Calorimetric detection of volatile organic compounds

Calorimetric detection of volatile organic compounds

Sensors and Actuators B 70 Ž2000. 57–66 www.elsevier.nlrlocatersensorb Calorimetric detection of volatile organic compounds J. Lerchner ) , D. Caspar...

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Sensors and Actuators B 70 Ž2000. 57–66 www.elsevier.nlrlocatersensorb

Calorimetric detection of volatile organic compounds J. Lerchner ) , D. Caspary, G. Wolf Institute of Physical Chemistry, TU Bergakademie Freiberg, Leipziger Straße 29, D-09596 Freiberg, Germany Received 20 October 1999; accepted 10 April 2000

Abstract A calorimetric sensor arrangement was developed to detect volatile organic compounds. The heat power which is generated by the sorption or desorption of the vapor at a receptor layer and which is converted into a voltage signal by silicon thermopile chips was used as input parameter for the multicomponent analysis. If the design and operation conditions have been properly adjusted, calorimetric sensors are useful for measurements in the lower ppm range as the reported results show. In order to demonstrate the improvement of the discrimination power of an electronic nose by incorporating calorimetric sensors, the developed sensor arrangement was adapted to the electronic nose MOSES II. According to the calculated Mahalanobis distance, the improvement of the discrimination power by combination with other types of sensors depends on the nature of the samples. In some cases, best results were obtained with combinations including the calorimetric sensors. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Electronic nose; Calorimetric sensor; Thermopile chip; Sensor arrays; Gas sensors

1. Introduction At present, one can notice two different trends in the development and application of electronic noses. Because the combination of an increasing number of sensors of the same detection principle seems to yield little gain in discrimination power, the design of hybrid electronic noses became popular. Hybrid electronic noses incorporate sensors of different detection principle as for example metal oxide sensors, quartz microbalances, conducting polymer sensors or electrochemical sensors w1–3x. Unfortunately, the effort of such complex systems is rather high, which restricts these systems to laboratory application. On the other hand, there is a need for low cost and transportable instruments, i.e. for routine, field or home applications. As a consequence, an increasing offer of more or less simple constructed multisensor arrays for application as electronic noses can be observed w4–6x. But now, newer developments also show w7x that hybrid sensor arrays at a miniaturized level are possible. It is therefore still profitable to search for new sensing principles that are suitable for gas detection. Some years ago, first attempts were made to measure the heat power of the analyte–receptor interaction ) Corresponding author. Tel.: q49-3731-39-2125; fax: q49-3731-393588. E-mail address: [email protected] ŽJ. Lerchner..

as a sensor signal w8x. However, the parameters of the applied thermopiles as well as the non-optimized construction of the sensor arrangement did not permit any satisfactory result. The situation has been changed with the availability of solid state integrated thermopile chips. Using this new type of heat power transducers, we have constructed miniaturized calorimeters for a wide variety of applications w9x: e.g. for the detection of heats of reactions in microliter samples, the monitoring of temperature induced reaction in small solid samples and also for the measurement of heats of solid–gas interactions. With silicon integrated thermopile chips, we obtained a heat resolution of less than 100 nJ for absorption measurements on thin layers w10x. This was the prerequisite for the first relevant and successful application of the calorimetric detection principle in an electronic nose. Some details of the design of the sensor arrangement we have already published w11,12x. In this paper, we will present a more detailed discussion of the signal generation in a calorimetric sensor. Furthermore, we will demonstrate the increase of the discrimination power due to the incorporation of calorimetric sensors into a hybrid electronic nose. In order to study the latter, the developed sensor arrangement was adapted to the electronic nose MOSES II ŽLennartz Electronic, Tubingen, ¨ Germany.. We have decided in favour of MOSES II because it provides the most favourable conditions for a flexible configuration of different sensor types.

