Journal of Automatic Chemistry, Vol. 17, No. 5 (September-October 1995), pp. 179-185

Thethe

electronic NOSE and its application tomanufacture of food products

Diana HodginsNeotronics Scientific Ltd, Western House, 2 Cambridge Road, StanstedMount.fitchet, Essex CM24 8BZ, UK

and Derek SirnmondsMaterials Research Institute, Sheffeld Hallam University, City Campus, Sheffield$1 1WB, UK

The Electronic NOSE (Neotronics Olfactory Sensing Equipment)is an instrument which mimics the human olfactory sensory system.I1 analyses complex vapours and produces a simple output. In thefood industry there are numerous examples where the aroma fromthe raw ingredients through to the final product are important.These aromas are currently analysed using human sensory panelsor analytical equipment such as gas chromatography/mass,poop: ac/a4s

The Electronic NOSE described in this paper was not developedto replace the GC/MS or the sensory panel but to provide aninstrumental measure of aroma quality which would be related toand complement the current methodology. The Electronic NOSEis a robust system which can detect complex vapours at levels similarto the human, which means typically in the parts per billion range.The system produces an output which can be easily related tosensory data and is easy to interpret by a non-skilled operator. Nopart of this system reacts with the sample under test.

Introduction

The NOSE (Neotronics Olfactory Sensing Equipment)which is described in this paper is shown in figures and2. The key aspects are the sensor array and the overallsystem design, both of which will be described in somedetail.

The potential applications for the system within the foodindustry include all operations where the endpoint isdefined by aroma. This covers on-line control for a rangeof processing including roasting, toasting, baking andcooking and blending operations for products such assauces, soups and coffees. The NOSE could also be usedfor routine Q.A/Q.C operations such as new materialscharacterization in particular species, herbs and flavour-

Vapour array of sensor analysissignals

,- Output

System

Figure 1. Principal components of the NOSE.

Figure 2. Electronic NOSE.

ings, authenticity monitoring and detection ofoff-flavoursand taints.

The flavour of foods is made up of two components: thetaste which is caused by non-volatile compounds whichreact with biological sensors on the tongue and in themouth; and the volatile compounds which interact withsensors in the nose and are responsible for odour. Of these,the many hundreds of volatiles which make up the odourare by far the most important in defining product typeand individual preferences, and are responsible foroff-flavours and taints which may arise in foods. Volatileodour compounds may arise in foods from a variety ofsources--for example the process of toasting and roastingcan generate a range of odorous compounds whichimport the typical roasted, toasted, nutty, odour associ-ated with bread, breakfast cereals and snack foods.The types of compounds generated are formed viacaramelization ofsugars or through the Maillard reactionwhich generates such compounds as thiazoles, pyrazinesand pyridines.

Off-odours and taints arise in foods due to decompositioncaused by endogenous enzymes, contamination or chemi-cal oxidation. There are many examples of these including

0142-0453/95 $10.00 (C) 1995 Taylor & F is Ltd.179

D. Hodgins and D. SimmondsThe electronic NOSE and its application to the manufacture of food products

microbial spoilage which can lead to putrid, ammoniacal,musty/mouldy odours in a range of products due to

sulphur volatiles, amines and chloroanisoles. Both enzymeand chemical oxidation can lead to a range of rancidoff-odours, particularly in high lipid containing productsdue to the formation of aldehydes and ketones. Othertaints which arise are to simple contamination, forexample by spillable of oils, diesel or cleaning agents.

All of these compounds are typically present at lowconcentrations in the vapour phase and the human candetect these down to the parts per billion level or evenlower. It is therefore necessary that any sensor used in anelectronic system must respond to the different groupsdescribed and effectively be able to detect these at similarconcentrations to the human.

Conducting polymer sensors

The sensor materials used in the NOSE are electro-chemically grown onto a silicon substrate. The resultingpolymer, for example polypyrrole, is in an oxidized formcontaining cationic sites balanced by anions from theelectrolyte and the composite is electrically conductive.Conduction occurs via electron transport and the chargecarriers are associated with the cation sites of the polymer(probably polarons or bipolarons providing mobile holesfor electron transport). The conductivity attained issensitive to the presence or absence of substituents on thepyrrole rings and to their electron-donating or withdrawingcharacter.

