Sensors andand Materials,
Materials, Vol.
Vol. 17,
17, No.
No. 77 (2005)(2005) 365– 383
Sensors
MYU Tokyo

365

S & M 0614

Record of Dynamic Changes of Odors
Using an Odor Recorder
Takamichi Nakamoto*
Graduate School of Science and Engineering, Tokyo Institute of Technology
S3-36, 2-12-1, Ookayama, Meguro-ku, Tokyo, 152-8552, Japan(Received January 6, 2005; accepted May 9, 2005)
Key words: odor recorder, QCM gas sensor, olfactory display, MIMO feedback control, real-timereference method

We have developed the system called an odor recorder for reproducing the smell
recorded using the odor sensing system. The recipe of the odor, where the sensor-array
output pattern of a target odor matches that of an internally blended odor using multi-input
multi-output (MIMO) feedback control, is recorded. It can be reproduced using an odor
blender. The odor recorder has the capability of dynamic change in the odor in addition to
the ability of recording the static recipe of the odor. Here, several techniques such as aneural network with feedback error learning, the real-time reference method and its
modified technique for speeding up are described. The experiments revealed that the realtime reference method had the robustness against the environmental change. Moreover,
the recording of the dynamic change of the odor over a few seconds was successfully
performed using that method.

1.

Introduction

Recently research has focused on communication using five senses. Since the fields ofvision and hearing are mature, it is indispensable to open new fields in addition to thosesenses. The study of recording olfactory information is very challenging.
Although odors have been analyzed using gas chromatograph/mass spectrometry (GC/MS) for long time, odor-sensing systems, often called electronic noses, have been studied
for last two decades.(1) An odor sensing system offers a simple, rapid and objective
evaluation method in place of GC/MS. Its principle is based upon pattern recognition ofmultiple-sensor outputs with partially overlapping specificities in the same manner as the
biological olfactory system. This principle was proposed in 1982,(2) the concept ofsimilarity was introduced,(3) and then a combination of a sensor array with a neural network
was proposed.(4) Thereafter, many researchers came into this field and research on odor
sensing systems became popular.(5,6)*Corresponding author, e-mail address: nakamoto@mn.ee.titech.ac.jp

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On the other hand, an olfactory display, a device for smell presentation, was recently
studied in the field of virtual reality.(7) A computer-controlled scent diffuser connected to
the Internet,(8) an odor-source localization in virtual space,(9) and the olfactory display fordelivering smell to a single user’s nose were studied.(10)Our group proposed an odor recorder with the capability of reproducing smells as well
as recording them in the same manner as in a video cassette recorder (VCR).(11) This is a
new field, since the odor sensing system and the olfactory display have been studied
independently. There are many consumer products related to smell such as food, beverage,
cosmetics, toothpaste, and air refreshners for breath, room and bathroom. It is possible touse an odor recorder in a variety of fields and activities, such as e-commerce, games, virtual
reality, cinema.
In the odor recorder, odor quality is represented as a recipe of the multiple odor
components. Unlike the primary colors in vision, primary smells have thus far not been
discovered, although the stereochemical theory was proposed by Amoore.(12) Thus, therange of smells to be realized is limited at the current stage. However, the range of smells
can be gradually extended after information on smells is accumulated using this system.
There are two types of odor recorder. One determines the odor recipe kept constantduring recording. The other is the odor recorder to obtain the recipe of the dynamicallychanging odor. In the actual environment, the odor concentration is rapidly changed due toturbulence. In this report, the latter type of the odor recorder is reviewed.

2.

Principle of Odor Recorder

2.1 Whole system
Although a sensing system together with an olfactory display has been discussed,(13)only a few reports have appeared for systems with the capability of both recording andreproducing smells. Keller et al. proposed the olfactory transmission method in which anodor identified by a neural network was transmitted to a place, where the same odor was
generated.(14) However, the range of odors was quite limited because the system did nothave the ability to blend odors. Although reproduction of smell was studied using thecommercially available sensing system and conventional multivariate analysis, evaluationof the reproduced smell was not carried out.(15)The principle of an odor recorder is illustrated in Fig. 1. First, the target odor to berecorded is introduced into a sensor array composed of partially overlapping characteristics
and its output pattern is memorized. Then, the responses of the sensors to the blended odor
made up of multiple component odors given by an odor blender are measured and arecompared with those of the target odor. The recipe of the target odor is obtained from thatof the blended odor in the case in which the sensor-array output pattern of the blended odor
agrees with that of the target odor. Otherwise the recipe of the blended odor is iteratively
modified so that the sensor array output pattern of the blended odor approaches that of thetarget odor using a nonlinear optimization algorithm or adaptive feedback control theory.(16)The recipe of the target odor is obtained after convergence.
Once the recipe is recorded, the smell can be reproduced using the odor blender. The
odor blender located remotely from the odor recorder can be used to generate the smell as

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Fig. 1. Principle of odor recorder. (a) Recording and (b) reproduction.

can the blender inside the odor recorder. When the odor blender is located far from theodor recorder, the recipe can be transmitted to it via the Internet.
When the odor is reproduced at a place far from the recording point, there is anotherway to realize the odor recorder as is illustrated in Fig. 2. First, the sensor array output
pattern is recorded. In the reproduction phase, the recipe of the blended odor is iteratively
modified so that the sensor responses to the blended odor can match those to the target
odor.(17)In the recording phase, the output pattern of the sensor array is just recorded without theodor blender. However, the sensor array as well as the odor blender is required in the
reproduction phase. When the odors at various places are collected, the approach in Fig. 2
seems attractive because no blender is required at the recording place.