0925-4005r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 0 0 . 0 0 5 5 4 - 2

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2. Experimental 2.1. The heat–power transducer The calorimetric sensors detect heat flows from reversible reactions of active layers with analyte gases by measuring the thermal voltage that is generated by monolithic integrated silicon thermopiles. Fig. 1 shows an array of four thermopiles Žquadrupol chip. used in our sensor arrangement. Each thermopile consists of 164 p-type silicon–aluminium thermocouples which are connected in series and integrated in an n-type epilayer grown on the silicon wafer w13x. The bulk material underneath the epilayer is removed by etching in order to obtain a sufficient thermal isolation between the coated area of the chip and the silicon rim. The resulting membrane has a thickness of a few micrometers. The proper design of the thermopile chip determines essentially the performance of the sensor arrangement. As the most important parameters of the thermopile chip, the temperature coefficient of the thermal voltage, ´ , the thermal resistant of the membrane, R TP , and the size of the active area, A, must be considered. The temperature coefficient of the thermal voltage arises from the number of the integrated thermocouples N and their Seebeck coefficient a . There are two possibilities to optimize the thermal sensitivity Sth s dUTP r d q˙ ŽUTP — thermal voltage, q˙ — heat power.: either to increase the Seebeck coefficient or the thermal resistance of the membrane. If dielectric membranes are used, the thermal resistance is relatively large. Kohler et al. w14x have developed ¨ a thermoelectric thin-film system including 76 thermocouples of antimony and bismuthrantimony ŽBi 0.87 Sb 0.13 . thin films. The thin-film stack of metal and insolating layers is deposited on a membrane of a Si 3 N4rSiO 2 sandwich layer system with a thickness of 0.8 mm. The low thermal conductivity of the material as well as the small thickness of the membrane result in a thermal resistance of approxi-

Fig. 1. Structure of the applied thermopile chips.

Fig. 2. Calorimetric block: aluminium frame with two mounted quadrupole chips Žlower part. and the heat exchanger Župper part.. Both parts are screwed together. The heat exchanger consists of two aluminium plates containing the milled gas inlet and outlet tubes.

mately R TP s 300 K Wy1 related to an air environment. By a temperature coefficient of ´ s 0.01 V Ky1 , a thermal sensitivity of Sth s 3 V Wy1 was obtained. The high thermal sensitivity of Sth s 20 V Wy1 , which was obtained by Koll et al. w15x for their calorimetric chemical sensor, is also mainly determined by a remarkably high thermal resistance of the membrane. In contrast, our chip membranes consist of n-doped silicon resulting in a considerable lower thermal resistance Ž R TP s 95 K Wy1 .. On the other hand, the Seebeck coefficient of the p-siliconrAl thermocouples is extraordinary Ž a S s 400 mV Ky1 ., so that a sufficiently high thermal sensitivity for the used thermopile chip is nevertheless reached Ž Sth s 6.7 V Wy1 .. In our opinion, the maximization of the temperature coefficient of the thermal voltage Ž a S , N . should have the priority because by increasing thermal resistance, the influence of the heat exchange between surface of the membrane and ambient gas atmosphere and hence the intensity of external distortions increases too. Furthermore, the size of the chip membrane has to be discussed. Assuming homogenous coating and constant reaction conditions, the generated heat power depends linearly on the area A of the active part of the membrane. Thermopile chips of two different dimensions were available, one with an active area of 2 = 2 mm2 ŽLCM 2506. and the other one with 4 = 4 mm2 ŽLCM 2524.. Both chips are developed and manufactured by Xensor Integration ŽDelft, NL.. Because of the size requirements for an array of eight thermopiles, we have selected the LCM 2506 chips, accepting a little loss of chemical sensitivity. A further reduction of the area on less than 1 mm2 as for example described in Ref. w16x leads to non-acceptable results despite of higher thermal sensitivities.

J. Lerchner et al.r Sensors and Actuators B 70 (2000) 57–66

Fig. 3. Gas flow circuitry.

2.2. Design of the sensor arrangement The sensor arrangement contains two thermopile arrays, each mounted in a ceramic chip carrier. The two quadrupol chips are housed in a small aluminium frame ŽFig. 2.. The seperate inlet channels for the analyte and reference gas are performed as heat exchanger in order to ensure defined gas temperatures. Each gas flow is split into eight partial flows Žnot seen in Fig. 2. corresponding to the eight thermopiles. Analyte and reference channels are combined immediately above the entry into the reaction chamber. Thus, premixing of analyte and reference gas is avoided and a sufficient symmetry of the sorption and desorption process is obtained. The reaction chamber is the free volume above the surface of the chip membranes. The