In addition to the polypyrrole chains, the conductivecomposite contains the counterions and at least some ofthe solvent used for electrode position. All three compon-ents contribute both to the inherent resistance and to theinteraction of analyte species with the sensor. Somecounterions and solvents also affect the stability of sensors,so that the ’sensor’ is best thought of as the composite(rather than just the polypyrrole) and a good sensor isone that reproducibly recognizes analytes and has goodstability. Sensing depends on a change in conductance ofthe conductor composite on interaction (which must bereversible) with the analyte. The change may resultdirectly from interactions between the organic vapoursand the polymer, or indirectly from interactions betweenthe vapours and the solvent-counterion matrix, or bothmechanisms may be operative. In any case the sensingactivity will result from a change to the ’doping’ of thepolymer, i.e. the population of active charge carriers inits structure that contribute to the conductance.

Little has been published on the detailed microstructureof conductive polymer materials related to polypyrrole[1]; however, it is known that the counterions and thesolvent produce consistent effects [2] and that thepolarity of organic analytes contributes to changes inthe conductance I-3]. In addition, it is probable that thesolubility of different analytes in the local phase of theSensor surface will also effect the conductance.

In order to increase understanding of the material andvapour interaction, Neotronics and Sheffield HallamUniversity are currently combining their theoreticalknowledge of materials, including the structure formation

down to molecular level. This work will enable futurematerials to be designed based on models developed formore specific applications and could make the existingdevice more diagnostic ofstructural type in some analytes.

One aspect worth noting is that the polymer material hasa finite thickness: typically around 50 gm. The composi-tion of the material is such that a large number of chainswill form alongside one another and on top ofone another.While the surface of the sensor is probably the primaryregion for the important interactions with analytes, it islikely that counterions and solvents are distributedbetween the polymer chains and throughout the bulkmaterial.

It is clear that, in a three-dimensional structure, theproblem ofmolecular interaction from an external vapouris far more complicated than for the simple two-dimensional structure already described. Fortunately, thecomplications of the three-dimensional form do not haveto be fully analysed, since it can be assumed that thesurface interactions are repeated within the materialstructure.

The sensors show good stability but, in the long term,there is a gradual change in conductance. While, initially,no primary bonding occurs between the polypyrrole chainsand the counterions, solvent or the analyte, it seems likelythat small molecules become lodged in interstitial spaces,probably leading to local distortions of the conductivestructure and hence a slight reduction in conductivity.Polar, donor molecules such as water (that can associatewith cation sites) are probably responsible and observa-tions confirm that prolonged exposure to high humiditylevels is deleterious. The result is that the conductance ofthe polymer will reduce with time due to fewer chargecarriers being present. However, the charge carriers thatremain will not have been noticeably affected. Therefore,since the system monitors the percentage change inconductance or exposure to an organic vapour, thesensitivity will remain relatively constant.

Sensors

Sensor technologies which are either available in produc-tion or as prototypes include tin oxide sensors [4];conducting polymer sensors [5-7]; surface acoustic wavedevices [8]; and biosensors/enzyme sensors [9]. Each ofthese offers different characteristics in terms of theirselectivity and sensitivity to different compounds. Con-ducting polymer sensors offer the largest range in termsof selectivity and sensitivity and therefore this sensor

technology has been adopted for the NOSE.

Conducting polymer sensors

The sensor materials are electrochemically grown onto asilicon chip. The sensors are unique: first, the materials,which comprise a conducting polymer, a counterion anda solvent, are grown from solution onto a workingelectrode which is separated by a 10 gm gap. As thematerial grows it bridges the gap, thus producing a simpleresistor. Each sensor type has either a different polymer,counterion or solvent, which alters its response to complex

180

D. Hodgins and D. Simmonds The electronic NOSE and its application to the manufacture of food products

-, Sensor head .assemblyPurge out Purge in

Seal

Sample vessel

Liquid sample

Figure 3. Schematic ofpurging method.

is no contamination of the sample, nor any carry-overfrom one sample to another.

A second important consideration is that the samplesmust not be affected by environmental changes. Toprevent this, the sample vessel and sensor array en-closure are purged with a known reference gas, which istypically dry air. Thus the sample equilibrates into theknown reference gas and this is also the reference far thesensors. A schematic of the purging system is shown infigure 3.

The system is designed to work at room temperature,which closely correlates with sensory panel data. Howeverif the sample changes with temperature and this variationis important then the system can be trained to recognizethe sample at different temperatures.

vapours. This very simple, single stage fabrication processproduces the same matrix structure every time andtherefore sensor to sensor reproducibility is extremelygood. Second, semiconductor technology is used toproduce a gas sensor; and 10l.tm gaps are easy toreproduce consistently.