2.2 Sensors
A variety of sensors are used in odor sensing systems such as metal oxide semiconductor (MOS) gas sensors,(18) quartz crystal microbalance (QCM) gas sensors,(19) surface
acoustic wave (SAW) gas sensors,(20) cantilever-type gas sensors,(21) flexural plate wave
(FPW) gas sensor,(22) conducting polymer (CP) sensors,(23) carbon-black polymer composite gas sensors,(24) MOS FET gas sensors,(25) mass spectrometry (MS),(26) ion mobility
spectrometry (IMS),(27) high-speed GC,(28) optical sensors,(29) and electrochemical gas
sensors.(30)
We used the QCM gas sensor since a variety of gas sensors may be easily fabricated. Itscharacteristics are somewhat similar to the human ones since its limit of detection
correlates with human threshold values. Moreover, its output has a digital form and iseasily interfaced with a digital LSI.

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Fig. 2. Another type of odor recorder.

Its typical orientation is AT-CUT with a temperature coefficient of zero at 25°C. Thus,
stable oscillation is achieved even if its structure is simple. Its operating mode is thickness
shear, in which the particle displacement of the acoustic wave is parallel to the quartz platesurface.(31) It operates even in a liquid phase because of the thickness-shear mode.(32)
Although the typical size of the quartz plate is conventionally 7–8 mm, a recent quartz
resonator mounted on a surface-mounted device (SMD) package was very small down to 2
mm. Thus, the SMD type QCM is effective when many sensors are integrated.(33)The quartz resonator gas sensor consists of a resonator and the sensing film coated overits electrode. There are several methods for coating sensing films such as cast method, spin
coating, spraying, Langmuir Blodgett (LB) method,(34) dip coating, plasma deposition(35)and ultrasonic atomizing.(36) The spray coating method is often used because of its
simplicity. The sensing film material is dissolved in an organic solvent such as chloroform
and is sprayed over the electrode of the quartz resonator. Since the organic solvent soon
evaporates, only the sensing film is deposited over the electrode.
When odorant molecules are adsorbed onto the sensing film, its resonance frequency
decreases due to the mass loading effect.(37–38) It returns to its original frequency when thegas around the sensor is replaced with clean air. Thus, it is possible to measure the sensor
response repeatedly and reproducibly.
The sensitivity of a QCM gas sensor increases when its fundamental resonant frequency is raised. The quartz plate should be thinner as the resonant frequency becomes
higher. Thus, it was difficult to raise the fundamental resonance frequency higher than 30
MHz, because a thin quartz plate is easily broken. However, the VUHF band QCM can beused when the active region of the quartz plate is partially etched to support the thin quartz
plate by the thick plate around it.(39)The materials of sensing films are lipids and celluloses, the stationary phase materials
for gas chromatography. The materials are characterized using the partition coefficient of
the sensing film, the ratio of the vapor concentration in the film to that in the gas phase inequilibrium. We previously studied the responses of many sensors to many vapors using
an automated static measurement system to characterize the sensor coatings.(40) We selectSensors and Materials, Vol. 17, No. 7 (2005)369

the combination of sensing films for a given set of samples from the huge number ofcandidates using the statistic index.(41)

2.3 Mixture quantification technique
The problem to be solved is closely related to the quantification of the composition ofa mixture. When the linear relationship between the odor concentration and the sensor
response and the superposition theorem for all the component odors are valid, only thesimple techniques such as multiple linear regression or Partial Least Squares (PLS)(42) are
required. However, it is rare that the linear relationship is completely valid. In the case of
the quartz crystal microbalance sensor, the linearity is still insufficient even if the characteristics of that sensor are better than the other sensors.(42) Thus, an iterative method
together with feedback control was adopted in the odor recorder.
Several methods have been proposed to obtain the composition of a gas mixture using
PLS,(42) a typical artificial neural network,(43) a neural network tuned to semiconductor gas
sensors,(44) locally weighted regression,(45) extended disjoint principal component regression (PCR)(46) and nonparametric techniques.(47) Other techniques utilized the spectral data
for the quantification of mixtures.(48,49)
Our group proposed an active odor sensing system using an odor blender and anumerical optimization technique.(50,51) Our method is flexible, and no model for describing the characteristics of the sensor response to a gas mixture is required because of therelative measurement method, whereas all other methods required a model. Thus theaccuracy of the active odor sensing system is high because no error is caused by modelbuilding. Furthermore, the proposed system is also robust against environmental change
because of the relative measurement method. The odor recorder is based upon this active
odor sensing system.