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aluminium frame containing the thermopile chips is covered with the heat exchanger block and a bottom plate making the calorimeter block. On the bottom plate, a foil heater is attached. The heater serves as the temperature control of the calorimeter block. Seven of the eight thermopile chips are coated with different chemically sensitive polymers ŽMOTECH, Reutlingen, D; because of copyright reasons the polymers are not specified in detail.. The thermopile chips and the QMB sensors in MOSES are coated with the same polymers. The non-coated thermopile can be used as reference. The coating was done by dropping a solution of polymerrdichloromethane onto the active area of the membrane. It is important to cover this region as precisely as possible. In contrast to the spray-coating method, which leads to a complete covering of the whole membrane, only in the active area does absorption take place and hence the sensitivity will not be reduced by any absorption outside the active area that does not contribute to the signal Žsee Section 3.. In heat power measurements, only changes of the concentration in the gas phase lead to measurable sensor signals. Therefore, fast and rectangular-shaped partial pressure variations are necessary. This can be performed by alternate switching of the gas flow between inert and analyte containing gas. Fig. 3 shows the principle of the gas flow. The third valve is used to introduce the outlet gas flow into the gas channel of the MOSES system during the sorption phase. The bypass which connects sample input and output is necessary to disconnect the sensor arrangement from the system gas flow. The sensor arrangement is designed as a MOSES compatible module ŽFig. 4.. All components of the module are

Fig. 4. Calorimetric module with calorimetric block, valves, internal pump and electronic unit.

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J. Lerchner et al.r Sensors and Actuators B 70 (2000) 57–66 Table 1 Mean sensitivities of the calorimetric sensors Žchip 6 is not coated. Chip no.

1 2 3 4 5 6 7 8

Noise ŽnV.

280 270 418 231 259 – 313 295

Sensitivity ŽnVrppm. Ethanol

Toluene

n-Octane

n-Propanol

55.02 28.36 36.52 5.01 47.87 y0.42 22.59 39.83

110.44 38.69 237.88 57.05 121.17 y1.42 213.43 208.38

141.11 21.9 329.87 96.34 217.43 y1.19 407.43 294.08

93.75 75.67 167.46 26.25 40.21 y0.37 227.1 134.85

Fig. 5. Thermal voltage signals Žthree-fold accumulated. due to concentration pulses of octane at 50 and 200 ppm, respectively.

mounted in a standard 19-in. plug-in unit. The module can be switched into a gas channel via 1r8 in. swagelok connectors. Apart from the calorimeter, the pump and the valves, the module also contains the electronic control and data processing unit that is divided into an analogue and a digital electronic part. The former contains the signal preconditioning of the thermopile signals and the temperature signals of the chip packages and also the driver circuits for the control of the chip-heaters, the valves and the pump. As final results of the data processing, the peak parameters of accumulated sensor signals are calculated. These information can be transferred to the master system Že.g. electronic nose. via the installed I 2 C-interface. 2.3. Signal processing All eight thermopile voltage signals are prefiltered and amplified individually and digitalized after multiplexing. The primary sampling time is 4 ms. After an additional two-stage software filtering, a decimation to a sampling time of 80 ms takes place. The signal noise after filtering is lower than 200 nV at zero gas flow and 600 nV at a flow rate of 20 ml miny1 Žwith a recalculated gain of 1000.. The data acquisition is synchronized with the peri-

odical switching of the gas flow in order to do a real-time accumulation of data sets of successive sorption–desoption periods. Thus, the signal-to-noise ratio can be increased with the number of accumulation steps approximately in a square route relationship w10x. Fig. 5 shows typical thermopile signals measured for octane Ž50 and 200 ppm.. At each measurement, three accumulation steps have been applied. The positive peak is due to the absorption and the negative one is due to the desorption process. Due to the high switching rate of almost 0.5 Hz, the equilibrium state of the sorption is not achieved. Thus, dynamic parameters effect the signals. The concentration information can be drawn from the different peak parameters such as the maximum or the area of the absorption or desorption peaks. Fig. 6 shows the dependence of the mean value of the absorption peak Žit corresponds to the peak area. on the concentration of octane for the seven differently coated thermopiles. As can be seen, there is an almost linear dependence of the signal on the analyte concentration. In Table 1, the sensitivities of the sensor array that are calculated from the slope of curves as shown in Fig. 6 are summarized for some organic compounds. Regarding the measured base line noise ŽTable 1., in the best cases a concentration resolution of approximately 1 ppm seems possible.