Sensor array design

To achieve a fingerprint from a complex vapour whichmay contain many thousands of molecules, it is necessaryto have an array of sensors. A sufficient number of sensorsis required in the array to ensure that a unique fingerprintis obtained from different samples. The array describedhas 12 sensors which are soldered onto a printed circuitboard, which is then mounted into a sealed enclosure.Each sensor in the array uses a different polymer structure,and this structure defines the response characteristic ofthe sensor to complex vapours. It is very difficult to statewhich polymer material responds to individual com-pounds, since these generally exist in a complex form inthe vapour. However, sensors can" be chosen by utilizingthe sensor response characteristics to known individualcompounds. When testing complex vapours, a uniquefingerprint will be obtained. The user can relate eachfingerprint to sensory panels and/or GM/MS data ifdesired. Therefore for different applications the user willhave a library of fingerprints which can be matched todifferent sensory attributes or flavour chemicals.

System design

The system has been designed to ensure that optimumperformance is obtained and that it is suitable for use inthose industries for which it is designed. In the foodindustry, sensory panels place the samples in inert glassvessels, allow the sample to equilibrate into the headspaceand then smell the headspace. The system has beendesigned to simulate this methodology as closely aspossible. The sample container is glass and the sample isleft to equilibrate before the sensors are exposed to thestatic headspace above the sample. All other materialsthat come into contact with the sample or the vapour aremanufactured from inert materials to ensure that there

Sample testing

The system requires minimal sample preparation, withsamples being presented to the NOSE whole, ground orpureed. In most applications the sample is placed into thesample vessel and allowed to equilibrate. The sensor arrayis then exposed to the headspace above the sample. Thesensors respond to the vapour in a transient nature, with90 of the response achieved within 30 to 60 seconds,depending upon the sample type. The test period is setfor typically 60 and the data is stored from all sensorsevery second during this period. On completion of thetest, the sensor array is resealed from the sample vessel.The system has also been designed to allow for multiplesampling.

Operation

Operation is simple, the operator is initially requested toenter information regarding the sample to be tested. Thiswill include:

(1) Description of the test, for example a test identificationnumber.

(2) Class which specifies the product group.

(3) Substance which specifies the member of a class.

(4) Taints which specifies any known impurities in thesample.

(5) Effects which describes the result of a taint or numberof taints.

(6) Comments for any other information the operatorwishes to record alongside the test data.

(7) Method which defines the sensors to be used and theduration of the acquisition for the specified class.

Once the details have been entered, acquisition cancommence and the operator is kept informed of progressvia a real time graphical profile. When acquisition iscomplete, the graphical profile of sensor response against

181

D. Hodgins and D. SimmondsThe electronic NOSE and its application to the manufacture of food products

Sample

Class sesame oilSubstance sample 3- desired tasteSample Average(Template)Method NIATime NtACalibrated None

R=+I

Cursor at 01:00

+0.28 Type 278 +0.37 Type 264 [ +0.25 Type 263+0.13 Type 261 Type 261 +0.20 Type 260+0.54 Type 259 [ +0.27 Type 258 +0.38 Type 257

Sample CommentsN/A

Figure 4. Offset polar offresh oil.

F -0.o3 Type 262Type 260

time is automatically sealed. The data is then ready forfurther analysis.

Analysis techniques

A number of analysis techniques could be used for theapplication, for example graphical representation of theindividual sensor outputs with time; polar plots or spiderplots; statistical techniques; offset polar or difference plots;neural networks.

For simple QC/QA type testing the last two exampleswould be suitable.

Comparison of samples to a known reference

In the majority of cases users will want to monitor a

product and determine whether it has deviated significantlyfrom the reference sample. Using the difference plottechnique any sample can be quickly compared to aknown reference. If both sample and reference wereidentical then a circle would be drawn. If the sampleoutput is less than the reference then the vector movesinwards and ifit is greater then the vector moves outwards.Again the radius defines the magnitude of this difference.An example is the testing of two edible oil samples. Thefirst was passed by a sensory panel, while the second was

182

D. Hodgins and D. Simmonds The electronic NOSE and its application to the manufacture of food products

Analysis Sam>le Average Offset Polar view

Sample

Class sesame oilSubstance sample 4 bitter tasteSample Average(Template)Method NIATime N/ACalibrated None

R=+1.00%

Cursor at 01:00

+0.22 Type 278+0.12 Type 261+0.49 Type 259

Sample CommentsNtA

Figure 5. Odse! polar of rancid oil.