2.4 Olfactory display
The olfactory display is a device to present smells to people. It should have the functionof blending component odors at an arbitrary ratio in the odor recorder. There are several
pieces of equipment to present the smells.
The simplest device to present odors is a scent diffuser. Although it is not possible tomix several components at arbitrary ratios, it is easy to generate a smell. Several types ofsmells can be generated when the scent cartridge is replaced. The computer-controlled
scent diffuser can be connected to the internet and can be controlled remotely to generatesmells.(8)Another method to generate smells is an olfactometer often used to measure theolfaction-induced brain wave potential.(52) The conditions of olfactory stimulus such asconcentration, duration and flow rate are precisely controlled using this equipment.
However, the equipment is large and expensive.
Several types of olfactory displays have also been developed.(7,13) Hirose developed an
olfactory display attached to a head mount display for a virtual environment.(9) It is
possible to let people sense how far away the object generating the smell is located.
Yanagida et al. developed the smell generator to carry smell in a specific direction using an
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The specifications required for olfactory display is summarized in Table 1. First, thenumber of smells to be generated is important. Since primary smells have not yet been
known, no equipment has been developed to emit any kind of smell. However, a variety ofsmells can be generated if component odors are blended at any composition. Second, thefunction of the concentration adjustment is required. A large dynamic range of odor
intensity is preferable. Third, the speed of the odor variation both in strength and quality isa factor to be considered since the odor in the ambient air changes dynamically andirregularly. Moreover, the direction to the object generating the smell and the area of smell
diffusion are important for the virtual environment.
Our group focused on blending aromas. Several methods are available to realize anaroma blender. The principles of olfactory display with the function of blending aromas
are shown in Fig. 3. The first method is to use mass flow controller (MFCs). A mixture
with any composition can be realized by adjusting the flow rates.(25) The MFC is goodequipment for controlling the flow rate precisely and electronically. Although the MFC istypically controlled using an analog voltage, a recent model of an MFC has a digitalinterface. However, it is not suitable for realizing a blender with many components
because of its cost. If a number of MFCs are integrated on a silicon chip using MEMS
technology,(53) a sophisticated olfactory display can be realized.
The second method is an inkjet-based olfactory display. The inkjet devices individually controlled by a computer are used to blend aromas of any composition. An organic
solvent-free device should be used to generate a smell. When we use the inkjet device, nota vapor but a liquid droplet is expelled from the nozzle. Since an odorant with high odor
intensity typically has a high boiling point, it is difficult for the odorant to evaporate
completely in spite of the tiny droplet size. Thus, the heater should be used to evaporate it
immediately and completely. In our case, a mesh heater was employed. The combinationof the inkjet device with a mesh heater enables the rapid change in concentration of theodor with high odor intensity, which often remains inside the plumbing in an MFC
system.(54)The third method is an olfactory display made up of solenoid valves. The solenoid
valve is originally a fluidic switching device and could not be used to express finedifferences in concentration. However, it becomes possible to express subtle concentration
changes using high speed switching followed by a fluidic low pass filter. Our groupapplied the delta-sigma modulation technique, a 1-bit A/D conversion technique, to switchof the solenoid valves.(55) A photo of that type of olfactory display is shown in Fig. 4. Eightcomponent odors can be blended based on an arbitrary recipe using this equipment. Since
Table 1
Specifications required for olfactory display.
No
1
2
3
4
5

Item
Number of smells
Adjustment of concentration (Dynamic range)
Speed of odor variation in quality and intensity
Direction to object producing smell
Area filled with smell

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Fig. 3. Principles of olfactory display with the function of blending smells. (a) MFC, (b) inkjet
device and (c) solenoid valve with high speed switch.

Fig. 4.

Photo of olfactory display using solenoid valves controlled by delta-sigma modulation.

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the solenoid valve is cheap, robust, and is easily controlled by a computer, it is not difficult
to realize an olfactory display equipped with many components using solenoid valves.

3.

Odor Recorder for Recording Recipes with Multiple Odor Components

In an odor recorder, multi-input multi-output (MIMO) feedback is used. This section
provides a description of the realization of MIMO feedback when the number of sensors isn and the number of components is m. The response vector and the concentration vector of
the blended odor at time kT are expressed by

sk = [s1 (k ), s2 (k ),.., si (k ),.., sn (k )]

T

(1)

and

[
]
= [c (k ) – c (k – 1), c (k ) – c (k – 1),.., c (k ) – c (k – 1),.., c (k ) – c (k – 1)]

uk = u1 (k ), u2 (k ),.., u j (k ),.., um (k )

T

T

1

1

2

2

j

j

m

m

(2)

where si(k), uj(k), cj(k) are the ith sensor responses, the change in the jth component
concentration and the change of the jth component concentration, respectively. The statespace equation is given by
sk +1 = Fsk + Guk .

