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High performance solvent vapor identification with a two sensor array using temperature cycling and pattern classification
Sensors and Actuators B 95 (2003) 58–65
High performance solvent vapor identification with a two sensor array using temperature cycling and pattern classification Andreas Gramm∗ , Andreas Schütze Laboratory for Measurement Technology, Department of Electrical and Electronic Engineering, Saarland University, Building 13, 66123 Saarbrücken, Germany
Abstract Temperature modulation of semiconductor gas sensors is a powerful strategy to improve selectivity and stability of gas sensor arrays in applications where different gases have to be identified [IEEE Sens. J. 1 (3) (2001) 207; Sens. Actuators B 40 (1997) 33; Sens. Actuators B 43 (1997) 45; Anal. Chim. Acta 361 (1998) 93; Anal. Chem. 68 (1996) 2067; Sens. Actuators B 33 (1996) 142]. A recent review can be found in [Sens. Actuators B 60 (1999) 35]. We present an array composed of two commercial metal oxide gas sensors allowing discrimination of six organic solvents over a wide concentration range from 2 to 200 ppm in air. Temperature cycling reduces sensor baseline drift considerably over a test period of several months. Additional signal pre-processing suppresses the influence of humidity and leads to further drift reduction. The system comprises a hierarchical pattern classification evaluating shape features generated from the sensor response curve during temperature cycling, which are compared with other feature generation methods (like FFT and wavelet, see also [Sens. Actuators B 41 (1997) 105]). The classification requires comparatively little computing power and allows flexible adaptation to different operating environments, for example, to suppress false alarms from interfering gases. Reproducibility of sensor performance was checked using four systems with identical sensors. The same features can be used for the classification with all systems but the areas for classification have to be adapted to the individual sensors. © 2003 Published by Elsevier Science B.V. Keywords: Dynamic gas sensor array; Temperature cycling; Hierarchical pattern classification
1. Introduction/experimental For applications like leak detection in chemical warehouses, sensor systems with high sensitivity and selectivity are required to detect leaks (i.e. ruptured containers) at an early stage and identify the leaking substance to allow the correct safety measures for cleaning up the spill. In addition, for stand-alone operation over several months good stability of the system is required. We selected six organic solvents (benzene, iso-pentane, methyl alcohol, diethyl ether, methyl tert-butyl ether, propylene oxide) as model substances to evaluate systems for this type of application. A first approach using an array of seven commercial sensors operating at constant temperature did not allow discrimination of the substances over a large concentration range. In addition, sensor drift would have necessitated complex recalibration within short intervals. We therefore chose a system based on only two sensors (UST 1330 and 2330 [9]) but with a dynamic operating ∗
Corresponding author. Tel.: +49-681-302-5018; fax: +49-681-302-4665. E-mail address: [email protected] (A. Gramm). 0925-4005/$ – see front matter © 2003 Published by Elsevier Science B.V. doi:10.1016/S0925-4005(03)00404-0
mode using temperature cycling to obtain more information from the sensors and reduce sensor drift. A similar system is currently used for detecting smoldering fires in coal power plants [10]. The sensors were operated using an analog temperature control circuit (heater resistance was used as temperature signal); the set points are adjusted by a digital control system which also measures the sensor resistance every second. Different temperature cycles with two and three temperature set points covering a wide temperature range between 150 and 450 ◦ C were evaluated to select the best operating mode for our model application. Fig. 1 shows the selected temperature cycle and resulting response curves for the UST 1330 sensor, the cycle for the UST 2330 is equivalent. This operation mode yields 40 data points per 20 s cycle which are then evaluated by pattern classification.
2. Pattern classification Our pattern classification approach reflects the usual steps necessary for machine olfaction (for an excellent review see [11]). First, the obtained sensor data are normalized by division through their mean value over the whole cycle. This
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Fig. 1. Temperature cycle and response curves of the UST 1330 gas sensor. (a) Temperature set points and actual sensor temperature during cycling. Response curves in synthetic air (b) and synthetic air with admixture of 20 ppm organic solvent vapor as indicated (c–h) at 30 and 70% relative humidity.
Fig. 2. Long term stability of the response curve for clean air (a) and benzene (b), normalization of the curves by division through their mean improves reproducibility (c). Sensors were operated in clean air between tests.
significantly reduces the effects of sensor (baseline) drift improving the classification of the response curves and resulting in better system stability (Fig. 2). In addition, this pre-processing eliminates the influence of humidity almost completely, as shown for air and three solvents in Fig. 3. In the next step, secondary features, which are descriptive of the shape of the curves, are generated, e.g. the slope after a temperature change, mean value at constant temperature set point, etc. The most suitable features for classification were selected using a feature extraction algorithm which calculates the overlap between different classes and selects the
features showing the least overlap in a bottom-up or sequential forward selection algorithm. The same features can be used over the whole concentration range investigated even though the response curve changes considerably with gas concentration. For the discrimination a hierarchical approach is used, i.e. not all classes are separated in one step. Instead, subgroups are defined which are further discriminated by using different features. This approach has two main advantages. First, the computing capacity necessary for this classification approach is very low so that low cost systems can be realized;
Fig. 3. Normalized sensor response curves showing almost no influence from changes in relative humidity.