3. Analysis of the signal transfer 3.1. Modeling of the signal generation For a better understanding of the signal generation and to find optimal design and operation conditions, we have tried to model the signals of the sensor arrangement. The following assumptions have been made: v

v

v

Fig. 6. Concentration dependence of the output signals of the calorimetric module for octane at three-fold accumulation. Chip no. 6 is not coated.

v

constant flow rate of the gas, rectangular concentration change at the entry of the reaction chamber, perfect mixing within the reaction chamber, i.e. uniform concentration distribution, diffusion in an uniform and cylindrical receptor layer,

J. Lerchner et al.r Sensors and Actuators B 70 (2000) 57–66 v

v

v

v

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the conductive heat transfer between receptor and gas flow is negligible concerning the convective heat transfer, the heat power is produced close at the surface of the receptor layer, the reference temperature of the thermopiles is temporally and spatially constant, a mutual influence of adjacent thermopiles is neglected.

A schematic drawing of the model is shown in Fig. 7. It corresponds to the notation of models in a SIMULINK software environment ŽMATLAB-SIMULINK-REALTIME WORKSHOP, The Math Works, Natick, USA., which we used as implementation platform and applied for simulations and parameter estimations Žfor details of the model description see Ref. w16x.. The first part of the model describes the flattening of the concentration step due to mixing and absorption. It calculates the concentration cgas within the reaction chamber for a given concentration step c in according to Eq. Ž1. and considers the volume VR of the reaction chamber, the flow rate Õ˙ and the absorption mass flow into the receptor layer n˙ abs . The INT-block in the scheme symbolizes a numerical integration procedure. c˙gas s

n Ž c in y cgas . y n˙ abs VR

.

Ž 1.

The absorption mass flow n˙ abs is controlled by diffusion, which can be calculated by Fick’s law. In our model, a state-space variable representation of the diffusion system Ždiffusion block. is applied corresponding to a discretion of the receptor coating into a limited number of layers with uniform analyte concentrations: csAcqBc ˙ 0

Ž 2.

Fig. 8. Comparison of simulated and measured signals at different flow rate ŽŽa. 40 ml miny1 , Žb. 70 ml miny1 , Žc. 100 ml miny1 ; 40 mg polymer; 1000 ppm octane.. The simulated curves were obtained by simultaneous fit of the three signals.

n˙ abs sCcqDc0 . The vectors c and c˙ represent the analyte concentrations in the layers Žstate variables. and their derivatives with time. Input variable is the concentration in the upper layer c 0 immediately following the concentration in the

reaction chamber cgas multiplied by the distribution coefficient K c ŽEq. Ž3... c 0 s K c cgas

Ž 3.

A discretion of the receptor coating into four layers yields the following equations for the matrices in Eq. Ž2. y5rt 1rt As 0 0

1rt y2rt 1rt 0

0 1rt y2rt 4rt

0 0 , 1rt y4rt

4rt Bs 0 , 0 0 Fig. 7. Scheme of the model for the sensor arrangement with the typical shape of signals at marked points.

Cs y

Frec d rec

t

000 ,

Ds

Frec d rec

t

Ž 4.

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DT across the thermopile can be described by the differential equation Crec

dDT

1 s

dt

R rec R TP

q1

q˙ y

ž

1

1 q

R TP q R rec

R air

/

DT ,

Ž 6.

where Crec is the heat capacity of the absorbent, R rec the heat resistance of the absorbent, R TP the heat resistance of the membrane with the thermopiles and, R air the resistance for the heat transfer to the gas flow. From the solution of the differential equation, the thermal voltage UTP can be derived regarding the thermal sensitivity Sth of the thermopiles: UTP s Sth DT .

Ž 7.

3.2. Verification of the model For a more exact dosing of the polymer, we have not used the quadrupol chips but a single thermopile chip LCM 2524 with the larger active area to test the model. In Fig. 8, the sensor response measured for a rectangular concentration step at different flow rates is compared with simulated signals. The static model parameters D Habs , K C and Sth can be summarized as a constant, which are determined experimentally from the integral of the measured signal peak. The other unknown or not exactly known quantities as the diffusion coefficient of the absorbent, the active volume of the reaction chamber and the distribution coefficient for the absorption have been fitted to the three sets of measuring data simultaneously. Because of the correlation between the parameters, the simultaneous fit is necessary to obtain non-ambiguous results. The parameter estimation was performed using the NCD Toolbox in SIMULINK. As seen in Fig. 8, there is a reasonable agreement between the measured and the simulated data. Measurements with variable receptor mass also Fig. 9. Comparison of simulated and measured signals at different receptor mass ŽŽa. 40 mg, Žb. 160 mg, Žc. 400 mg; flow rate 60 ml miny1 , 1000 ppm octane.. The simulated curves were obtained by simultaneous fit of the three signals.