(3)

In the optimal feedback control, the index value
p-1

{(

) (
T

)

J = ∑ s k+1 − s target Q s k+1 − s target + u k T Ru k
k =0

}

(4)

is minimized by modifying the recipe of the blended odor, where p is the number of theconcentration change during the odor-recipe exploration.(56) The first term in eq. (4)expresses the difference in the sensor response vector between the target odor and theblended one weighted by the diagonal matrix Q, and the second term expresses thedifference in the concentration-change vector weighted by a diagonal matrix R. The
second term is typically used to suppress the oscillation. The recipe of the blended odor isiteratively changed to decrease the index J. The method to determine Q and R is describedin reference.(57) Using method described herein, the apple aroma, jasmine scent, spice andcitrus aromas(58–60) were recorded.
The experimental setup is shown in Fig. 5. The odor blender was realized using 16
solenoid valves. The number of component odors was at most eight. The solenoid valves
were controlled using the second-order delta sigma modulation and were switched every
50 ms. The flow rate at the bottle for each component odor was always kept constant evenSensors and Materials, Vol. 17, No. 7 (2005)

Fig. 5.

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Experimental setup for the odor recorder.

if that odor component was not supplied to the sensors. Thus, the odor component flowed
to the bypass and air was supplied to the sensors instead of that component when thesensors were not exposed to that component. The flow rate through the bypass was
adjusted to be the same as that at the senor cell. QCMs (20 MHz, AT-CUT) with different
coatings were installed in the sensor cell made of stainless steel. The number of the sensors
was at most eight. The experiment was performed at room temperature. The frequencychanges of the oscillators connected to the QCMs were measured using a multi-channel
frequency counter, with its data transferred to a computer via RS232C interface. Then therecipe was calculated and the solenoid valves were controlled using that information. The
recipe modification was repeated until convergence. The target odor was in the headspaceof the one of the bottles. It could be also synthesized using the component odors. The apple
aroma composed of five main components was successfully recorded when its recipe was
kept constant.(57)4.

Odor Recorder for Recording Dynamic Change of an Odor

The study of the technique for recording and reproducing smells is becoming more
popular in the field of chemical sensors as well as that of virtual reality.(7) People working
in virtual reality watch these techniques because they want to include olfaction into fivesense communication.
The eight-component recipe of the apple aroma was successfully determined using theodor recorder.(60) Although a constant recipe was obtained in most of previous studies, theactual odor in the atmosphere is continuously and dynamically changing. Dynamic

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recording and regeneration are required to have much presence when a system presents
smells to people. Therefore, a technique of recoding dynamically changing odor has beendeveloped.

4.1 Odor recorder using feedback error learning
The first approach to recording a dynamic change in odor is based upon a neuralnetwork using feedback error learning.(61) Although the dynamic change in the composition of the mixture was quantified using a sensor array together with a recurrent-type neuralnetwork,(62) the change in the composition was too slow. Therefore, a neural network withfeedback error learning was adopted to track the dynamic recipe change quickly.(63) A
multilayer perceptron (MLP) with feedback error learning is usually used to control theactuators in a robot. It was reported that it was possible to track the target trajectory quickly
when an MLP with feedback error learning was incorporated.
The MLP is a typical neural network shown in Fig. 6(a). The MLP consists of an input
layer, an intermediate layer, often called the hidden layer, and an output layer.(64) It
typically has target inputs to adjust the weights so that the outputs of the MLP can be the(b)Fig. 6. Schematic diagram of odor recorder with feedback error learning neural network: (a)structure of the MLP, and (b) entire system.

