Sensors and Actuators B, 13-14 (1993) 35 1-354

Development of odour-sensing system using an auto-sampling stage

Junichi IdeKawasaki Research Centre, T. Hasegawa Co., Ltd., 335-Kariyado, Nakahara-ku, Kawasaki, Kanagawa 211 (Japan)

Takamichi Nakamoto and Toyosaka MoriizumiFaculty of Engineering Tokyo Institute of Technology, 2-12-1 Ookayarna, Megtao-ku, Tokyo 152 (Japan)

Abstract

The present authors previously reported that odours could be discriminated by a sensing system using a quartz-resonator sensor array and neutral-network pattern recognition . In that system, however, the number of sampleswas limited due to the increase in the number of solenoid valves. A new system for measuring many sampleshas been developed recently, in which two syringe needles driven by a XYZ mechanical stage are inserted intoa vial with a rubber stopper where a sample is poured . A dry air supply through needles carries odourant vapourto a sensor array. This sequence continues repeatedly to measure many samples automatically . By modifyingexperimental parameters, the mean coefficient of variation can be suppressed to 3.3% . 80 data for 20 typicalessential oils have been measured in 320 min by the present system.

1. Introduction

An odour-sensing system is desired in a broad varietyof fields, from the food, drink and cosmetics industriesto the environment and others. The human sensorytests now utilized to discriminate odours in these fieldsare inevitably affected by the inspector's state of healthand mood. It is therefore essential that an objectiveevaluation method be developed for practical use .The present authors developed an odour-sensing

system using a quartz-resonator sensor array and neu-tral-network pattern recognition, by which the odourof alcoholic beverages could be identified [1] . Thereafter,it was found that selecting the sensing membranes bymultivariate analysis and measurement-system modi-fication for reducing data variation were effective inheightening the identification capability [2, 3] . However,the number of samples was limited due to the increasein the number of solenoid valves, since two valves persample were used for controlling the sample vapoursupply .

A new system for measuring many samples by anautomatic sampling stage has been developed to over-come this problem, and applied to flavour discrimination .The results for many kinds of essential oils obtainedby principal-component analysis are reported here .

2. Principle of system

A quartz-resonator sensor, composed of a resonatorand sensing membrane coated over it, is used here .

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The resonance frequency decreases when odourantmolecules are adsorbed onto the membrane, and thefrequency recovers after desorption . This phenomenonis called the mass-loading effect [4-6] and the shift infrequency is proportional to the total mass of adsorbedodourant molecules. As the responses of each sensorwith a different membrane to odours are slightly dif-ferent, the output pattern from the sensor array canbe used to identify odours .

3. Measurement system

3.1. Previous systemA vapour-flow system was used here, and the sensor

output could be repeatedly measured within a shorttime. A sample vapour was injected into a sensor cellwhere eight sensors were installed, and then dry airwas supplied to refresh the sensors. After recovery ofthe sensor responses, the next sample vapour wasinjected. In the previous system [3], five samples couldbe measured sequentially. The vapour flow was switchedby miniature solenoid valves controlled by a personalcomputer, making the measurement fully automatic.However, the number of samples was limited due tothe increase in the number of solenoid valves . A newsystem for measuring many samples has been developedto overcome this problem .

© 1993 - Elsevier Sequoia . All rights reserved

352

3.2. Developed systemIn this system (Fig. 1), liquid odour samples are

poured into vials which are sealed with rubber stoppers .By using a mechanical stage (purchased from SankyoSeiki MFG, Co ., Ltd., Sankyo SKIRAM SR5383) whichmoves along the XYZ axes, two syringe needles areplaced on the vials, and inserted into them, throughthe rubber stoppers . Three solenoid valves in Fig . 1(b)also move together with the syringe needles .

After the penetration of the needle, dry air with theflow rate (90 ml/min) controlled by a mass-flow controlleris supplied through one needle for 30 s, and the samplevapour is flowed out through the other needle into thesensor array . Then the needles are moved to an emptyvial, and dry air is flowed in the same way to refreshthe sensors. These operations are repeatedly controlledby a personal computer so that many samples can bemeasured automatically. The schematic diagram of thesample flow system is shown in Fig . 2 .

22 W

.T , Temperature-Control led-

Water Bath

(a)

(b)

Fig . 1. Photograph of automatic sampling system : (a) wholesystem; (b) detail of syringe needles .

