1370 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 50, NO. 5, OCTOBER 2001

Identification of Forane R134a in an Air-ConditionedAtmosphere With a TGS Sensor Array

Claude Delpha, Maryam Siadat, and Martine Lumbreras

Abstract—For electronic nose applications based on tin oxidegas sensors, relative humidity is a very influential parameter thatmay cause false gas detection. In our application, we want to dis-tinguish a refrigerant gas (Forane R134a) in an air-conditioned at-mosphere using a TGS gas sensor array. First, this paper gives abrief overview for accurate test equipment for characterizing thesetypes of sensors under closely controlled humidity and temper-ature conditions. Next, the humidity influence is experimentallyshown, either for air or for Forane R134a gas concentrations in air.Then, by using two complementary pattern recognition methods,principal component analysis (PCA) followed by discriminant fac-torial analysis (DFA), we show the ability to discriminate accu-rately the target gas whatever the humidity rate. We also provethat it is possible to identify unknown test cases with the resultantdecisive law.

Index Terms—Data analysis, discrimination and identification,electronic nose, pattern recognition, tin oxide gas sensor array.

I. INTRODUCTION

T ODAY, there is an increasing interest in electronic nose ap-plications [1], [2]. In fact, the concept of electronic nose

is based on a gas sensor array with different selectivity patterns,a signal processing and a data acquisition unit in associationwith a pattern recognition system. The final aim of our work isthe conception of an environmental electronic nose, based on anarray of TGS-type metal oxide sensors, for the main detection ofa refrigerant gas Forane R134a, carbon dioxide (CO) and dif-ferent volatile organic compounds (VOCs) in an air-conditionedatmosphere. These sensors are very sensitive, but their selec-tivity and response are greatly affected by many pollutants likethe gas relative humidity ratio and temperature [3], [4]. Severalinvestigations have been done to solve the humidity problem byincorporating additives into the oxide [5] or by modulating thesensor operating temperature [6]. Unfortunately, this problemwas not completely solved by these methods. Another type ofinvestigation consists of taking the humidity ratio into accountin the pattern recognition analysis method. For our application,this last method was used: the humid air is then considered as apollutant gas. Therefore, the gas sensor array has been charac-terized under several humidity-controlled atmospheres, and theresults have been included in the sensor database for the patternrecognition.

This paper concerns the Forane R134a identification in ahumid air atmosphere whatever the relative humidity rate. This

Manuscript received July 23, 1999; revised June 1, 2001.The authors are with the Laboratoire Interfaces Composants et Microélec-

tronique, LICM/CLOES/SUPELEC, Université de Metz, 57070 Metz, France(e-mail: delpha@ese-metz.fr; lumbre@ese-metz.fr).

Publisher Item Identifier S 0018-9456(01)08736-8.

new generation refrigerant gas, Forane 134a, is studied becauseit can cause greenhouse effects and also health problems in caseof leakage in a closed space. In this paper, we first describe thesensor array characterization results, in terms of steady-stateconductance, for a humid air atmosphere, then for a mixtureof Forane R134a in dry or humid air. Then, by using theprincipal component analysis (PCA) method, we show how theexperimental results can be correlated, and then separated ingroups. We then insert these groups into our database, and trainit with the discriminant factorial analysis (DFA) method. Weprove that it is possible to discriminate accurately the gas typeand afterwards identify unknown test data even if the relativehumidity rate varies.

II. EXPERIMENTAL

For the experiments, we have designed a system able to createclosely controlled conditions of humidity and temperature [7].It is a dynamic flow system (100 ml/min): the carrier gas (syn-thetic air) is brought into a humidity generator based on the bub-bler principle and then mixed with the main target gas (ForaneR134a) via gas lines controlled by mass flow controllers (Model5850TR, Brooks Instruments). Then the mixture is introducedin the test chamber that is placed in a gas-controlled tempera-ture atmosphere (33C). This chamber was specially designedto be circular in stainless steel materials and also optimized toprovide the shorter gas exchange and laminar gas flow to theenclosed gas sensors.

