Discrimination abilities of transient signal originating from single gas sensor

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1 Discrimination abilities of transient signal originating from single gas sensor MONIKA MACIEJEWSKA, ANDRZEJ SZCZUREK Faculty of Environmental Engineering Wroclaw University of Technology Wyb. Wyspianskiego 27, Wroclaw POLAND Abstract: - In recent years dynamic signals, which are measured when gas sensing material is in unsteady state, have received much attention. In this work discrimination abilities of differently evoked transient gas sensor signals were analyzed. Gas recognition problem consisted in discriminating between eight organic pollutants in air. A considerable pool of fifteen semiconductor gas sensors was investigated. Based on our results, the transient sensor signal induced by the rapid alteration of exposure conditions exhibited high dicrmination abilitiy. The influence of phenomena which cause transient states of gas senor could be observed when applying the appropriate fragmentation of sensor signal for the sake of analysis. Key-Words: - gas sensor, signal preprocessing, volatile organic compounds 1 Introduction According to the current IUPAC's (International Union of Pure and Applied Chemistry) definition, a chemical sensor is a device that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytically useful signal. Gas sensors are one of the most important groups of these devices. Sensor types for gas measurements can be quite diverse. Metal-oxide semiconductor gas sensors based on a change of the sensing element resistance under the presence of gases, currently constitute one of the most investigated groups of gas sensors [1]. These devices offer high sensitivity in detecting very low concentrations (at level of ppm or even ppb) of a wide range of gaseous chemical compounds, small sizes, low cost and flexibility in production, simplicity of their use, on-line operation. Moreover, the possibility of easily combining in the same device the functions of a sensitive element and signal converter and control electronics markedly simplifies the design of a sensor and constitutes the main advantage of chemiresistive-type sensors over biochemical, optical, acoustic, and other gas-sensing devices. Although semiconductor gas sensors are widely used, it is known that they are not selective to detect an individual chemical species in a gaseous mixture. This limitation is originating from fundamental features of the sensing mechanism. The semiconductor gas sensor responds to most reducing gases, such as hydrocarbons and alcohols, and therefore it is impossible to oxidize only a specific gas in a gaseous mixture for detection. Different strategies are used to solve lack of semiconductor gas sensor selectivity. One of them is based on the collection and processing of signal from partially selective single sensor operated in dynamic conditions. Nowadays, gas sensor technology exploits three types of signals: (1) static or quasi-static, (2) periodic, (3) transient and quasi-transient [2]. This diversification is important because signals can include different types of information about the analyte [3]. It results from the sensing mechanism of semiconductor gas sensor. Static or quasi-static signals are measured when sensing material is in steady state. This state is obtained in stationary conditions that do not change over time (quantities acting on the sensor are constant in time). The sensor is at equilibrium with a surrounding gas in this case. Static signals are preferred in commercial instruments up to now, because they are easily measurable and timeindependent. Thus the preprocessing and data analysis are less complicated. Additionally, problems with unstable flow, pressure and temperature of gas sample at the beginning of exposure are minimized. Static signal is very often delayed because measurement system needs time to reach equilibrium (steady state). This time is dependent on factors such as flow rate of sample, volume of the sensor chamber and the gas delivery line, gas diffusion rate and sensor reaction rate. ISBN:

2 Furthermore, in the static signal there is inaccessible information originating from the kinetics of processes which induce sensor response. The magnitude of the static signal is related to concentration of gas under test. Information on the temporal axis is ignored. Therefore static signal include one-dimensional information. It is inadequate for distinguishing between the response to a target gas and those to other interfering chemical species. To overcome this problem, an alternative approach has received much attention in recent years. It is based on dynamic signals (periodic or transient) which are measured when a sensing material is in unsteady state [4]. Sensor keeping in this state produces a time-dependent, non-linear output signal, which is a function of three variables: (1) physical and chemical properties of gas under test, (2) its concentration and (3) time. The time dependent signal is related to the kinetics of gas molecules (i.e. diffusion, adsorption, reaction and desorption) on the semiconductor surface, and is influenced by the chemical structure and concentration of the gas species [5]. Therefore signal measured in unsteady state of the sensing material conveys useful information leading to an enhancement in the discriminating ability of gas sensor devices Several attempts have been focused on a signal generation in unsteady state of the sensing material. In cycling approach, the sensor is excited by parameter changing in a periodic way. Periodic signals may be induced using AC operation mode, modulation of the gas concentration or flow [6], working parameters (especially sensor temperature) [7]. The special equipment coupled with gas sensor is required in this case [8]. In transient technique, on the other hand, the sensor is driven by a step or pulse waveform activation. In this work, we have developed a method for generation of transient signals based on a stop flow mode of operation. We assume that unsteady conditions in sensor surrounding are reflected in transient state of sensing material. In our approach output signal is affected by changes of sensor exposure conditions to gas under test. The alterations are mainly caused by a time-dependent gas composition, gas flow and sensor temperature. 2 Problem formulation The aim of this work was to determine discrimination abilities of different kinds of the transient signal. This knowledge allows us to design simple measurement system based on a single sensor and a pattern recognition algorithm. 2.1 Experimental setup The experimental setup, which was used for performing measurements consisted of the following functional blocks: 1. the pure air generator, 2. the system for preparation of test gas, 3. the unit containing measurement chambers and gas sensors, 4. the voltage supplier, 5. the interface circuits containing reference resistors, 6. the digital multimeter with multiplexer module and the data acquisition card, 7. the personal computer with HP BenchLink Datalogger software). Sensor signals were measured with 1 s time resolution. Fifteen commercially available Taguchi Gas Sensors made by Figaro Engineering Japan were investigated in this work: TGS821, TGS822, TGS824, TGS825, TGS826, TGS880, TGS883, TGS800, TGS2201 (gasoline), TGS2201 (diesel), TGS2106, TGS2104, TGS2602, TGS2620, TGS2600. These sensors were chosen because of their satisfactory performance in many measurement applications. Single sensor was mounted inside its own, specially designed, airtight, flow-type test chamber. Chambers were made of aluminum and each of them was fitted with gas inlet and outlet. The single chamber could be interchangeably connected with pure air generator and with system for test gas preparation. 2.2 Measurement procedure Gas sensor responses obtained in stop flow mode of operation were investigated in this work regarding transient sensor signals. The transient character of sensor signal in this operation mode resulted from the following operations: i) delivery of the test gas to sensor chamber, ii) stopping the flow of the test gas, iii) delivery of pure air to the sensor chamber. The operations were performed in sequence, upon single exposure to the test gas, as shown in Fig.1. The first operation resulted in the dynamic exposure of a sensor to a stream of air and volatile organic compound of interest. The sample was allowed to continuously flow through the chamber with a sensor inside, at a constant rate (0.07 l/min). Transient sensor signal occurred in response to sample injection into the measurement system. It resulted from the kinetics of the processes which evoked sensor response. Initially, the concentration of the test gas was quickly increasing in the sensor ISBN:

3 chamber, as it was earlier filled with pure air. The gas concentration change at the sensor surface was associated with that. The conductivity of semiconductor was additionally affected by a number of time dependent processes such as: the transport of the reactive species into the sensor, the diffusion of gas molecules inside pores of the sensing material, adsorption and desorption, the catalyzed redox reactions on the surface of the sensing layer (mainly their kinetics) and the electrical/electronic effects in the semiconductor. All these processes caused quick increase of sensor signal. Later, the atmosphere surrounding sensor stabilized, which allowed for attaining the dynamic equilibrium and a quasi-steady state of the sensing material. The sensor signal was no longer considered transient. chamber with a stream of pure, dry air. The fragment of the rapid sensor signal change was considered transient. Following, the regeneration of sensors continued, until readouts from each device reached the level as before the dynamic exposition to test gas. The sensor signal was losing its transient character. Segments of the output signal related to various conditions of the sensor exposure to the test gas contain different information about the analyte. Measurements in steady state reflect mainly the thermodynamics of interactions between the target gas and the sensing material, whilst results associated with transient states contain information about kinetics of adsorption/desorption, chemical reactions and the charge transfer. Each of these processes has its own characteristic time constant, which is dependent on properties of sensing material and gas molecules, especially their molecular weight and shape, electron affinity, ionization potential, diffusion coefficient. Hence, the information contained in the signal which was measured when sensors were in steady and transient states may be quite different. While analyzing a continuous sensor signal the distinction of transient parts is not obvious and may be considered disputable. Therefore, in order to arrive at the complete picture, we decided to investigate the entire sensor signal obtained in the stop flow mode. Fig.1. The principle of the transient signal generation. Transient parts of the sensor signal were marked with the dash-line boxes. Stooping the test gas flow was another operation, which resulted in transient sensor signal. The sensing material was in transient state, because sensor temperature and partial pressure of the volatile organic compound in sensor chamber changed continuously due to oxidation reaction at sensor surface. The output signal decreased slowly while the gas remained still in the sensor chamber. Initially, directly after flow stopping, the change was more rapid and this part of the sensor signal was considered transient. The third kind of transient sensor signal, considered in this work, resulted from the delivery of pure air to sensor chamber, at a constant rate (0.07 l/min). The processes, which influenced transient response, were similar as upon the first operation. The response was obtained in dynamic conditions of exposure. But, this time, the chemical composition of gas stream was changed, because the test compound was removed from the sensor 2.3 Target gases Single gas senor measurements were the basis for discrimination of eight volatile organic compounds. They were investigated in the following concentration ranges: cyclohexane ( ppm), hexane ( ppm), heptane ( ppm), octane ( ppm), benzene ( ppm), ethylobenzene ( ppm), toluene ( ppm), xylene ( ppm). Four concentrations of each substance were considered. Five replicate measurements of toluene (212 ppm) were additionally performed as a repeatability test. The results of these measurements were utilized to increase the training data set. Based on n replicate measurements of toluene the standard deviation s j of the sensor signal at jth time point of exposure was estimated. The generated value of the sensor response rg j was assumed to originate from normal distribution N(r j, (s 2 j/n) 0.5 ), where r j was the actual measured value of the sensor signal obtained in response to a test gas. The data set was increased five-fold in this manner. ISBN:

4 2.4 Discrimination method The discrimination abilities of transient signals as elements of the sensor signal obtained in the stop flow mode of operation were studied in this work. In order to perform the analysis, the sensor signal was divided into smaller fragments, which were delimited by the time window of a fixed size, as shown in Fig.2. Longer parts of sensor signal, including transient ones could be defined as composed of such fragments in a flexible manner. Different fragmentation schemas (eight in total) were applied as determined by the size of the time window. The following sizes were used: 10, 20, 30, 42, 60, 70, 84 and 105 seconds. They were chosen deliberately in order to fulfill the following conditions: i) entire sensor signal has to be covered by the whole number of time windows, ii) time points at which the operations inducing transient sensor signal were executed have to be always the first points of the time window. The reason for applying different fragmentation schemas was the ambiguity of the exact distinction of the transient sensor signal. Fig.2. The principle of the sensor signal fragmentation. The finest fragmentation 10 s (top) and the most coarse fragmentation 105 s (bottom) were