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same as those inputs. The typical algorithm to adjust the weights is back propagation.(64,65)
The whole system is illustrated in Fig. 6(b). The system to be controlled is composedof an odor blender and a sensor array. The odor blender used here was an old one
consisting of several mass flow controllers. The recipe can be determined by the ratio of
the flow rates. The sensors were the QCMs coated with sensing films. Both the MFC andthe sensor have time constant of a few seconds. The inputs of the system are theconcentration changes of the component odors and the outputs are the sensor responses.
This is a discrete-time system, since the responses are obtained at discrete time.
First, a target odor with a dynamic recipe change is introduced into the sensor array, andthe time-series data for the response pattern are recorded. Then the feedback system
controls the recipe of the blended odor so that the response patterns to the blended odor
match with that of the target odor. The time-series data of the blended-odor recipe iselectronically recorded.
The outputs of the system to be controlled are fed back and the gain matrix is multiplied
after those outputs are subtracted from the inputs. The positive and negative signs in Fig.
6(b) mean that each sensor response to a blended odor is subtracted from the current eachsensor response to the target odor. The gain matrix determines the concentration changesof the component odors at the next time from the concentrations at the current time. The
outputs of the gain matrix are zeros if the response pattern of the target odor matches thatof the blended one.
Moreover, the outputs of the neural network are added to the concentration changes of
the component odors. There are no target inputs to the neural network, whereas the outputsof the gain matrix are used as the learning signals for the neural network. The neuralnetwork was trained simultaneously with the control of the blended-odor recipe in the same
way as in the manipulator control of the robot. The learning stops after sufficient training
because there are no feedback errors. Then the fast tracking of the target odor is expected,
since the feed forward control of the neural network dominates over the feedback control of
the gain matrix.
The neural network used in this study was a three-layer MLP. The inputs to the neuralnetwork were the sensor outputs at the current time and those one discrete time unit before
the current time. The data at different times were used because the delay of the sensor
response must be taken into account. Since there were five sensors, there were ten input
layer neurons. The output of the intermediate-layer neuron was expressed as sigmoid
function plus a constant term, so it can have a value between –1/2 and +1/2. The output of
the output-layer neuron was linear with the sum of the weighted outputs in the intermediate-layer neuron, since its output should not be restricted to a certain range in the case of thesigmoid function.
The gain matrix was obtained using the theory of optimal feedback. The sensor
dynamics model is described using a first-order state equation that is indispensable todetermine the gain matrix. The coefficients of the state equation were determined from theobserved data and the least squares method when each component odor concentration waschanged according to a certain sequence.(16)Using this system, the experiment was carried out. The two component odors used here
were trans-2-hexenyl acetate (Component odor 1) and isobutyric acid (Component odor 2).

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Those are the components of the apple aroma previously described. The five QCMs (20MHz, AT-CUT) coated with silicone GF SF-96, silicone OV-17, (R)-(+)-2,2’-bis(diphenylphosphine)-1,1’ (BINAP), Apiezon-L and polyphenyl ether.
After the simple concentration profile was studied, the complicated concentration
profile was tested. The result is shown in Fig. 7. The concentration changes often occur inan open field where the target odor is spouted in the atmosphere. Since the target odor isusually carried by wind accompanied with turbulence, its concentration changes arecomplicated. The MLP was initially trained using different concentration profiles, andthen the complicated profile was tested. The concentration of each component odor in theblender tracked that in the target odor without any delay. Thus, the dynamic change in theodor was successfully recorded using the MLP with feedback error learning.

4.2 Odor recorder using a real-time reference method
Since the odor recorder using feedback-error-learning was somewhat complicated andconsumed much time for neural-network training, the real-time reference method forrecording dynamic changes of odor was developed.(66) This method is useful for recording
dynamic changes of odor as well as for compensating for change in the environment suchas temperature and humidity.(67)
The comparison of the real-time reference method with the conventional method is
illustrated in Figs. 8(a) and 8(b). Although the multiple component odors and the multiple
sensors are actually used, only single component odor and single sensor are shown here forsimplicity.
In the previous method, the results from which are shown in Fig. 8(a), a steady-state
response to the target odor with constant concentration was measured for the first time.

Fig. 7.

Experimental results of recording the dynamic change of odor using a neural network.(63)377

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Fig. 8. Comparison of the real-time reference method with the conventional method in the odor
recorder. (a) conventional method and (b) real-time reference method.

Then the recipe of the blended odor was adjusted so that the response to the blended odor
matched that to the target odor. Since it takes a few minutes to determine the concentration,changes during the process of recipe determination cannot be detected.
On the other hand, the target and blended odors were alternately introduced into thesensor array at every sampling interval (several seconds) in the real-time reference methodin Fig. 8(b). Although the sensor response to the blended odor deviates at first from that tothe target odor due to the concentration difference, it soon approached the response to thetarget odor.
In the real-time reference method, the index value J is modified to

J = (sk + 2 – sk +1 ) Q(sk + 2 – sk +1 ) + (ck+1 – ck–1 ) R(ck+1 – ck–1 ),
T

T

(5)

where k is an even number. The first term means the difference between the sensor
responses to the target odor and those to the blended odor, and the second term minimizes
the concentration changes of the blended odor. Once convergence occurs, the blended odor
concentration tracks that of the target odor. The real-time reference method achieves a378