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Fig. 4. First step of the hierarchical classification discriminating between clean air, benzene, iso-pentane and others. The figure shows 80 measurements for each gas in the concentration range of 2–200 ppm at 30 and 70% relative humidity. Values for 2, 5, 10, 20, 50, 100, 200 ppm benzene and iso-pentane are shown as full symbols to indicate the effect of relative humidity and increasing concentration (arrows). Features were normalized to the interval [0, 1]. x–y means values x to y in the temperature cycle.
classification using linear discriminant analysis (LDA) [12] yields slightly better results (compare Fig. 8), but would need more computing power for the algorithm as more data processing is needed. Second and more important, the quality of each decision is easily checked using a two-dimensional plot of the data as shown for the first step in the classification process (Fig. 4); with these plots possible problems for classification, for example, influence of relative humidity, can be easily identified. In Fig. 4 the area for iso-pentane and benzene might overlap if iso-pentane is measured at very low relative humidity values. If this situation might occur in the envisaged application, the classification can be improved by using additional features (see Fig. 6). The final validation, necessary for all artificial nose or machine olfaction applications, is therefore more transparent for the user. To study the reproducibility of the commercial sensors used in these tests, four test systems with identical sensors
were set up. We found that the features and the classification steps can be transferred from one calibration system (system 1 in Fig. 4) to the other sensor systems, but the areas corresponding to the different classes have to be individually adapted for each system or sensor combination. Fig. 5 shows the same plot for systems 2–4 as in Fig. 4 (first decision of the hierarchical classification). The plots show that discrimination of the four classes (air, benzene, iso-pentane, others) is possible for all four systems. However, in system 4 the separation of the classes benzene and iso-pentane is marginal, so that changes in humidity or even slight sensor drift might lead to false classifications. In this case an additional classification step could be added, which allows good separation between both classes as shown in Fig. 6. This step would also improve the classification for the other sensor systems. This example shows that the presented classification method has excellent reserves for achieving sufficient
Fig. 5. First classification step as shown in Fig. 4 for three sensor systems with the same sensors as in the reference system 1. The areas for the different classes have to be individually adapted to the sensors.
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Fig. 6. Alternative classification step for separation of benzene and iso-pentane for system 4. Solid symbols represent concentration of 2, 5, 10, 20, 50, 100, 200 ppm with arrows indicating increasing concentration.
selectivity for most applications. Indeed, we could show that the classification presented here is even possible with a single sensor (UST 1330), dynamic operation and online-switching between different temperature cycles for critical decisions [13], see also outlook below. In the case of the single sensor system, however, the classification has to be adapted more thoroughly to the individual sensor.
Fig. 7. Decision tree for the identification of six organic solvents with hierarchical classification.
Fig. 8. LDA plot showing good separability between five solvents using secondary shape features. Synthetic air and iso-pentane were separated in a prior step.
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To achieve a 100% classification different features have to be used for each sensor requiring a complete calibration of each sensor. For the dual sensor systems on the other hand, the classification scheme can be adapted more easily to individual sensor combinations by measuring only a few concentrations for each gas and adapting the classification areas accordingly. Alternatively, sensors could be pre-selected for
a given application based on a few gas measurements and comparison with the standard sensor. If the encountered differences are too large, the sensor would not be accepted for this application but could be used in other applications where less selectivity is acceptable. The identification of the remaining four solvents can be achieved by further classification steps not shown in detail here, for further results see [13]. Overall, the complete
Fig. 9. LDA plots for five solvents using raw data and secondary features generated using Fourier (DFT) and wavelet transforms. Discriminant functions are specific for each feature set. Symbols are the same as in Fig. 8.
Fig. 10. LDA plot for shape features generated from system 2 using the discriminant functions of system 1. Symbols are the same as in Fig. 8.