with the first order diffusion time constant

ts

1 D0

d rec

ž / 4

2

,

Ž 5.

where the diffusion coefficient D 0 , the thickness d rec and the area Frec of the receptor coating. The generated heat power is directly related to the absorption mass flow n abs by multiplying the absorption enthalpy D Habs . Regarding heat conduction within coating and membrane as well as heat loss due to heat exchange between surface and gas flow, the temperature difference

Fig. 10. Simulated flow rate dependence of the signal amplitude with Žb. and without Ža. considering of the heat exchange between surface and gas flow.

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Fig. 11. Dependence of the integrated thermal voltage signal on the receptor mass. To obtain absorption equilibrium the modulation period was 24 s.

yield a good agreement between the experimental and the simulated data ŽFig. 9.. From the mass dependence of the estimated first order diffusion time constant and the known geometry of the receptor layer, we have calculated the diffusion coefficient. The calculated value D 0 s 1.34 10y1 1 m2 sy1 is found in the same order of magnitude as reported data for the diffusion of alkanes in different polymers w17x. The value determined likewise calorimetrically in Ref. w8x is substantially smaller Ž D 0 s 3.5 10y1 3 m2 sy1 .. This is quite clear, since the mixing within the reaction chamber was not considered. An increasing flow rate influences the signal in two different ways. On the one hand it accelerates the rise of the concentration in the reaction chamber, which leads to larger signal amplitudes. On the other hand, the heat exchange between the receptor layer and gas flow increases Ždecreasing R air in Eq. Ž6... Therefore, an optimal flow rate exists as shown in Fig. 10. The thermal resistance R air used for the simulation was determined experimentally w10x. The heat loss at a flow rate of 100 ml miny1

Fig. 12. Amplitudes of simulated thermal voltage signals at 40 mg Žb. and 400 mg Ža. receptor mass Žflow rate: 100 ml miny1 , 1000 ppm octane.. The simulation was performed with and without considering of the analyte depletion in the reaction chamber due to absorption.

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Fig. 13. Simulated dependence of the integrated thermal voltage signal on the receptor mass at non-equilibrium conditions Žmodulation period: 2.08 s..

is no more than 10% of the generated heat power. In our sensor arrangement, we work at a flow rate of only 20 ml miny1 because of the restriction by the MOSES system. The measured heat of absorption increases linearly with the receptor mass in a practically relevant range ŽFig. 11.. Since in addition, the thermal resistance of the receptor layer increases, at larger flow rates the fraction of the heat loss will rise. An increasing receptor mass also worsens the dynamic of the signal because the absorption mass flow decelerates the rise of the concentration in the reaction chamber Žsee Eq. Ž1... In Fig. 12, simulation results are shown which have been obtained with and without considering the depletion process in the model. The larger the receptor mass, the stronger the influence of the depletion on the amplitude of the signal. That is of particularly great importance if the modulation frequency does not permit a balancing of the absorption equilibrium. The simulation showed ŽFig. 13. that for a modulation period of 2.08 s, the signal maximum is reached at a receptor

Fig. 14. Simulated dependence of the signal amplitude on the volume of the reaction chamber. From the geometry, 65 ml was calculated.

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amplitude on the volume of the reaction chamber is depicted. The calorimetric cell of our sensor arrangement is designed with a chamber volume of 65 ml, i.e. no substantial improvements are possible.

4. Applications of the sensor array in an electronic nose 4.1. ImproÕement of the discrimination power As shown above, the calorimetric detection principle was proved to be very useful for the monitoring of volatile organic compounds. Thus, detection limits in the lower ppm range can be achieved with well-optimized calorimetric arrangements. To study at what extent calorimetric sensors are able to improve the discrimination power of hybrid electronic noses, measurements with the electronic nose MOSES II containing also the calorimetric module were made. The results of measurements with synthetic gas mixtures as well as with gas samples from technical processes show that the discrimination power of an electronic nose can be increased by the incorporation of calorimetric sensor arrays. The improvement of the discrimination can be seen from distances between the sample clusters in a PCA-plot. Fig. 15 shows the scores from measurements of pyrophen gases from foundries, in the first case measured with the QMB and the SnO 2 module only, in the second case involving also the calorimetric module. At this appli-