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time resolution of a few seconds to record dynamic changes of odor. This method is also
effective to compensate for the rapid environmental changes encountered during theprocess of recipe determination.(67)
The next experiment on recording a dynamically changing odor using the real-timereference method is described in the following. The concentrations of four odor components of the apple flavor were independently changed in the experiment. The odor
components used here were trans-2-hexenyl acetate (green note, Comp1), trans-2-hexenal(smell of grass, Comp2), isobutyric acid (sour sweet, Comp3) and ethyl valerate (fruity,
Comp4). The sensors used here were four quartz resonators (20 MHz, AT-CUT) coatedwith polphenyl ether, polyethylene glycol 1000, tricresyl phosphate and Apiezon L. The
sampling interval was 4 s. The vertical axis defines the concentration of each component
odor relative to the full-scale concentration with the unit [%RC].
The experimental results when the temperature was changed in spite of the constantconcentrations of the target odor are shown in Figs. 9(a) and 9(b). The increase intemperature due to heating the sensor cell was 5°C for 100 s. In the conventional method,
the convergence was not successful because of the dependency of the sensing-filmcharacteristics upon the temperature given in Fig. 9(a). In the real-time reference method,
however, good convergence was achieved in spite of the temperature change given in Fig.
9(b).
Then the temperature and humidity were intentionally changed during the experimentto evaluate the robustness against the temperature and humidity changes. The change in
the air conditioner mode (DRY/COOL) caused 2.5°C of the temperature change and 20%of the humidity change during the experiment.
The experimental results are shown in Figs. 10(a) and 10(b). The solid and dashed lines
are the concentrations of the component odors in the target odor, and the plots are therecorded concentrations of the component odors. Since the target odor was generatedusing the odor blender in this experiment, the concentration of each component odor in thetarget odor is the set point in the odor blender.
Since the recorded concentration of each component odor agreed closely with that of
the target odor, the real-time reference method achieves a record of the dynamic change ofodor even under an environment of temperature and humidity changes shown in Fig. 10(b).
The real-time reference method can be speeded up when the blended odor is measured
simultaneously with the target odor using the same two sensor arrays.

4.3 Recording odor more rapidly
In the real-time reference method, the target and blended odors are alternately measured for every sampling interval. When the two sensor arrays are simultaneously used as
illustrated in Fig. 2, more rapid changes in odor can be measured.(68) The sensor array
response pattern for the target odor is measured by the first sensor array, and the data are
immediately transferred to the system for the blended odor. The recipe for the blended
odor is adjusted so that the sensor responses to the blended odor in the second sensor arraycan be the same as those to the target odor in the first sensor array.
The characteristics of the two sensor arrays should be the same with good accuracy.
However, it is difficult to fabricate multiple QCM sensor arrays with exactly the same

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(a)

(b)
Fig. 9. Influence of temperature change on recording odor. (a) Conventional method and (b) realtime reference method.

characteristics. The spray coating method is often used because of its simplicity. However, it is difficult to obtain a uniform sensing film. Although coating technique using an
atomizer may improve the uniformity of the sensing film,(40) one can compensate for thedifference using the state equations. The index value corresponding to that in eq. (3) was
modified, considering the two sensor arrays for the target odor and for the blended odor are
different.(68)
Then the experiment was performed. The two odor samples, trans-2-hexenyl acetate(green note) and isobutyric acid (sour sweet) were used as the odor components of the odor

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(a)

(b)
Fig. 10. Recorded dynamic concentration change of each component vapor in the target odor. Line:concentration of each component vapor in the target odor; Plot: recorded concentration of eachcomponent odor. (a) Comparison between target and blended odors, and (b) temperature andhumidity changes during the experiment.(66)blenders. They were also used in the previous section. The sensors used here were the
QCM sensors (AT-CUT, 20 MHz) coated with polyethylene glycol 1000 (PEG1000) andpolyphenyl ether, stationary phase materials for gas chromatography. The samplinginterval of the QCM sensor was 4 s.
The first experiment was to record the dynamic changes of the odor generated by theodor blender. The concentration profile of each component odor was controlled in thiscase. The increased speed of the system in comparison with the previous method wasconfirmed, and the recipe change over 10 s was precisely recorded.(68)
Then the experiment to record the recipe change in the atmosphere was performed. The
difficulty in recording increases in the presence of turbulent airflows and environmental
changes. The odor samples of trans-2-hexenyl acetate, isobutyric acid and a mixture of the381

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(a)

(b)

Fig. 11. Results of recording dynamic odor changes in the atmosphere: (a) Sensor responses and (b)results of concentration-change profile. (time 1) pure trans-2-hexenyl acetate, (time 2) pureisobutyric acid and (time 3) mixture of trans-2-hexenyl acetate and isobutyric acid.(68)

two were prepared in three petri dishes. The odors evaporated from the petri dishes in theambient air were used as the target odors. The experimental results are shown in Fig. 11.
The first odor sample, trans-2-hexenyl acetate, was placed under the inlet of a Teflon
tube connected to the sensor cell from 30 s to 90 s (time 1). After supplying air from 90 to
150 s, the second odor sample, isobutyric acid, was placed under the inlet from 150 to 210
s (time 2). After supplying air from 210 s to 270 s, the third sample, a mixture of the two,
was placed under the inlet from 270 to 330 s (time 3). From 330 to 440 s, the air was
supplied to the sensor cell.
As can be seen in Fig. 11(a), the sensor responses were rapidly and irregularly changed
because of the turbulent airflow in the atmosphere. Moreover, it is clear from Fig. 11(b)that the odors in the atmosphere were successfully recorded, because the reproduced odors
during times 1, 2 and 3 were almost pure trans-2-hexenyl acetate, pure isobutyric acid anda mixture of the two, respectively. Thus, the proposed system can be used in recording the
dynamic changes in odors even in the atmosphere.