A. Gramm, A. Schütze / Sensors and Actuators B 95 (2003) 58–65 Table 1 Features used for the classification as shown in Fig. 7a Decision
Features
1 2 3 4 5
Av. 6–8/Av. 1–20; Sl. 11–13/Av. 1–20 (Sl. 6–8 − Sl. 11–13)/Av. 1–20; Av. 6–8/Av. 15–17 Av. 11–13/Av. 1–20; Av. 15–17/Av. 18–20 Sl. 4–5/Av. 1–20; Sl. 15–17/Sl. 18–20 Av. 11–13/Av. 18–20 (Sl. 6–8 – Sl. 11–13)/Av. 1–20; Av. 15–17/Av. 18–20 a
Features extracted from UST 1330 data are shown in bold type, and those from UST 2330 in italics. Av.: average; Sl.: slope.
classification is summarized in the decision tree shown in Fig. 7. Decision 1 is shown as an example in Fig. 4, in the next step either benzene and iso-pentane are discriminated (Fig. 6) or diethyl ether is identified followed by methyl tert-butyl ether and finally classification between propylene oxide and methyl alcohol. Table 1 summarizes the features which are used for the classification steps. In the final classification, three features are used to achieve a 100% classification for all four sensor systems as all two-dimensional plots show overlaps between classes which are resolved by taking into account a third dimension.
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functions of system 1 (i.e. identical functions as in Fig. 8). A similarity between both plots is evident by the relative position of the groups, but all groups are shifted and/or spread over a much larger area preventing a successful classification. The higher class separation performance of LDA obviously leads to reduced generalization performance—a problem that is often encountered with classification methods with many free parameters usually denoted as overfitting [11]. Compare this result with the two-dimensional plots of our shape features as shown in Figs. 4 and 5; with these features an adaptation of the areas used for the identification of the different gases seems possible with only a few reference measurements for each system (i.e. one low, medium and high concentration for each gas). As the effect of increasing gas concentration and humidity is easily seen and reproducible, the classification areas can be individually set for each system.
2.1. Comparison with other feature generation methods We compared our classification approach using secondary shape features with the information provided by the raw data from the temperature cycles and with features generated by Fourier and wavelet transforms. LDA, which directly maximizes class separability [11,12], was used to determine the relative information content of the features. Even with this technique, which makes use of all available data simultaneously, it is not possible to classify all solvents in one step; to achieve 100% classification with good separation (i.e. distance between classes greater than typical scatter) hierarchical steps also had to be used. In this comparison our shape feature generation approach has proven more powerful than FFT or wavelet; after separation of air and iso-pentane in a first step, LDA allows direct classification of all five remaining solvents with the reference sensor system 1 as shown in Fig. 8. By comparison, LDA plots of the same five classes using the raw data as well as FFT and wavelet features generated from the data of system 1 show significant overlap between the classes (Fig. 9) necessitating several more hierarchical classification steps. It is interesting to note that the LDA plots obtained with all four data sets are very similar (i.e. relative position of the classes). Therefore the resulting decision trees for hierarchical classification would be nearly identical for all data sets (in this case, methyl alcohol would be separated next). Unfortunately, the discriminant functions obtained from the LDA of system 1 cannot be transferred to the other systems. Fig. 10 shows the shape feature data generated from the calibration of system 2 and plotted using the discriminant
Fig. 11. Temperature cycle 2 (a) allowing improved discrimination between ethers and dynamic development of response curves after switching between different temperature cycles (b–c).
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3. Conclusion/outlook
References
In this paper we have shown that stable low cost sensor systems with very high selectivity can be achieved using commercial semiconductor gas sensors with dynamic operation (i.e. temperature cycling) and pattern classification techniques. Furthermore, the presented classification approach leads to transparent and easily validated decisions. The same classification can be adapted to different sensor systems with only a few calibration measurements. A pre-selection of the sensors might be necessary, if fabrication parameters are varying over a too large range. On the other hand our approach could also be used to improve the quality control during the manufacturing process. Further work is necessary to allow easier identification of suitable temperature cycles for a given application as this process is currently very time consuming. A possible approach currently under study in our group tries to extract relevant parameters from a series of standardized measurements which are then used to predict the response curves for arbitrary temperature cycles, see also [14,15]. Further improvement of the overall system performance is possible by implementing different temperature cycles (T-cycles) within one system. The standard operation would be based on T-cycles individually adapted for each sensor to provide maximum long term stability (and/or low power consumption depending on application requirements). If a gas is detected, which is easily possible even with very simple cycles and signal processing, the system can then switch to different T-cycles providing sufficient information for gas identification. This online-switching can be repeated if a branch is reached in the hierarchical classification where further classification is not possible. Fig. 11 shows the development of the response curves after switching from our standard T-cycle 1 (Fig. 1) to a two temperature cycle spanning a larger range. This cycle allows discrimination of the three chemically very similar ethers even with single sensor systems [13]. It has to be noted, that the overall performance of this cycle for all gases is inferior to T-cycle 1. After changing to a different cycle, the response curves quickly reach their final form (Fig. 11b and c) allowing classification within 1 min after switching. Therefore this enhancement can be used in many applications which are not time critical providing an opportunity to optimize the system for different operating conditions.