In the same way as in the previous system, quartzresonators used here were AT-cut with fundamentalresonance frequencies of about 10 MHz . The eightresonators with different membrane materials wereconnected to CMOS oscillation circuits and their fre-quencies were measured in parallel using frequencycounters. A personal computer (NEC 9801 DX2) readout the frequency data through an 1/0 interface .

4. Experimental

4.1. Essential-oil aroma separationThe sensing membranes used here were gas-chro-

matographic stationary phase materials, cellulosic ma-terials and lipid membranes (Table 1). As Okahata etal . reported that a lipid membrane was effective indetecting odourant materials [7], many lipid membraneswere used in the present study .

The experiments on essential-oil aromas were per-formed as follows. In the first experiment, the samplesused were four essential oils, e.g., ginger, clove, valenciaorange and peppermint oil, which have typical aromasthat can be distinguished easily by an ordinary individual .Two bottles were set for each sample, so that eightsamples in total could be measured in a measurementcycle. After each sample bottle was measured, theresulting data were analysed by multivariate analysis(i.e., principal-component analysis) .

Figure 3 shows the scattering diagram of the fouressential-oil samples with different aromas . In thisFigure, the category of clove oil was close to that ofpeppermint oil. The fact that both of them are madefrom medicinal plants might be the reason why theywere close to each other.

In the next experiment, the samples used were fiveessential-oil samples with the same substantial aroma,e.g ., valencia orange, calabria orange, grapefruit, limeand lemon . Those samples cannot be easily distinguishedby an ordinary individual. The measurement systemwas operated in the same manner as that of the firstexperiment. The result from principal-component anal-ysis is shown in Fig. 4.

Although valencia orange and lime were distinguishedfrom each other, the separation among grapefruit, lemonand calabria orange was not sufficient . Hence, to sup-press the data variation, the temperatures of sampleand sensor cell were stabilized at 23 °C, placing vialsin a water bath the temperature of which was controlledby a thermoregulator, and circulating the same waterinto the sensor cell by a peristalic pump (see Fig . 1) .As a result, the mean coefficient of variation of eightsensor responses to ethanol was decreased from 2 .6 to1 .9% . Moreover, if the sample volume was increasedfrom 4 to 8 ml, the mean coefficient of variation was

StandardAirVessel

Temperature-ControlledWater Bath

Clove

Z1 [72%]IIIaId

t IIII

Q5

ePeppermint

ri

Solenoid Valves

~Mass Flow~-;Controller I '

Sample

Z2126%]2-

0

-1-

Driven by XYZ Stage

AI Sal

RubberStopper

Sample

Pump

e

Valencia Orange

Fig. 3. Scattering diagram of four typical essential oils by PCA(principal-component analysis) .

water circulation

Sensor Cell

Osc. Circuits

J

Freq. CountersI

Personal Computer

Ht

Exhaust

Lime

-2-

Fig. 4. Scattering diagram of essential oils with similar aromacomponents by PCA.

further decreased from 9.7 to 6.8% in the case of thevalencia orange sample. The separations among fiveessential oils were improved as shown in Fig. 5 .The authors changed the sensing membranes to im-

prove further the coefficient of variation . The mem-branes in Table 1 were mainly used for whisky-aromaidentification in the previous studies [3] . As the sen-sitivities of membranes such as cholesterol and di-ethyleneglycol succinate were low and the coefficientof variation of acetyl cellulose membrane was large inthe study of the essential oils, these sensors wereremoved. Instead of them, membranes which showedlarge responses to citrus oils were selected from themembranes in ref. 8. They were dioleyl phosphatidyl

353

Fig. 2. Diagram of odour-sensing

TABLE 1. List of membranes

system using an auto-sampling stage .

used in the sensor array Z2 [42%]

0No. Sensing membrane Classification 2- Grapefruit

e

I

Dioleyl phosphatidyl serin2 Cholesterol (Tokyo Kasei

(AVT Inc.)

lipidCo.)

sterol e e3

Perfuorinated bilayer synthesized lipid e e O4 Lecithin (egg) (Wako Pure Chemical Co.) lipid Valencia Orange

S

e o0 0a

5

Diethyleneglycol succinate GC o Lemon6 Sphyngomyelin (egg) lipid

•Zi [53%]

7

Acetyl cellulose cellulose I-2

i

I , I

O

4I• I

8 Ethyl cellulose celluloseCalabria Orange -

354

Valencia orange

o

0

Calabria orange

2-22 [30%]

-2-

Grapefruit0i

0 0

Lemono

A

Lime

A n

A

Z1 [54%)i1

Fig. S. Scattering diagram of essential oils with similar aromacomponents by PCA.