5.

Conclusions

The new idea of an odor recorder to record dynamic changes of odor was described.
The feedback-error-learning method could be used to record the dynamically-changing
odor. Moreover, the dynamically-changing odor was successfully recorded in spite of
temperature and humidity changes using the real-time reference method. The rapidly

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changing odor in an open field was recorded using the two sensor arrays and the feedback
control algorithm. The odor recorder can be used to record more complicated odors
accompanied with the dynamic changes.

Acknowledgements
The author wishes to thank former students, Dr. T. Yamanaka (Texas A&M University)and Ms. H. Hiramatsu (Matsushita Electric Industrial Co., Ltd.)) for their previous works
on the odor recorder.

References
1 T. C. Pearce, S. S. Schiffman, H. T. Nagle and J. W. Gardner eds.: Handbook of machine
olfaction (Wiley-VCH, 2003).
2 K. C. Persaud and G. Godd: Nature 299 (1982) 352.
3 M. Kaneyasu, A. Ikegami, H. Arima and S. Iwanaga: IEEE Trans. Components, Hybrids andManufacturing Technology, CHMT-10 (1987) 267.
4 T. Nakamoto and T. Moriizumi: Proc. IEEE Ultrason. Symp. 1988, p. 613.
5 H. T. Nagle, S. Schiffman and R. Gutierrez-Osuna: Spectrum, IEEE 35 (1998) 22.
6 J. W. Gardner and P. N. Bartlett: Sens. Actuators, B 18 (1994) 210.
7 J. J. Kaye: ACM Interaction (2004) 49.
8 J. Messager: 3rd Aroma Science Forum (2002) 2 (in Japanese).
9 H. Hirose: Aroma Research 10 (2002) 60 (in Japanese).
10 Y. Yanagida, S, Kawato, H. Noma, A. Tomono and N. Tetsutani: Proc. IEEE Virtual Reality
2004, 2004, p. 43.
11 T. Nakamoto, Y. Nakahira, H. Hiramatsu, T. Moriizumi: Sens. Actuators, B 76 (2001) 465.
12 J. E. Amoore: Molecular basis of odor (Charles C Thomas Publisher, Springfield, 1970).
13 F. Davide, M. Holmberg and I. Lundstrom: Communication through Virtual Technologies, eds.
G. Riva and F. Davide (IOS Press, Amsterdam, 2001) p. 193.
14 P. E. Keller, R. T. Kouzes, L. J. Kangas and S. Hashem: Interactive technology and the newparadigm for healthcare, eds. K. Morgan, R. Satava, H. B. Sieburg, R. Matteus and J. P. Christensen(IOS Press, Amsterdam, 1995) p. 168.
15 Y. Matsushita: Aroma Research 10 (2002) 42 (in Japanese).
16 T. Nakamoto, K. Okazaki and T. Moriizumi: Sens. Actuators, B 41 (1997) 183.
17 T. Moriizumi, T. Nakamoto and Y. Nakahira: Japan Patent, 2002, No. 3331371 (in Japanese).
18 H. V. Shumer and J. W. Gardner: Sens. Actuators, B 8 (1992) 1.
19 K. Ema, M. Yokoyama, T. Nakamoto and T. Moriizumi: Sens. Actuators 13 (1989) 476.
20 D. S. Ballantine, S. L. Rose, J. W. Grate and H. Wohltjen: Anal. Chem. 58 (1986) 3058.
21 H. Baltes, D. Lange and A. Koll: IEEE Spectrum 35 (1998) 35.
22 Q. Cai, J. Park, D. Heldsinger, M. Hsieh, E. T. Zellers: Sens. Actuators, B 62 (2000) 121.
23 J. V. Hatfield, P. Neaves, P. J. Hicks, K. Persaud and P. Travers: Sens. Actuators, 18–19 (1994)
221.
24 A. J. Doleman and N. S. Lewis: Sens. Actuators, B 72 (2001) 41.
25 H. Sundgren, I. Lundstrom, F. Winquist, I. Lukkari, R. Carlsson and S. Wold: Sens. Actuators,
B 2 (1990) 115.
26 L. Pillonel, J. O. Bosset, R. Tabacchi: Eur. Food Res. Technol. 214 (2000) 160.
27 U. Bindig, W. Katzung, H. Mauch, J. Leonhardt and G. Muller: Technical Digest of 9th

Sensors and Materials, Vol. 17, No. 7 (2005)