[1] E. Llobet, R. Ionescu, S. Al-Khalifa, J. Brezmes, X. Vilanova, X. Correig, N. Bˆarsan, J.W. Gardner, Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition, IEEE Sens. J. 1 (3) (2001) 207–213. [2] Y. Kato, K. Yoshikawa, M. Kitora, Temperature-dependent dynamic response enables the qualification and quantification of gases by a single sensor, Sens. Actuators B 40 (1997) 33–37. [3] A. Heilig, N. Bˆarsan, U. Weimar, M. Schweizer-Berberich, J.W. Gardner, W. Göpel, Gas identification by modulating temperatures of SnO2 -based thick film sensors, Sens. Actuators B 43 (1997) 45– 51. [4] S. Nakata, E. Ozaki, N. Ojima, Gas sensing based on the dynamic nonlinear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change, Anal. Chim. Acta 361 (1998) 93–100. [5] S. Nakata, S. Akakabe, M. Nakasuji, K. Yoshikawa, Gas sensing based on a nonlinear response: discrimination between hydrocarbons and quantification of individual components in a gas mixture, Anal. Chem. 68 (1996) 2067–2072. [6] R.E. Cavicchi, J.S. Suehle, K.G. Kreider, M. Gaitan, P. Chaparala, Optimized temperature–pulse sequences for the enhancement of chemically specific response patterns from micro-hotplate gas sensors, Sens. Actuators B 33 (1996) 142–146. [7] A.P. Lee, B.J. Reedy, Temperature modulation in semiconductor gas sensing, Sens. Actuators B 60 (1999) 35–42. [8] L. Ratton, T. Kunt, T. McAvoy, T. Fuja, R. Cavicchi, S. Semancik, A comparative study of signal processing techniques for clustering microsensor data (a first step towards an artificial nose), Sens. Actuators B 41 (1997) 105–120. [9] UST Umweltsensortechnik GmbH, Geschwenda, Germany. http://www.umweltsensortechnik.de. [10] H. Petig, J. Kelleter, D. Schmitt, Gas sensor fire detectors prove effective in coaling plants, Global Risk Rep. 4 (1999) 19–22. [11] R. Gutierrez-Osuna, Pattern analysis for machine olfaction, IEEE Sens. 2 (3) (2002) 189–202. [12] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed., Wiley, New York, 2000. [13] A. Schütze, A. Gramm, T. Rühl, Identification of organic solvents by a virtual multisensor system with hierarchical classification, Presented at IEEE Sensors 2002, Orlando, USA, June 12–14, 2002. [14] S. Nakata, K. Takemura, K. Neya, Non-linear dynamic responses of a semiconductor gas sensor: evaluation of kinetic parameters and competition effect on the sensor response, Sens. Actuators B 76 (2001) 436–441. [15] J. Ding, T.J. McAvoy, R.E. Cavicchi, S. Semancik, Surface state trapping models for SnO2 -based microhotplate sensors, Sens. Actuators B 77 (2001) 597–613.
For further reading see [1–8].
Acknowledgements Financial support for this work by Merck KGaA, Germany, is gratefully acknowledged.
Biographies Andreas Gramm studied Physics at the University Regensburg and finished his diploma thesis in 1999. Since autumn 1999 he is working on his PhD thesis in the group of Prof. Dr. A. Schütze, first at the University of Applied Sciences in Krefeld, since April 2000 at the Laboratory for Measurement Technology in the Department of Electrical and Electronic Engineering of Saarland University, Germany. His research focuses on temperature modulation of semiconductor gas sensors, appropriate signal processing and the implementation of these methods in innovative gas sensor applications. Andreas Schütze received his diploma in Physics from RWTH Aachen in 1990 and his doctorate in Applied Physics from Justus-Liebig-University
A. Gramm, A. Schütze / Sensors and Actuators B 95 (2003) 58–65 in Giessen in 1994 with a thesis on microsensors and sensor systems for the detection of reducing and oxidizing gases. From 1994 until 1998 he worked for VDI/VDE-IT, Teltow, Germany, mainly in the fields of microsystems technology, technology oriented start-up support and international relations. From 1998 until 2000 he was Professor for Sensors and Microsystem Technology at the University of Applied Sciences in
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Krefeld, Germany. Since April 2000 he is Professor for Measurement Technology in the Department of Electrical and Electronic Engineering of Saarland University, Saarbruecken, Germany and Head of the Laboratory for Measurement Technology (LMT). His research interests include microsensors and microsystems, especially gas sensors and sensor systems for security applications.
