Commercial electronic odour-measuring instrumentsElectronic odour monitors have been produced com-

mercially for a number of years . These instruments are

215

Array type No. of sensingchannels

Country Reference

Sintered metal-oxidechemoresistors 6 Japan 22

6 USA 238 Japan 24

12 UK 25

Lipid layerspiezoelectric crystals 8 Japan 26SAW devices 6 Japan 27

Phthalocyaninechemoresistors 5 UK 28

Organic polymers on 12 UK 29chemoresistors 20 UK 30SAW devices 12 USA 31

Electrochemical 2-18 USA 32

Pd-gate MOSFET 10 Sweden 33

Optical FET camera 324 pixels Sweden 34

216

(a)

capable of detecting the intensity of an odour, but sincethey have only one sensor they cannot be describedas an electronic nose . There are now several electronicnoses commercially available or close to it. Figure 7shows two of these, the Odour Mapper (UMIST Ven-tures, UK), which consists of an array of 20 conductingpolymer chemoresistors, and the Intelligent Nose (AlphaMOS, France), which consists of an array of six metaloxide sensors, each capable of running at one of twotemperatures . Both instruments require a separate PCto calibrate and run the odour-sensing arrays . Thevarious commercial odour monitors and electronic nosesare listed in Table 9 .

(b)

Receiver

4f amplifier

Transmittermcs (Aul

tats {Aul

Piezoelectric substrate

The outlook for electronic noses

4.1. Development of application-specific electronic nose(ASEN) technology

The application of conventional gas-sensor technol-ogies to odour sensing has so far brought limited success .Gas sensors tend to suffer from quite significant prob-lems when compared to non-chemical sensor-types (e.g.,thermal). Thus, metal oxide, polymeric chemoresistorsand SAW devices are sensitive to both the operatingtemperature and humidity of the carrier gas. This meansthat they should ideally be controlled or at least mon-itored to permit parametric compensation . The human

(c)

(d)

Fig. 3. Some odour sensors : (a) commercial Figaro sensor; (b) Pd-gate MOSFET structure [20] ; (c) conducting polymer chemoresistor ;(d) lipid-coated SAW device [20] .

TABLE 5. Some sensor processing algorithms used in gas and odour sensors

Model

Difference

Relative

Fractional difference

Logarithm

Sensor normalization

Array normalization

X3

(a)

Fig. 4. (a) General representation of the response of an arrayof odour sensors to odours j and j' and (b) the normalizedresponses .

TABLE 6. Some supervised learning PARCs used in electronicnoses

Response Vectors

(b)

Formula

x,;= (vf1^l

x" _ (V"a- Vmin) rtnn"

X ii - log(Vij'"- V+ in)

xii=x,,/(x}f'-xIT'y)

xU =x%i~~

xl

X2

chemoreceptors are maintained at a constant temper-ature by the body, and constant humidity by the mucuslayer. Secondly, and more importantly, the baseline ofthe sensors (e.g ., resistance in air) tends to have apoor stability and is liable to poisoning. The importanceof this, problem is really application specific. Whenmeasuring samples in which odour space is well definedand relatively inert (e.g., low levels of unreactive species

InputLayer

Sensor type

Refs.

metal oxide chemoresistorSAW

metal oxide chemoresistorSAWs

metal oxide chemoresistorconducting polymer

metal oxide

metal oxide chemoresistorpiezoelectric

metal oxide chemoresistorpiezoelectric

HiddenLayer

OutputLayer

1

02

Os

sError

Fig. 5 . The back-propagation neural network that crudely mimicsthe olfactory system. The input layer is equivalent to the olfactoryreceptor cells, the hidden layer is equivalent to the glomerulinodes, and the output layer equivalent to the mitral cells .

TABLE 7. Some unsupervised learning PARCs used in electronicnoses

PARC method

Linear

Parametric

Refs.

21 7

Euclidean cluster

yes

no

17, 22, 24, 35analysis

Other duster

no

noanalysis

Kohonen network no

no

41

with a low molecular weight), sensors tend to performwell and there should not be any significant interferenceeffects. On the other hand, when measuring reactivespecies at high levels, poisoning of the sensors is likely(e.g., chlorine with metal oxides, or ammonia withpolymers). However, we can take heart from the bi-ological system, which uses poor chemical sensors thatin fact only live for about 22 days on average yetachieves high levels of selectivity and sensitivity . Con-sequently, considerable improvements on existing elec-tronic noses can be achieved by improving the signal-

PARC method linear Parametric Refs .