28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68

383

International Symposium on Olfaction and Electronic Nose (2002) 106.
E. J. Staples: Proc. IEEE Ultrasonic Symposium 1999, p. 417.
J. White, J. S. Kauer, T. A. Dickinson and D. R. Walt: Anal. Chem. 68 (1996) 2191.
J. R. Stetter, P. C. Jurs and S. L. Rose: Anal. Chem. 58 (1986) 860.
T. Nakamoto and T. Moriizumi: Jpn. J. Appl. Phys. 29 (1990) 963.
K. K. Kanazawa and J. G. Gordon: Anal. Chim. Acta. 175 (1985) 99.
H. Ishida, T. Tokuhiro, T. Nakamoto and T. Moriizumi: Sens. Actuators, B 83 (2002) 256.
S. Munos, T. Nakamoto and T. Moriizumi: Sensors and Materials 11 (1999) 427.
I. Sugimoto, M. Nakamura and H. Kuwano: Anal. Chem. 66 (1994) 4316.
S. Munos, T. Nakamoto and T. Moriizumi: Sens. Actuators, B 105 (2005) 144.
G. Sauerbrey: Z. Phys. 155 (1959) 289.
W. H. King: Anal. Chem. 36 (1964) 1735.
T. Nakamoto, Y. Suzuki and T. Moriizumi: Sens. Actuators, B 84 (2002) 98.
K. Nakamura, T. Nakamoto and T. Moriizumi: Tech. Dig. Sensor Symp. (2000) B4-2.
T. Nakamoto, S. Sasaki, A. Fukuda and T. Moriizumi: Sensors and Materials 4 (1992) 111.
W. P. Carey, K. R. Beebe and B. R. Kowalski: Anal. Chem. 59 (1987) 1529.
H. Sundgren, F. Winquist and I. Lundstrom: Tech. Digest, Transducers 91 (1991) 574.
Z. Wang, J. N. Hwang and B. R. Kowalski: Anal. Chem. 67 (1995) 1497.
Z. Wang, T. Isaksson and B. R. Kowalski: Anal. Chem. 66 (1994) 249.
E. T. Zellers, S. A. Batterman, M. Han and S. J. Patrash: Anal. Chem. 67 (1995) 1092.
W. P. Carey and S. S. Yee: Sens. Actuators, B 9 (1992) 113.
S. Nakata, S. Akakabe, M. Nakasuji and K. Yoshikawa: Anal. Chem. 68 (1996) 2067.
M. E. H. Amrani, R. M. Dowdeswell, P. A. Payne, K. C. Persaud: Sens. Actuators, B 44 (1997)
512.
T. Nakamoto, S. Utsumi, N. Yamashita and T. Moriizumi: Sens. Actuators, B 20 (1994) 131.
T. Nakamoto, H. Matsushita and N. Okazaki: Sens. Actuators, A 50 (1996) 191.
M. Tonoike: Fragrance Journal, 9 (1990) 49 (in Japanese).
S. Shoji and M. Esashi: J. Micromechanics and Microengineering 4 (1994) 157.
T. Nakamoto, H. Takigawa and T. Yamanaka: Trans. IEICE, E87-C (2004) 2081.
T. Yamanaka, R. Matsumoto and T. Nakamoto: Sens. Actuators, B 87 (2002) 457.
S. Tanaka: Measurement system engineering (Asakura Shoen, 1994) (in Japanese).
T. Yamanaka, R. Matsumoto and T. Nakamoto: Sens. Actuators, B 89 (2003) 112.
B. Wyszynski, T. Yamanaka and T. Nakamoto: Tech. Dig. of Sensor Symp. (IEEJ, 2003) 181.
T. Yamanaka, R. Matsumoto and T. Nakamoto: Sens. Actuators, B 89 (2003) 120.
T. Yamanaka, T. Matsumoto and T. Nakamoto: IEEE Sensors Journal 3 (2003) 468.
H. Miyamoto, M. Kawato, T. Setoyama and R. Suzuki: Neural Networks 1 (1988) 251.
M. Schweizer-Berberich, J. Goeppert, A. Hierleman, J. Mitrovics, U. Weimar, R. Rosentiel and
W. Goepel: Sens. Actuators, B 26–27 (1995) 232.
T. Nakamoto and H. Hiramatsu: Sens. Actuators, B 85 (2002) 263.
D. E. Rumehart, J. L. McClelland and PDP Research Group: Parallel distributed processing(MIT Press, Cambridge, 1986) p. 318.
D. E. Rumelhart, G. E. Hinton and R. J. Williams: Nature 323 (1986) 533.
T. Yamanaka and T. Nakamoto: Sens. Actuators, B 93 (2003) 51.
T. Yamanaka and T. Nakamoto: IEEJ, Transactions on Sensors and Micromachines, 122-E(2002) p. 317 (in Japanese).
T. Yamanaka, K. Yoshikawa and T. Nakamoto: Sens. Actuators, B 99 (2004) 367.