High performance solvent vapor identification with a two sensor array using temperature cycling and pattern classification Andreas Gramm∗ , Andreas Schütze Laboratory for Measurement Technology, Department of Electrical and Electronic Engineering, Saarland University, Building 13, 66123 Saarbrücken, Germany
Abstract Temperature modulation of semiconductor gas sensors is a powerful strategy to improve selectivity and stability of gas sensor arrays in applications where different gases have to be identified [IEEE Sens. J. 1 (3) (2001) 207; Sens. Actuators B 40 (1997) 33; Sens. Actuators B 43 (1997) 45; Anal. Chim. Acta 361 (1998) 93; Anal. Chem. 68 (1996) 2067; Sens. Actuators B 33 (1996) 142]. A recent review can be found in [Sens. Actuators B 60 (1999) 35]. We present an array composed of two commercial metal oxide gas sensors allowing discrimination of six organic solvents over a wide concentration range from 2 to 200 ppm in air. Temperature cycling reduces sensor baseline drift considerably over a test period of several months. Additional signal pre-processing suppresses the influence of humidity and leads to further drift reduction. The system comprises a hierarchical pattern classification evaluating shape features generated from the sensor response curve during temperature cycling, which are compared with other feature generation methods (like FFT and wavelet, see also [Sens. Actuators B 41 (1997) 105]). The classification requires comparatively little computing power and allows flexible adaptation to different operating environments, for example, to suppress false alarms from interfering gases. Reproducibility of sensor performance was checked using four systems with identical sensors. The same features can be used for the classification with all systems but the areas for classification have to be adapted to the individual sensors. © 2003 Published by Elsevier Science B.V. Keywords: Dynamic gas sensor array; Temperature cycling; Hierarchical pattern classification
1. Introduction/experimental For applications like leak detection in chemical warehouses, sensor systems with high sensitivity and selectivity are required to detect leaks (i.e. ruptured containers) at an early stage and identify the leaking substance to allow the correct safety measures for cleaning up the spill. In addition, for stand-alone operation over several months good stability of the system is required. We selected six organic solvents (benzene, iso-pentane, methyl alcohol, diethyl ether, methyl tert-butyl ether, propylene oxide) as model substances to evaluate systems for this type of application. A first approach using an array of seven commercial sensors operating at constant temperature did not allow discrimination of the substances over a large concentration range. In addition, sensor drift would have necessitated complex recalibration within short intervals. We therefore chose a system based on only two sensors (UST 1330 and 2330 [9]) but with a dynamic operating ∗
Corresponding author. Tel.: +49-681-302-5018; fax: +49-681-302-4665. E-mail address: [email protected] (A. Gramm). 0925-4005/$ – see front matter © 2003 Published by Elsevier Science B.V. doi:10.1016/S0925-4005(03)00404-0
mode using temperature cycling to obtain more information from the sensors and reduce sensor drift. A similar system is currently used for detecting smoldering fires in coal power plants [10]. The sensors were operated using an analog temperature control circuit (heater resistance was used as temperature signal); the set points are adjusted by a digital control system which also measures the sensor resistance every second. Different temperature cycles with two and three temperature set points covering a wide temperature range between 150 and 450 ◦ C were evaluated to select the best operating mode for our model application. Fig. 1 shows the selected temperature cycle and resulting response curves for the UST 1330 sensor, the cycle for the UST 2330 is equivalent. This operation mode yields 40 data points per 20 s cycle which are then evaluated by pattern classification.
2. Pattern classification Our pattern classification approach reflects the usual steps necessary for machine olfaction (for an excellent review see [11]). First, the obtained sensor data are normalized by division through their mean value over the whole cycle. This
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Fig. 1. Temperature cycle and response curves of the UST 1330 gas sensor. (a) Temperature set points and actual sensor temperature during cycling. Response curves in synthetic air (b) and synthetic air with admixture of 20 ppm organic solvent vapor as indicated (c–h) at 30 and 70% relative humidity.
Fig. 2. Long term stability of the response curve for clean air (a) and benzene (b), normalization of the curves by division through their mean improves reproducibility (c). Sensors were operated in clean air between tests.
significantly reduces the effects of sensor (baseline) drift improving the classification of the response curves and resulting in better system stability (Fig. 2). In addition, this pre-processing eliminates the influence of humidity almost completely, as shown for air and three solvents in Fig. 3. In the next step, secondary features, which are descriptive of the shape of the curves, are generated, e.g. the slope after a temperature change, mean value at constant temperature set point, etc. The most suitable features for classification were selected using a feature extraction algorithm which calculates the overlap between different classes and selects the
features showing the least overlap in a bottom-up or sequential forward selection algorithm. The same features can be used over the whole concentration range investigated even though the response curve changes considerably with gas concentration. For the discrimination a hierarchical approach is used, i.e. not all classes are separated in one step. Instead, subgroups are defined which are further discriminated by using different features. This approach has two main advantages. First, the computing capacity necessary for this classification approach is very low so that low cost systems can be realized;
Fig. 3. Normalized sensor response curves showing almost no influence from changes in relative humidity.