Principal-componentanalysis

yes no 17, 24, 26, 35

Discriminant functionanalysis

yes yes 17, 39

Template matching(usually least squares)

yes yes 17, 29, 32

Back-propagationneural network

no no 17, 37, 38, 40

Learning vectorquantization

no no 17, 40

23, 3627

21, 3531

3529

24, 37

37, 3840

3526

218

TGS711

(a)

0.4

N 0.3

CaC

0.2C0 0.1E•

• 0

-0•

.1

U -0.2

(b)

7 .00

2 .00

(c)-8 .60

C. arebica

TGS812

TGS800

Freeze dried

C. robusta

-0.3-0.5 -0.3

-0.1 0 0.1-0.4

-0.2Cluster component 1

Spray dried

PRINCIPAL COMPONENT 1

0.2 0.3 0 .4 0.5 0.6 0.7

-1 .00

Fig. 6. Typical results from electronic noses on the classificationof various odours: (a) coffee beans [22] ; (b) a set of taintedlager beers (group A) and a set of control lager beers (groupB) 1291 ; (c) several odour classes (P-pungent, M = minty andE = ethereal) [24] .

processing techniques currently used as well as thematerials technology. This almost certainly means thedevelopment of more realistic artificial neural networks,which can automatically compensate for the drift insensor parameters and thus extend the calibration periodfrom the current level (typically, two days to threemonths) .

In essence, there is not a universal nose at presentthat can solve all odour-sensing problems . Instead weneed to develop application-specific electronic nose

TABLE 8. Some applications of electronic noses

(a)

(b)

Fig. 7. Commercial electronic noses: (a) the Odour Mapper(UMIST Ventures, UK) ; (b) the Fox 2000 Intelligent Nose (AlphaMOS, France) .

(ASSN) technology appropriate to the application . Thismeans developing the appropriate sensor structures(e.g., ref. 42), the appropriate sensor materials and theappropriate PARC recognition method . Thus the ap-plication cannot be divorced from the instrumentation .

.r

r •rr/

~1i

Application Array type Refs.

Grading of coffee blends or beans metal oxide 17, 22Roasting level of coffees metal oxide 39Grading of whiskeys piezoelectric 17Grading of lagers and beers metal oxide 17, 35Off-flavours in lagers polymer 29Freshness of fish metal oxide 17, 21Freshness of meat MOSFET 33Quality of grains electrochemical 17, 32Quality of air polymer 17Perfumes piezoelectric 26, 27

TABLE 9. Some commercial odour monitors and electronic noses

Product name

Portable odour monitor

Portable odour level indicator(XP-329)

Alabaster-UV

Oral checker

Rhino

The Nose

Intelligent Nose(Fox 2000)

Odour Mapper

Supplier

Sensidyne Inc.,USA

New Cosmos Electric Co.,Japan

Europhor Instruments,France

National,Japan

USA

Neotronics Ltd,UK

Alpha MOS,France

UMIST Ventures,UK

4.2. Future applicationsThere is a plethora of potential applications of elec-

tronic noses today . Besides the assessment of variousfoodstuffs and beverages, there is considerable scopein the field of environmental monitoring . Odour controlis of increasing importance in our lives, for example,in automobiles, trains, aircraft, inside and outside ofbuildings and factories. This environmental applicationarea is particularly important because the architecturefor an electronic nose is similar to that of a ChemSAD,so it could be trained to recognise hazardous chemicalsas well as odours.

Finally, it is quite likely that there will be increasinginterest in the use of electronic noses in the medicalfield. One particularly exciting prospect is the use ofan electronic nose to supply diagnostic information tomedical practitioners. The Chinese have used smell forthousands of years to help diagnose ailments. Classicexamples are acetone on the breath of diabetics, halitosisfor stress or stomach disorders, and a sweet skin smellfor Hansen's disease. Perhaps one day an electronicnose will form an integral part of diagnostic medicalpractice .

References

1 J.E. Moore, J.W. Johnston and M . Rubin, The stereochemicaltheory of odour, Sct. Am., 210 (1964) 42-49.

2 J.S. Kauer, Contributions of topography and parallel pro-cessing to odour coding in the vertebrate olfactory pathway,Trends Neuroscience, 14 (1991) 79-85 .

3 D. Schild (ed .), Chemosensory Information Processing, NATOASI Series H: Cell Biology, Vol . 39, Springer, Berlin, 1990 .