+0.26 Type 264 i +0.20 Type 263Type261 li!’, +0.18 Type260

+0.35 Type 258 I1 +0.36 Type 257

E]] 0.00 Type 262Type 260

stated as rancid. The two individual plots are shown infigures 4 and 5 and the difference between the two shownin figure 6. It is important to note that the offset polarplots for the fresh and rancid oil samples are plotted ona 1.00 radius plot and the difference plot on a moresensitive scale with a radius of 0.20. It is clear from thedifference plot that there is a significant differencebetween the two samples shown on at least 50 of thesensors. In other examples a difference may only be seenon two sensors, which would suggest that a taint was

present; ifall sensors exhibited a difference then the samplewould be said to be very noticeably different.

Neural networks

The analysis techniques described so far are suitable forcomparing samples to a specified reference. This isadequate when there is only one reference but becomesmore complicated when there are a number of references.

183

D. Hodgins and D. SimmondsThe electronic NOSE and its application to the manufacture of food products

Analysis Difference ((Avg)-R(Avg)/- utrset ’omar v=ew

sample Reference

Class sesame oilSubstance sample 4 bitter tasteSample Average(Template)Method N/ATime NtACalibrated None

sesame oilsample 3- desired tasteAverage(Template)N/ANIANone

R=+(}.2(,

Cursor at 01:00

-0.06 Type 278-0.01 Type 261-0.05 Type 259

-0.11 Type 264Type 261

+0.08 Type 258

Sample CommentsN/A

Reference CommentsN/A

Figure 6. Difference plot between the two oils.

For example, if a user wishes to compare a sample whiskyto six different whisky types using the techniques so fardescribed, the user would have to compare the sampleto one reference at a time, determining any differences.This would be quite a time-consuming process as thenumber of references increased.

A solution would be to use a neural network to analysethe data frbm the sensor array. In the simplest arrangementthe percentage change in resistance at specified times for

-0.05 Type 263-0.02 Type 20-0.02 Type 257

:] "+0.03 Type 262Type 260

each of the sensors would be used as the inputs to theneutral network. The outputs, defined by the user, wouldbe different known types, for example all the differentwhisky types.

There are a number of different neural networks thatcould be used for this task. One of the simplest has beensuccessfully used with data obtained from both an eightand a 12 sensor array. In this case there were either eightor 12 sensor inputs and four to six outputs with one hidden

184

D. Hodgins and D. Simmonds The electronic NOSE and its application to the manufacture of food products

layer. The artificial neurons carry out a simple summationusing the weighting factors from all the links connectedto the neuron. An arbitrary set of weights is given to theneural network before it is trained.

Further industries where the NOSE has been appliedinclude tobacco; flavours and fragrances; packaging;petrochemicals; pharmaceuticals; water; power; andhealth care.

Applications

The NOSE has been applied to both fresh and processedfood, for example to test the freshness of fruit when theproduct is picked, during shipment and at the point ofsale; the maturing process of cheese and product character-ization; quality assurance checks on high value flavouringmaterials; and the identification of different cuts of meatto reduce potential packaging problems.

The NOSE has also been applied to both alcoholic andnon-alcoholic beverages, for example in the identificationof coffee bean types; instant coffee aromas duringprocessing; the detection of diacetyl, dimethyl sulphideand amylacetates during fermentation; and the detectionof trichloroanisole in both corks and packaging.

References

1. CHANDLER, G. K. and PLETCHER, D., Electrochemistry, R.S.C.Specialist Periodical Reports, 10 (1985), Chapter 3.

2. ZHANG, W. and DONG, S., Electrochimica Acta, 38 (1993), 441.3. KUNUGI, Y., NIGORIKAWA, K., HARIMA, Y. and YAMASHITA, K.,

Journal of the Chemical Society, 863 (1994).4. SHURMER, H. V., GARDNER, J. W. and CHAN, H. T., Sensors and

Actuators (1989).5. BARTLETT, P. N., ARCHER, P. B. M. and LINO-CHUNG, S. K., Sensors

and Actuators (1989).6. BARTLETT, P. N. and LNG-CHUNO, S.J., Sensors and Actuators (1989).7. Dmz, A. F., JKANASAWA, K. K. and GARDIN, G. P., Journal of the

Chemical Society, 635 (1979).8. WOHLTZEN, H., Sensors and Actuators (1984).9. HIGSON, S. P. J., REDDY, S. M. and ,rADGAMA, P. M., Engineering

Science and Education Journal (February 1994).

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