5. Conclusions

An odour-sensing system using an automatic samplingstage was developed and it is found that this systemis quite efficient for measuring a lot of samples in ashort time . Mapping between the sensor output patternsand the result of human sensory tests will be a furtherresearch target, though further detailed and extensivemembrane selections and the enhancement of mea-surement stability are required.

Acknowledgement

The authors wish to thank Mr M. Hayashi of Mei-densha Corp. for providing the quartz resonators .

References

1 T. Nakamoto and T. Moriizumi, Odour sensor using quartz-resonator array and neutral-network pattern recognition,Proc.IEEE Ultrasonics Symp., Chicago, IL, USA, 1988, p. 613 .

2 T. Nakamoto, K. Fukunishi and T . Moriizumi, Identificationcapability of odour sensor using quartz-resonator array andneutral-network pattern recognition, Sensors and Actuators B,1 (1990) 473-476 .

3 T. Nakamoto, A. Fukuda, T. Moriizumi and Y. Asakura,Improvement of identification capability in an odour-sensingsystem, Sensors and Actuators B, 3 (1991) 221 .

4 G. Sauerbrey, Verwendung von Scbwingquarzen zur WagungdUnner Schichten and zur Mikrowagung, Z . Phys., 155 (1959)206 .

5 W. H. King, Piezoelectric sorption detector, Anal. Chem., 36(1964) 1735 .

6 T. Nakamoto and T. Moriizumi, A theory of a quartz crystalmicrobalance based upon a Mason equivalent circuit, .Ipn. J..App!. Phys., 29 (1990) 963 .

7 Y. Okahata, H. Ebato and K. Taguchi, Specific adsorptionof bitter substances on lipid bilayer-coated piezoelectric crys-tals, 1. Chem . Soc. Chem. Commun, (1987) 1363 .

8 T. Nakamoto, S . Sasaki, A . Fukuda and T. Moriizumi, Selectionmethod of sensing membranes in odour-sensing systems,Sensors Mater., 4 (1992) 111-119 .

TABLE 2. List of membranes suitable for citrus oils used in thesensor array

Sandal wood

SpearmIntCananga

b'WA,11b; Ylarg Ylang

,

w, %b'vI

' 1310, BergamotStar Anis

1

0Fennel . ~9 Caraway

Thyme

Cardamon# Wintergreen

Lemon grass

. -

~7,7 Cedar wood

Fig. 6. Scattering diagram of 20 typical essential oils by PCA .

:v ONutmeg

;i# -Rosemary

Galbanum

Zi (75%)

serin, a mixture of sphyngomyelin and cholesterol andthat of sphyngomyelin and lecithin; the membrane setgiven in Table 2 was chosen . The preliminary experimentusing these membranes showed that the mean coefficientof variation was suppressed to 3 .3%.

Using these membranes, the output patterns of 20typical essential oils were measured . These essentialoils have citrus notes, flower note, mint notes, spicynotes, and so on . The scattering diagram of principal-component analysis which has 97% variance with respectto the principal axes is shown in Fig. 6. 80 data for20 samples were measured efficiently by the system in320 min. Hence, it can be said that the system usingan auto-sampling stage is quite convenient for mea-surements of a wide variety of odour samples .

o. Sensing membrane Classification

1 Dioleyl phosphatidyl serin (AVT Inc.) lipid2 Sphyngomyelin (egg) lipid3 Dioleyl phosphatidyl choline lipid4 Lecithin (egg) (Wako Pure Chemical Co .) lipid5

Perftuorinated bilayer synthesized lipid6 Sphyngomyelin (egg) 33% lipid

Cholesterol 67% sterol7 Sphyngomyelin 50% lipid

Lecithin 50%8

Ethyl cellulose cellulose

Z2(22%) 0 1st2-~y o 2nd

ty: Cassia 0 3rdv 4th

,8b Peppermint