For our application, we are using three pairs of TGS-type gassensors provided by Figaro Engineering Inc. (TGS 832, a refrig-erant gas sensor; TGS 800, a general air contaminant sensor; andTGS 813, a hydrocarbon sensor) inside the test chamber. Thesetypes of sensors were chosen because of their high sensitivity toorganic, pollutant, and combustible gases. In our application, allthe sensor output responses are collected via a data acquisitionboard (LabPC , National Instruments), treated, and then ana-lyzed by pattern recognition methods using statistical and dataanalysis software (SPSS 8.0, SPSS Inc.).

III. RESULTS AND DISCUSSION

A. Characterization

To create our database, it is necessary to take into accountthe simultaneous effect of the humidity and the studied gas forobtaining a more accurate identification of the gas in the realatmosphere. Therefore, we have characterized the sensors inhumid air or in a mixture of Forane R134a in dry or wet air.

0018–9456/01$10.00 © 2001 IEEE

DELPHA et al.: FORANE R134a IN AN AIR-CONDITIONED ATMOSPHERE 1371

Fig. 1. Typical sensor time-dependent behavior for reducing gases.

Fig. 2. Sensors steady-state response in humid air.

We have studied the behavior of the sensor array for five dif-ferent relative humidity rates (18%, 35%, 52%, 68%, and 85%)at 33 C. For the measurement in humid air, a dry synthetic airflow is set for 1 h before submitting each humidity rate to thesensors for 1 h. For each sensor, we have noted a similar be-havior to a reducing gas exposure (Fig. 1), stabilized in the bestcases after 20 min [8]. Therefore, the time-dependent responseas well as the steady-state conductance increases as a function ofthe relative humidity rate for the three types of sensors (Fig. 2).

As for humid air, the sensors have been characterized in amixture of Forane R134a in dry air. In this case, we have studiedthe sensor array behavior in the Forane R134a concentrationrange 200 ppm–1000 ppm with steps of 200 ppm. Before eachForane R134a-dry air flow, a 100 ml/min synthetic dry air flowis set to stabilize the sensor sensitive layer [9] for 1 h. The sensorarray response to Forane R134a, which is a reducing gas, in-creases along with the concentration (see Fig. 3) in the sameway as the previous results in humid air.

Then, we have studied the sensor behavior under a mixture ofForane R134a in wet air. Therefore, for a Forane R134a/humidair atmosphere, we present the sensor array conductance time-dependent response (see Fig. 4). We can note, for all the sensors,that the reducing effects due to humid air and Forane R134a arecumulative. For the studied relative humidity rates, the sensorsoffer similar responses. In fact, the steady-state conductancevalues increase along with the gas concentrations and the rel-ative humidity rates (see Fig. 5), but we note that the ForaneR134a response is masked for the lowest concentrations (200ppm–400 ppm) at the highest relative humidity value (85%).

Fig. 3. Sensors steady-state response in dry Forane R134a.

Fig. 4. TGS 800 dynamic behavior in humid Forane R134a.

Fig. 5. TGS 800 steady-state response in humid Forane R134a.

All these characterization results (160 measurements) have beengrouped in a database and exploited by using first PCA and thenDFA to prove that this base is efficient to identify the ForaneR134a even if the relative humidity rate varies.

B. Principal Component Analysis (PCA)

This pattern recognition technique is a powerful unsuper-vised method [10] usually employed for the tin oxide gas sensorarray. To apply this method, steady-state conductance valuesobtained from the characterization measurements are groupedin a database. Then, linear combinations of the orthogonal re-sponse vectors are expressed, and principal components are cal-

1372 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 50, NO. 5, OCTOBER 2001

TABLE IEIGENVALUES OF THERESPONSEDATA BASE

Fig. 6. PCA results for the TGS sensor array steady-state conductance.

culated. Then, eigenvalues representing the data variance per-centage contained in each principal component are given. Theaim of this technique consists of removing any redundancy andreducing the dimensionality of the database. In Table I, we havesummarized the eigenvalues and the data variance percentagesfor all the principal components. Because the two first compo-nents allow us to take into account more than 95% of the database variance, we limit our representation to these two compo-nents. Therefore, for these two axes, we present in Fig. 6 thePCA results for all the studied TGS sensors steady-state con-ductance. We note that the results can be separated into threeparts corresponding to each measurement gas type: humid air,dry Forane R134a, and wet Forane R134a. However, the bound-aries between the groups are not well defined, and we noteminor overlappings. Therefore, with these three groups definedby PCA, we now study the measurement database with the DFAmethod to see if the steady-state conductance values are suffi-cient variables to accurately identify the gas type. If not, we haveto define a new variable to improve the target gas type identifi-cation.