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Fig. 4. First step of the hierarchical classification discriminating between clean air, benzene, iso-pentane and others. The figure shows 80 measurements for each gas in the concentration range of 2–200 ppm at 30 and 70% relative humidity. Values for 2, 5, 10, 20, 50, 100, 200 ppm benzene and iso-pentane are shown as full symbols to indicate the effect of relative humidity and increasing concentration (arrows). Features were normalized to the interval [0, 1]. x–y means values x to y in the temperature cycle.
classification using linear discriminant analysis (LDA) [12] yields slightly better results (compare Fig. 8), but would need more computing power for the algorithm as more data processing is needed. Second and more important, the quality of each decision is easily checked using a two-dimensional plot of the data as shown for the first step in the classification process (Fig. 4); with these plots possible problems for classification, for example, influence of relative humidity, can be easily identified. In Fig. 4 the area for iso-pentane and benzene might overlap if iso-pentane is measured at very low relative humidity values. If this situation might occur in the envisaged application, the classification can be improved by using additional features (see Fig. 6). The final validation, necessary for all artificial nose or machine olfaction applications, is therefore more transparent for the user. To study the reproducibility of the commercial sensors used in these tests, four test systems with identical sensors
were set up. We found that the features and the classification steps can be transferred from one calibration system (system 1 in Fig. 4) to the other sensor systems, but the areas corresponding to the different classes have to be individually adapted for each system or sensor combination. Fig. 5 shows the same plot for systems 2–4 as in Fig. 4 (first decision of the hierarchical classification). The plots show that discrimination of the four classes (air, benzene, iso-pentane, others) is possible for all four systems. However, in system 4 the separation of the classes benzene and iso-pentane is marginal, so that changes in humidity or even slight sensor drift might lead to false classifications. In this case an additional classification step could be added, which allows good separation between both classes as shown in Fig. 6. This step would also improve the classification for the other sensor systems. This example shows that the presented classification method has excellent reserves for achieving sufficient
Fig. 5. First classification step as shown in Fig. 4 for three sensor systems with the same sensors as in the reference system 1. The areas for the different classes have to be individually adapted to the sensors.
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Fig. 6. Alternative classification step for separation of benzene and iso-pentane for system 4. Solid symbols represent concentration of 2, 5, 10, 20, 50, 100, 200 ppm with arrows indicating increasing concentration.
selectivity for most applications. Indeed, we could show that the classification presented here is even possible with a single sensor (UST 1330), dynamic operation and online-switching between different temperature cycles for critical decisions [13], see also outlook below. In the case of the single sensor system, however, the classification has to be adapted more thoroughly to the individual sensor.
Fig. 7. Decision tree for the identification of six organic solvents with hierarchical classification.
Fig. 8. LDA plot showing good separability between five solvents using secondary shape features. Synthetic air and iso-pentane were separated in a prior step.
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To achieve a 100% classification different features have to be used for each sensor requiring a complete calibration of each sensor. For the dual sensor systems on the other hand, the classification scheme can be adapted more easily to individual sensor combinations by measuring only a few concentrations for each gas and adapting the classification areas accordingly. Alternatively, sensors could be pre-selected for
a given application based on a few gas measurements and comparison with the standard sensor. If the encountered differences are too large, the sensor would not be accepted for this application but could be used in other applications where less selectivity is acceptable. The identification of the remaining four solvents can be achieved by further classification steps not shown in detail here, for further results see [13]. Overall, the complete
Fig. 9. LDA plots for five solvents using raw data and secondary features generated using Fourier (DFT) and wavelet transforms. Discriminant functions are specific for each feature set. Symbols are the same as in Fig. 8.
Fig. 10. LDA plot for shape features generated from system 2 using the discriminant functions of system 1. Symbols are the same as in Fig. 8.