shown. The signal fragments between two neighboring markers were used for test gas recognition. The discrimination ability of sensor signal fragments was analyzed as a means of evaluating the discrimination ability of transient signals. In order to eliminate the influence of varied dimensionality on classification results, fragments of different size were mapped into feature space of fixed dimensionality. Principal component analysis (PCA) was applied to perform this operation. Five principal components were found appropriate, based on the criterion of the percent of variance explained. As a result of the employed signal compression all classification tasks were solved in feature spaces of the same dimensionality. The discrimination abilities of the sensor signal fragments were tested with regard to gas recognition problem. There were recognized eight VOCs. This problem was represented as a classification task, which consisted in discriminating among eight classes. Each class was composed of data vectors, which were patters of air contaminated with one of the substances, in the experimentally investigated concentration ranges. The all-against-all type representation of classification problem was utilized. The knn classifier was applied for testing the discrimination abilities of sensor signal fragments. Individual classifier was built for each fragment of the sensor signal. The inputs of the classifier were the first five principal components obtained as a result of sensor signal fragments mapping. From among numerous available knn was chosen as classification methods for a number of reasons. First, knn method is a well known one. To classify a pattern the closest k patterns are found in the dataset and the class, which is predominant among those k neighbors is selected as the class of the investigated pattern. On a number of occasions the method was shown to offer very good classification performance in conditions of limited data availability [9 11]. K-nn classifier is simple. Its development it is not computationally demanding if the data sets are not large. Therefore it may be trained fast. This property was important for our investigations, due to analyzing many fragments of signals from a number of sensors. Classification performance was evaluated in terms of misclassification rate using ten-fold crossvalidation procedure. The possibility of comparing discrimination abilities of the sensor signal fragments was assured by fixing the dimension of feature space and by choosing to apply one classification method in all cases. 3 Problem Solution Based on the performed analysis, there were identified characteristic aspects of discrimination abilities of transient gas sensor signals. They are illustrated in Fig.3 to Fig.5. ISBN:

5 Fig.3. Results of target gases classifiction based on fragments of the signal originating from the gas sensor (TGS 2600). Markers are located in the middle of the time windows. Lines between the markers are shown only to help in following the results associated with the fixed fragmentation level. By comparing Fig.1 with Fig.3 and Fig.4 it can be observed that upon the exposure conditions change evoked by the mentioned operations, the discrimination abilities of gas sensor signals were improved. As shown in Fig.3 and Fig.4 the transients obtained in different way could exhibit different discrimination abilities. Additionally, the transient signals of different sensors, although induced in the same manner performed differently upon solving gas recognition task. Based on this observation different ways of inducing signal transients caused results which were sensor dependent. The particularly interesting gas recognition results (marked with circles in Fig.3 and Fig.4) were associated with sensor signal parts, which occurred immediately after the controlled change of gas sensor operating conditions (marked with squares in Fig.1). In the considered mode of operation these changes were: the initialization of the test gas delivery to the measurement chamber, the stop of the test gas flow through the sensor chamber, and the start of pure air flow in the measurement system (see Fig. 1). In the associated fragments, the sensor signal had transient character. Fig.4. Results of target gases classifiction using fragments of signal originating from the gas sensor (TGS 2201(gasoline)). Markers are located in the middle of the time windows. Lines between the markers are shown only to help in following the results associated with the fixed fragmentation level. Fig.5. Results of target gases classifiction using fragments of signal originating from the gas sensor (TGS 883). Fragmentation level: (a) 10 s, (b) 30 s, (c) 60 s, (d) 105 s. We observed that the influence of phenomena which cause transient