Fig. 15. PCA scores plots of the pyrophene gas sample measurements considering the QMB and SnO 2 sensors and the combination of all MOSES II sensor modules, respectively.

mass of approximately 1 mg. This could be confirmed experimentally. The volume of the reaction chamber also effects the speed of mixing. In Fig. 14, the dependence of the signal

Table 2 Mahalanobis distances from pyrophen gas samples measured with MOSES II and calculated for different sensor combinations Combination

Cal-module

QMB-module

SnO 2 -module

QMBq SnO 2-module

All modules

Sample 1–2 Sample 2–3 Sample 1–3 B

3.63 14.32 13.88 10.61

6.43 3.03 6.08 5.18

3.54 8.74 9.16 7.15

4.48 33.75 35.11 24.45

12.94 34.70 37.24 28.29

Table 3 Mahalanobis distances from aromatic gas samples measured with MOSES II and calculated for different sensor combinations Žgas concentration 300 ppm. Combination

Cal-module

QMB-module

SnO 2 -module

QMB q SnO 2 -module

All modules

Benzene–Toluene Benzene–Xylene Toluene–Xylene B

34.9 63.9 30.5 43.0

7.3 12.5 6.3 8.7

18.3 19.7 10.4 16.1

16.7 22.9 13.1 17.6

21.7 23.2 17.0 20.6

Table 4 Mahalanobis distances from alcohol gas samples measured with MOSES II and calculated for different sensor combinations Žgas concentration 300 ppm. Combination

Cal-module

QMB-module

SnO 2-module

QMBq SnO 2 -module

All modules

1-Butanol–2-Butanol 1-Butanol–2-Pentanol 2-Butanol–2-Pentanol B

20.0 18.1 22.7 20.3

11.0 7.8 10.0 9.6

33.2 16.5 38.8 29.5

33.4 26.1 45.4 35.0

35.6 30.0 46.1 37.2

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from 2.08 to 8.32 s, as shown in Fig. 16. But the best resolution is obtained if signals from more than one modulation frequency are combined. These possibilities for an improvement of the discrimination power of the calorimetric sensor array will be considered in future developments.

5. Conclusions In the paper, we have described a sensor arrangement for the detection of volatile organic vapors which is based on heat power measurements. The reported results show that well optimized calorimetric sensors are useful for measurements in the lower ppm range. The analysis of the signal transfer showed that design and operation conditions have to be properly adjusted to obtain a satisfying performance of the sensor arrangement. In order to demonstrate the improvement of the discrimination power of an electronic nose by incorporating calorimetric sensors, the developed sensor arrangement was adapted to the electronic nose MOSES II. According to the calculated Mahalanobis distances, the improvement of the discrimination power by combination with other types of sensors depends on the nature of the samples. But in some cases, best results were obtained with combinations including the calorimetric sensors. Further optimizations should be possible in view of the selection of the coating material. Fig. 16. Comparison of the PCA scores plots of three samples considering different modulation period times.

cation, the calorimetric sensors are necessary to discriminate all samples. As a more objective measure of the discrimination power of electronic noses, the Mahalanobis distance is useful w18x. To study the influence of the different sensors on the discrimination power of the hybrid electronic nose, we have calculated the Mahalanobis distance for different combinations of the sensors. In Tables 2–4 the data from measurements with the pyrophen gases as well as with pure organic vapors Žaromates, alcohols. are listed. It is obvious that no general rule exists for an optimal choice of sensors to obtain maximum values of the Mahalanobis distance. But in some cases, best results were obtained with combinations including the calorimetric sensors. 4.2. SelectiÕity tuning by signal modulation As mentioned above, the sensitivities of the calorimetric sensors are influenced by the dynamic properties of the signals. This yields an additional option for the selectivity tuning. Being dependent on the diffusion coefficients of the components, the signals are affected differently by the modulating frequency. Therefore, different PCA-plots result if the modulation period of the concentration changes

Acknowledgements The work was substantially inspired by Professor W. .. We have to thank him extraordinarily Gopel ¨ ŽTubingen ¨ for his permanent interest on our work. Furthermore, we thank the German Federal Ministry of Economy ŽBMWi. for financial support ŽAIF 10852 B..

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