A. Gramm, A. Schütze / Sensors and Actuators B 95 (2003) 58–65 Table 1 Features used for the classification as shown in Fig. 7a Decision
Features
1 2 3 4 5
Av. 6–8/Av. 1–20; Sl. 11–13/Av. 1–20 (Sl. 6–8 − Sl. 11–13)/Av. 1–20; Av. 6–8/Av. 15–17 Av. 11–13/Av. 1–20; Av. 15–17/Av. 18–20 Sl. 4–5/Av. 1–20; Sl. 15–17/Sl. 18–20 Av. 11–13/Av. 18–20 (Sl. 6–8 – Sl. 11–13)/Av. 1–20; Av. 15–17/Av. 18–20 a
Features extracted from UST 1330 data are shown in bold type, and those from UST 2330 in italics. Av.: average; Sl.: slope.
classification is summarized in the decision tree shown in Fig. 7. Decision 1 is shown as an example in Fig. 4, in the next step either benzene and iso-pentane are discriminated (Fig. 6) or diethyl ether is identified followed by methyl tert-butyl ether and finally classification between propylene oxide and methyl alcohol. Table 1 summarizes the features which are used for the classification steps. In the final classification, three features are used to achieve a 100% classification for all four sensor systems as all two-dimensional plots show overlaps between classes which are resolved by taking into account a third dimension.
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functions of system 1 (i.e. identical functions as in Fig. 8). A similarity between both plots is evident by the relative position of the groups, but all groups are shifted and/or spread over a much larger area preventing a successful classification. The higher class separation performance of LDA obviously leads to reduced generalization performance—a problem that is often encountered with classification methods with many free parameters usually denoted as overfitting [11]. Compare this result with the two-dimensional plots of our shape features as shown in Figs. 4 and 5; with these features an adaptation of the areas used for the identification of the different gases seems possible with only a few reference measurements for each system (i.e. one low, medium and high concentration for each gas). As the effect of increasing gas concentration and humidity is easily seen and reproducible, the classification areas can be individually set for each system.
2.1. Comparison with other feature generation methods We compared our classification approach using secondary shape features with the information provided by the raw data from the temperature cycles and with features generated by Fourier and wavelet transforms. LDA, which directly maximizes class separability [11,12], was used to determine the relative information content of the features. Even with this technique, which makes use of all available data simultaneously, it is not possible to classify all solvents in one step; to achieve 100% classification with good separation (i.e. distance between classes greater than typical scatter) hierarchical steps also had to be used. In this comparison our shape feature generation approach has proven more powerful than FFT or wavelet; after separation of air and iso-pentane in a first step, LDA allows direct classification of all five remaining solvents with the reference sensor system 1 as shown in Fig. 8. By comparison, LDA plots of the same five classes using the raw data as well as FFT and wavelet features generated from the data of system 1 show significant overlap between the classes (Fig. 9) necessitating several more hierarchical classification steps. It is interesting to note that the LDA plots obtained with all four data sets are very similar (i.e. relative position of the classes). Therefore the resulting decision trees for hierarchical classification would be nearly identical for all data sets (in this case, methyl alcohol would be separated next). Unfortunately, the discriminant functions obtained from the LDA of system 1 cannot be transferred to the other systems. Fig. 10 shows the shape feature data generated from the calibration of system 2 and plotted using the discriminant
Fig. 11. Temperature cycle 2 (a) allowing improved discrimination between ethers and dynamic development of response curves after switching between different temperature cycles (b–c).
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3. Conclusion/outlook
References
In this paper we have shown that stable low cost sensor systems with very high selectivity can be achieved using commercial semiconductor gas sensors with dynamic operation (i.e. temperature cycling) and pattern classification techniques. Furthermore, the presented classification approach leads to transparent and easily validated decisions. The same classification can be adapted to different sensor systems with only a few calibration measurements. A pre-selection of the sensors might be necessary, if fabrication parameters are varying over a too large range. On the other hand our approach could also be used to improve the quality control during the manufacturing process. Further work is necessary to allow easier identification of suitable temperature cycles for a given application as this process is currently very time consuming. A possible approach currently under study in our group tries to extract relevant parameters from a series of standardized measurements which are then used to predict the response curves for arbitrary temperature cycles, see also [14,15]. Further improvement of the overall system performance is possible by implementing different temperature cycles (T-cycles) within one system. The standard operation would be based on T-cycles individually adapted for each sensor to provide maximum long term stability (and/or low power consumption depending on application requirements). If a gas is detected, which is easily possible even with very simple cycles and signal processing, the system can then switch to different T-cycles providing sufficient information for gas identification. This online-switching can be repeated if a branch is reached in the hierarchical classification where further classification is not possible. Fig. 11 shows the development of the response curves after switching from our standard T-cycle 1 (Fig. 1) to a two temperature cycle spanning a larger range. This cycle allows discrimination of the three chemically very similar ethers even with single sensor systems [13]. It has to be noted, that the overall performance of this cycle for all gases is inferior to T-cycle 1. After changing to a different cycle, the response curves quickly reach their final form (Fig. 11b and c) allowing classification within 1 min after switching. Therefore this enhancement can be used in many applications which are not time critical providing an opportunity to optimize the system for different operating conditions.