states of gas senor and which modify the discrimination ability of sensor signal was revealed only when applying the appropriate signal fragmentation. The fine fragmentation was not favorable in that respect (see Fig.5) and the comprehensive picture emerged while increasing the size of the investigated fragments. As a rule, also the results of target gas discrimination improved while using longer fragments of the sensor signal for pattern recognition. For most of the considered sensors a stable picture of transient signal discrimination ability was obtained when using fragments longer than 1 min. Also the best results of target gas recognition were associated with this level of signal fragmentation. The proposed sensor signal fragmentation method may be applied for analyzing any gas sensor signal regarding the discrimination ability of its transient parts. ISBN:

6 4 Conclusions Discrimination abilities of transient gas sensor signal were studied in this work. Transient sensor states were induced by changes of exposure conditions. They activated and modified the dynamics of a number of phenomena, which directly influence sensor states and its response to gas under test. It was observed that at the points of exposure conditions change caused by: initializing the test gas delivery to the measurement chamber, stopping the test gas flow through the sensor chamber, and starting the flow of pure air in the measurement system, the discrimination abilities of gas sensor signals were increasing. Based on our results, transient signal influenced by the alteration of exposure conditions exhibited high dicrmination abilitiy. The factors which modified exposure conditions were shown to act on different sensors in a dissimilar manner. Therefore they may be employed to modify discriminative abilities of transient gas sensor signals on purpose. The influence of phenomena which cause transient states of gas senor could be observed when applying the appropriate fragmentation of the sensor signal. The sufficiently long fragments were needed to obtain comprehensive characteristics of the discrimination ability of transient gas sensor signal. Acknowledgement This work was supported by the project "Detectors and sensors for measuring factors hazardous to environment - modelling and monitoring of threats", POIG / References: [1] N. Yamazoe, K. Shimanoe, New perspectives of gas sensor technology, Sensors and Actuators B, vol. 138, 2009, pp [2] M.K. Muezzinoglu, A. Vergara, R. Huerta, N. Rulkov, M. I. Rabinovich, A. Selverston, H.D.I. Abarbanel, Acceleration of chemosensory information processing using transient features, Sensors and Actuators B vol. 137, 2009, pp [3] A. Bermak, S. B. Belhouari, M. Shi, D.E. Llobet, J. Brezmes, X. Vilanova, J.E. Sueiras, X. Correig, Qualitative and quantitative analysis of volatile organic compounds using transient and steady state responses of a thickfilm tin oxide gas senosr array, Sensors and Actuators B, vol. 41, 1997, pp [4] M. K. Muezzinoglu, A. Vergara, R. Huerta, N. Rulkov, M.I. Rabinovich, A. Selverston, H.D.I. Abarbanel, Acceleration of chemo-sensory information processing using transient features, Sensors and Actuators B, vol. 137, 2009, pp [5] L. Gajdošik, The concentration measurement with SnO2 gas sensor operated in the dynamic regime, Sensors and Actuators B, vol. 106, 2005, pp [6] N.E. Barbri, C.Duran, J. Brezmes, N. Canellas, J.L. Ramirez, B. Bouchikhi, E. Llobet, Selectivity enhancement in multisensor systems using flow modulaton technique, Sensors, vol. 8, 2008, pp [7] K. An Ngo, P. Lauque, K. Aguir, High performance of a gas identification system using sensor array and temperature modulation, Sensors and Actuators B vol. 124, 2007, pp [8] E. Martinelli, D. Polese, A. Catini, A. D Amico, C. Di Natale, Self-adapted temperature modulation in metal-oxide semiconductor gas sensors, Sensors and Actuators B vol. 161, 2012, pp [9] A Bermak, S.B. Belhouari, M. Shi, D.M. Martinez, Pattern recognition techniques for odor discrimination in gas sensor array, Encyclopedia of Sensors, vol.10, 2006, pp [10] A. Roncaglia, I. Elmi, L. Dori, M. Rudan, Adaptive K-NN for the Detection of Air Pollutants With a Sensor Array, IEEE Sensors Journal, Vol. 4, No. 2, 2004, pp [11] A. Perera, T Sundic, A. Pardo, R. Gutierrez- Osuna, S. Marco, A Portable Electronic Nose Based on Embedded PC Technology and GNU/Linux: Hardware, Software and Applications, IEEE Sensors Journal, Vol. 2, No. 3, 2002, pp ISBN:

Identification of Toxic Gases Using Steady-State and Transient Responses of Gas Sensor Array

Sensors and Materials, Vol. 18, No. 5 (2006) 251 260 MYU Tokyo 251 S & M 0647 Identification of Toxic Gases Using Steady-State and Transient Responses of Gas Sensor Array Kieu An Ngo, Pascal Lauque* and