C. Discriminant Factorial Analysis (DFA)

As for PCA, this technique is a factorial method [10] widelyused for electronic nose applications [11]. In fact, for thismethod ana priori group is given for all the measurements inthe database. Then, the discriminant procedure, which consistsof maximizing the differences between all the groups andminimizing these differences inside each group, is done. A

Fig. 7. DFA results for the TGS sensor array steady-state conductance.

TABLE IICLASSIFICATION RESULTS FOR THEDFA WITH THE STEADY-STATE

CONDUCTANCE DATA BASE

linear combination of the variables which best characterizesthe differences among the groups is produced, and the obtaineddiscriminant function can be used to classify new cases. Toapply this method to our database, the three main groups de-fined by PCA are applied, then two discriminant functions areobtained. In Fig. 7, we present the DFA results for our sensorarray with two main axes representing 100% of the database.With this representation, we show that the steady-state conduc-tance values can be considered sufficient to generate a quitegood discriminant function for the gas type discrimination.In fact, we have some reclassifications: only 98.1% of themeasurements in the database have been classified in theirapriori group. As shown in Table II, a few errors are producedby classifying several dry or wet Forane R134a measurementsin the humid air group, but these measurements correspond tothe lowest gas concentrations. These results have then beenvalidated by the cross-validation method, and we have foundthat only 97.5% of the cross-validated measurements have beenclassified correctly.

In order to test the created decisive law, we have used a testdata set composed of 15 unknown cases (see Table III) corre-sponding to different relative humidity ratios and Forane R134agas concentrations. In fact, we are able to accurately identifyall the unknown cases except the humid air measurements (fivecases) that were mainly identified as wet Forane R134a (seeFig. 7). Therefore, the decisive law found with this database has

DELPHA et al.: FORANE R134a IN AN AIR-CONDITIONED ATMOSPHERE 1373

TABLE IIITEST DATA SET CORRESPONDINGMEASUREMENTS ANDIDENTIFICATION RESULTS

Fig. 8. Sensor conductance dynamic slope responses in humid air.

to be improved by associating a new variable to the steady-stateconductance.

A new variable known as the conductance dynamic slopeof the time-dependent response measured in the first five min-utes of gas exposure is chosen (see Fig. 1). For the differenttypes of measurements, we found that this variable increasessignificantly with the relative humidity ratio (see Fig. 8), or thegas concentration (see Fig. 9). Therefore, for each sensor ofthe array, these values were associated to the previous steady-state conductance database. This new database is then trainedby using the same groups (Humid Air, Dry R134a, and WetR134a) and a new discriminant function is generated. As shownin Fig. 10, the three groups are more separated compared toFig. 7. In fact, the classifying results obtained for this anal-ysis give us only one reclassified data which corresponds to alow concentration of dry Forane R134a classified in the humidair group. Therefore, 99.4% of the complete new database hasbeen classified in thea priori group. The generated discriminantfunction has been checked by the cross-validation method andthe same percentage was found, showing that this function iscorrect and then can be used to classify unknown cases. Thus,we have tried to identify the same test data set (see Table III)

Fig. 9. Sensor conductance dynamic slope response in dry Forane R134a.

Fig. 10. DFA results by using two kinds of variables for each sensor: thedynamic slope and the steady-state conductance value.

with this new decisive law. We found that only one unknowncase was misidentified (see Fig. 10): only a humid air measure-ment (85% RH) was classified as dry Forane R134a. Therefore,

1374 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 50, NO. 5, OCTOBER 2001

the created law can be used to accurately identify unknown caseswhatever the gas relative humidity rate.