[1] E. Llobet, R. Ionescu, S. Al-Khalifa, J. Brezmes, X. Vilanova, X. Correig, N. Bˆarsan, J.W. Gardner, Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition, IEEE Sens. J. 1 (3) (2001) 207–213. [2] Y. Kato, K. Yoshikawa, M. Kitora, Temperature-dependent dynamic response enables the qualification and quantification of gases by a single sensor, Sens. Actuators B 40 (1997) 33–37. [3] A. Heilig, N. Bˆarsan, U. Weimar, M. Schweizer-Berberich, J.W. Gardner, W. Göpel, Gas identification by modulating temperatures of SnO2 -based thick film sensors, Sens. Actuators B 43 (1997) 45– 51. [4] S. Nakata, E. Ozaki, N. Ojima, Gas sensing based on the dynamic nonlinear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change, Anal. Chim. Acta 361 (1998) 93–100. [5] S. Nakata, S. Akakabe, M. Nakasuji, K. Yoshikawa, Gas sensing based on a nonlinear response: discrimination between hydrocarbons and quantification of individual components in a gas mixture, Anal. Chem. 68 (1996) 2067–2072. [6] R.E. Cavicchi, J.S. Suehle, K.G. Kreider, M. Gaitan, P. Chaparala, Optimized temperature–pulse sequences for the enhancement of chemically specific response patterns from micro-hotplate gas sensors, Sens. Actuators B 33 (1996) 142–146. [7] A.P. Lee, B.J. Reedy, Temperature modulation in semiconductor gas sensing, Sens. Actuators B 60 (1999) 35–42. [8] L. Ratton, T. Kunt, T. McAvoy, T. Fuja, R. Cavicchi, S. Semancik, A comparative study of signal processing techniques for clustering microsensor data (a first step towards an artificial nose), Sens. Actuators B 41 (1997) 105–120. [9] UST Umweltsensortechnik GmbH, Geschwenda, Germany. http://www.umweltsensortechnik.de. [10] H. Petig, J. Kelleter, D. Schmitt, Gas sensor fire detectors prove effective in coaling plants, Global Risk Rep. 4 (1999) 19–22. [11] R. Gutierrez-Osuna, Pattern analysis for machine olfaction, IEEE Sens. 2 (3) (2002) 189–202. [12] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed., Wiley, New York, 2000. [13] A. Schütze, A. Gramm, T. Rühl, Identification of organic solvents by a virtual multisensor system with hierarchical classification, Presented at IEEE Sensors 2002, Orlando, USA, June 12–14, 2002. [14] S. Nakata, K. Takemura, K. Neya, Non-linear dynamic responses of a semiconductor gas sensor: evaluation of kinetic parameters and competition effect on the sensor response, Sens. Actuators B 76 (2001) 436–441. [15] J. Ding, T.J. McAvoy, R.E. Cavicchi, S. Semancik, Surface state trapping models for SnO2 -based microhotplate sensors, Sens. Actuators B 77 (2001) 597–613.
For further reading see [1–8].
Acknowledgements Financial support for this work by Merck KGaA, Germany, is gratefully acknowledged.
Biographies Andreas Gramm studied Physics at the University Regensburg and finished his diploma thesis in 1999. Since autumn 1999 he is working on his PhD thesis in the group of Prof. Dr. A. Schütze, first at the University of Applied Sciences in Krefeld, since April 2000 at the Laboratory for Measurement Technology in the Department of Electrical and Electronic Engineering of Saarland University, Germany. His research focuses on temperature modulation of semiconductor gas sensors, appropriate signal processing and the implementation of these methods in innovative gas sensor applications. Andreas Schütze received his diploma in Physics from RWTH Aachen in 1990 and his doctorate in Applied Physics from Justus-Liebig-University
A. Gramm, A. Schütze / Sensors and Actuators B 95 (2003) 58–65 in Giessen in 1994 with a thesis on microsensors and sensor systems for the detection of reducing and oxidizing gases. From 1994 until 1998 he worked for VDI/VDE-IT, Teltow, Germany, mainly in the fields of microsystems technology, technology oriented start-up support and international relations. From 1998 until 2000 he was Professor for Sensors and Microsystem Technology at the University of Applied Sciences in
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Krefeld, Germany. Since April 2000 he is Professor for Measurement Technology in the Department of Electrical and Electronic Engineering of Saarland University, Saarbruecken, Germany and Head of the Laboratory for Measurement Technology (LMT). His research interests include microsensors and microsystems, especially gas sensors and sensor systems for security applications.