Comments

219

hand-held monitor with pellistor element

hand-held monitor with pellistor element

desk-top monitor with metal oxideelement

palm-held monitor of breath freshnesswith metal oxide (TGS 550) element

desk-top electronic nose with four metaloxide sensing elements

prototype electronic nose using 10 polymerelements to monitor beer

desk-top electronic nose with equivalentof 12 metal oxide elements

desk-top electronic nose with 20 polymerelements

4 M. Meredith, Neural circuit computation : complex patternsin the olfactory bulb, Brain Res. Bull., 29 (1992) 111-117 .

5 GIL Dodd, P.N. Bartlett and J.W. Gardner, Odours-thestimulus for an electronic nose, in J .W. Gardner and P.N .Bartlett (eds.), Sensors and Sensory Systems for an ElectronicNose, NA TO ASI Series E: Applied Sciences, Vol . 212, Kluwer,Dordrecht, 1992, Ch . 1 .

6 M.C. Meilgaard, Flavor chemistry of beer: Part II : flavor andthreshold of 239 aroma volatiles, MBAA Tech. Q., 12 (1975)151-168 .

7 M.G.J. Beets, Structure-Activity Relationships in Human Che-moreception, Allied Science Publishers, London, 1978 .

8 R.W. Moncrieff, An instrument for measuring and classifyingodours, J. Appl. Physiol., 16 (1961) 742.

9 W.F. Wilkens and A.D. Hatman, An electronic analog forthe olfactory processes, Ann. NY Acad Sci., 116 (1964)608 .

10 T.M. Buck, F.G. Allen and M. Dalton, Detection of chemicalspecies by surface effects on metals and semiconductors, inT. Bregman and A. Dtavnieks (eds.), Surface Effects in De-tection, Spartan Books Inc., USA, 1965 .

11 A. Dravnieks and P.J. Trotter, Polar vapour detection basedon thermal modulation of contact potentials, J. Set Instnrm .,42 (1965) 624 .

12 K. Persaud and G.H. Dodd, Analysis of discrimination mech-anisms of the mammalian olfactory system using a modelnose, Nature, 299 (1982) 352-355 .

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15 J.W. Gardner, Pattern recognition in the Warwick ElectronicNose, 8th Int Congress of European Chemoreception ResearchOrganisation, University of Warwick UK July 1987.

16 J.W. Gardner, P.N. Bartlett, G.IL Dodd and H .V. Shurmer,The design of an artificial olfactory system, in D . Schild (ed.),Chemosensory Information Processing NATO AS! Series H:•Cell Biology, Vol. 39, Springer, Berlin, 1990, pp. 131-173 .

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17 NATO Advanced Research Workshop, Reykjavik, Iceland,August 1991, J.W. Gardner and P.N. Bartlett (eds.), Sensorsand Sensory Systems for an Electronic Nose, NATO ASI SeriesE: Applied Sciences, Vol. 212, Kluwer, Dordrecht, 1992 .

18 S. Vaihinger and W. Gopel, Multicomponent analysis inchemical sensing, in W. Gopel, T.A. Jones, M . Kleitz, 1 .Lundstrom and T . Seiyama (eds.), Sensors: A ComprehensiveStudy, Vol. 2/3, Chemical Sensors, VCH, Weinheim, 1990, Ch.6, pp. 191-237.

19 J.W. Gardner and P .N. Bartlett, Pattern recognition in gassensing, in P . Moseley, J. Norris and D. Williams (eds),Techniques and Mechanisms in Gas Sensing, Adam Hilger,Bristol, 1991, pp. 347-380 .

20 P.T. Moseley, J.O.W. Norris and D .E. Williams (eds .), Tech-niques and Mechanisms in Gas Sensing Adam Hilger, Bristol,1991 .

21 M. Egashira, Y. Shimizu and Y. Takao, Trimethylamine sensorbased on semiconductive metal oxides for detection of fishfreshness, Sensors and Actuators, B1 (1990) 108-112.

22 T. Aishima, Aroma discrimination by pattern recognitionanalysis of responses from semiconductor gas sensor arrays,J. Agri Food Chem., 39 (1991) 752-756.

23 B.S. Hoftheins, Using sensor arrays and pattern recognitionto identify organic compounds, MS Thesis, University ofTennessee, Knoxville, USA, 1989.

24 H. Abe, T.Y. Yoshimura, S . Kanaya, Y. Takahashi, Y . Mi-yashita and S. Sasaki, Automated odor-sensing system basedon plural semiconductor gas sensors and computerised patternrecognition techniques, Anal. Chim. Acta, 194 (1987) 1-9 .

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