IV. CONCLUSION

We have characterized a TGS sensor array in a humid (0–85%RH) atmosphere composed of a mixture of Forane R134a in syn-thetic air (0–1000 ppm). The sensor steady-state conductancevalues are arranged in a database and then treated by PCA andDFA methods. For these two methods, we have shown that thesteady-state value is a variable that allows us to provide a quitegood separation of the gas types. However, this variable is notreally sufficient to create an accurate discriminant function to beused to perfectly identify unknown cases. As a matter of fact,another variable has been added for each sensor of the array:the dynamic slope. This variable allows us to improve the gasdiscrimination and to create a decisive law able to accuratelyclassify new unknown cases. Therefore, by using mathematicaltreatment and statistical analysis, we are able to compensate forthe sensor variations and inaccuracies due to the presence of hu-midity in an air-conditioned atmosphere.

REFERENCES

[1] W. Göpel, “Chemical imaging: Concepts and visions for electronic andbio-electronic noses,”Sens. Actuators B, vol. 52, pp. 125–142, 1998.

[2] J. W. Gardner and P. N. Bartlett, “Performance definition and standard-ization of electronic noses,”Sens. Actuators B, vol. 33, pp. 60–67, 1996.

[3] C. Delpha, M. Siadat, and M. Lumbreras, “Environmental temperatureand humidity variation effects on the response of a TGS sensor array,”in Proc. ISOEN, Tübingen, Germany, Sept. 20–22, 1999, pp. 156–159.

[4] K. Ihokura and J. Watson,The Stannic Oxide Gas Sensor: Principlesand Applications. Boca Raton, FL: CRC, 1994.

[5] D. S. Vlachos, P. D. Skafidas, and J. N. Avaritsiotis, “The effect of hu-midity on tin oxide thick film gas sensors in the presence of reducing andcombustible gases,”Sens. Actuators B, vol. 24–25, pp. 491–494, 1995.

[6] P. Van Geloven, M. Honore, S. Leppavuori, and T. Rantala, “The in-fluence of relative humidity on the response of tin oxide gas sensors tocarbon monoxide,”Sens. Actuators B, vol. 4, pp. 185–188, 1991.

[7] C. Delpha, M. Siadat, and M. Lumbreras, “Humidity effect on a com-mercially available refrigerant gas sensor TGS 832,”Proc. IEEE, vol.3539, pp. 172–179, 1998.

[8] , “Humidity dependence of a TGS gas sensor array in an air-con-ditioned atmosphere,”Sens. Actuators B, vol. 59, no. 2–3, pp. 255–259,1999.

[9] C. Delpha, F. Sarry, M. Siadat, and M. Lumbreras, “Evaluation of asensor array in an environmental controlled gas atmosphere,” inProc.Sensor, vol. 2, Nürnberg, Germany, May 18–20, 1999, pp. 513–518.

[10] D. F. Morisson,Multivariate Statistical Methods, 2nd ed. Singapore:McGraw-Hill, 1988, p. 415.

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Claude Delphareceived the M.S. degree in biomedical electronics engineeringand the Instrumentation and Microelectronics postgraduate degree in the fieldof gas sensing and signal processing techniques from the University of Nancy,Nancy, France, in 1995 and 1996, respectively, and the Ph.D. degree in the fieldof gas sensing and signal processing techniques from the Laboratory of Inter-faces, Components, and Microelectronics, the University of Metz, Metz, France,in 2000.

He is currently an Associate Professor at the University of Paris XI, Paris,France. His main areas of interest are in electronics and instrumentation, semi-conductor chemical sensors, chemical vapor discrimination using sensor arrays,gas sensors humidity and temperature dependence, electronic nose, and patternrecognition methods.

Maryam Siadat received the Dipl.Eng. degree in electronics in 1983 and thePh.D. degree in biomedical electronics engineering in 1989, both from the Poly-technic Institute of Lorraine (ENSEM/INPL), Nancy, France.

She is currently an Associate Professor at the University of Metz, Metz,France. Her research interests are in gas detection, signal and data processing,sensor characterization, and numerical electronics circuits development.

Martine Lumbreras received the M.S. degree in electrical engineering withspecialization in solids electronics in 1969, and the Ph.D. degree in 1979, bothfrom the University of Montpellier, Montpellier, France, and the Dr. Sci. degreein 1987 from the University of Metz, Metz, France.

Since 1991, she has been a Professor at the University of Metz. In 1994, shecreated a sensor research group, which is part of the university’s Laboratory ofInterfaces, Components, and Microelectronics.