Commercial Gas Sensors Research Articles

Evaluation of unpleasant odor with a portable electronic nose

An intelligent multi-sensor system with an entire autonomy, a low weight and a small size has been developed for in situ applications such as environmental pollutant gas detection or olfactory estimation. This portable electronic nose works with commercial metal oxide gas sensors and a microcontroller connected to a compact flash memory as intelligent unit. In this work we present the conception of this electronic nose, its laboratory validation, and a real application which concerns an outdoor air monitoring of a duck breeding. This application is developed in order to quantify continuously discomfort odor spread, causing neighbor complaints. The odor measurements were made using our electronic nose (Nepo) and results are compared to the simultaneous results obtained from other olfactometric techniques.

Ozone detection in the ppb-range with improved stability and reduced cross sensitivity

Abstract In the following paper, a novel method for determining the concentration of ozone using commercial microstructured semiconductor gas sensor elements is presented. The “virtual multisensor” principle was used with a quasi continuous sensor temperature variation (temperature cycle) to improve stability and reduce cross sensitivity to typical interfering gases compared to constant temperature operation. Characteristic features extracted from the sensor resistance response allow accurate prediction of the ozone concentration in the relevant range from 0 to 100 ppb. The feature extraction and concentration prediction algorithm considerably improves reproducibility, yielding valid results also for aged sensors and even when applied to different sensors of the same type reducing the necessity of individual sensor calibration. The combination of low power consumption and simple data processing allows cost-efficient realization of handheld devices or ozone detection in home air cleaners.

Design of a Smart Gas Sensor System for Room Air-Cleaner of Automobile (Thick-Film Metal Oxide Semiconductor Gas Sensor)

It is almost impossible to secure the reproductibility and stability of a commercial Thick-Film Metal Oxide Semiconductor Gas Sensor since it is very difficult to keep the consistency of the manufacturing environment. Thus it is widely known that the general Semiconductor-Oxide Gas Sensors are not appropriate for precise measurement systems. In this paper, the output characteristic analyzer of the various Thick-Film Metal Oxide Semiconductor Gas Sensors that are used to recognize the air quality within an automobile are proposed and examined. The analyzed output characters in a normal air chamber are grouped by sensor ranks and used to fill out the characteristic table of the Thick-Film Metal Oxide Semiconductor Gas Sensors. The characteristic table is used to determine the rank of the sensor that is equipped in the current air cleaner system of an automobile. The proposed air control system can also adapt the on-demand operation that recognizes the history of the passenger's manual-control.

Interplay between O2 and SnO2: Oxygen Ionosorption and Spectroscopic Evidence for Adsorbed Oxygen

Tin dioxide is the most commonly used material in commercial gas sensors based on semiconducting metal oxides. Despite intensive efforts, the mechanism responsible for gas-sensing effects on SnO(2) is not fully understood. The key step is the understanding of the electronic response of SnO(2) in the presence of background oxygen. For a long time, oxygen interaction with SnO(2) has been treated within the framework of the "ionosorption theory". The adsorbed oxygen species have been regarded as free oxygen ions electrostatically stabilized on the surface (with no local chemical bond formation). A contradiction, however, arises when connecting this scenario to spectroscopic findings. Despite trying for a long time, there has not been any convincing spectroscopic evidence for "ionosorbed" oxygen species. Neither superoxide ions O(2)(-), nor charged atomic oxygen O,(-) nor peroxide ions O(2)(2-) have been observed on SnO(2) under the real working conditions of sensors. Moreover, several findings show that the superoxide ion does not undergo transformations into charged atomic oxygen at the surface, and represents a dead-end form of low-temperature oxygen adsorption on reduced metal oxide.

Development of a Portable Electronic Nose System to Analyze Flavour Signal Patterns of Kochujang

The purpose of this study is to develop a portable electronic nose system to measure volatile components of agricultural and food products. Also, operating software to control the electronic nose system through the Internet was developed. Various experiments to find optimum operating conditions of the system were performed. An array of commercial metal oxide gas sensors was used to detect various gas components. For kochujang experiment, flavour signal patterns were different to the type of kochujang. Transient and steady state signals were analyzed. Transient signal analysis was more useful in PCA. Cluster analysis showed the possibility of reduction of the number of sensors. It is feasible to optimise the kochujang making process.

Bayesian Learning Using Gaussian Process for Gas Identification

In this paper, a novel gas identification approach based on Gaussian process (GP) combined with principal components analysis is proposed. The effectiveness of this approach has been successfully demonstrated on an experimentally obtained dataset. Our aim is the identification of different gases with an array of commercial Taguchi gas sensors (TGS) as well as microelectronic gas sensors. The proposed approach is shown to outperform both K nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers

The electronic nose as a warning device of the odour emergence in a compost hall

A self-made electronic nose consisting in a sensor array of six commercial tin oxide gas sensors is used to monitor the odour emission from a compost facility. Supervised data processing tools, such as discriminant analysis, are able to recognize, in real time, the odour of compost with respect to other possible sources in the hall. The paper shows that with unsupervised methods, such as principal component analysis, it is not essential to identify all the possible odour sources during the learning phase. The closeness to the compost group centroid could be used as an indicator of the compost odour level. Alternatively, by a suitable calibration from olfactory measurements, the signals generated by the sensor array can be used to estimate the odour emission rate from the compost hall. Such real time monitoring should allow to assess and to anticipate the annoyance in the surrounding.

Identification of gas mixtures by a distributed support vector machine network and wavelet decomposition from temperature modulated semiconductor gas sensor

Semiconductor gas sensors are wildly applied in gas identification and temperature-modulated dynamic test method is frequently used to improve sensors’ selectivity and stability. This paper introduces a new strategy combing wavelet decomposition and a distributed support vector machine network to identify gas mixtures. In a previous study concerning identifying gases of a few types, we had demonstrated that wavelet decomposition outperforms FFT in feature extraction from responses of thermally modulated semiconductor gas sensors. Here, we extend this method of recognizing gas mixtures of a large type and generalize the rules of selecting the character variables from decomposed wavelet coefficients as character variables. Besides, a distributed support vector machine network is introduced to discriminate the character variables into gas patterns instead of traditional BP neural network. Experiment results show that the strategy proposed here can perform accurate discrimination between hydrogen (H2), carbon monoxide (CO), ethane (C2H4) and their mixtures (total seven patterns) over a range of 50–1000ppm, by using only one commercial metal oxide semiconductor gas sensor, and the discrimination result has advantages in restraining gas concentration variation, sensor's drift and environmental changes. Experimental result also suggests that a distributed support vector machine outperforms a single support vector machine and a BP neural network in performing gas mixture identification.

Fluctuation-enhanced multiple-gas sensing by commercial Taguchi sensors

This study is carried out to investigate and demonstrate the possibility of practical applications of commercial Taguchi gas sensors for fluctuation-enhanced chemical sensing of multiple gases. The stochastic resistance fluctuations of the sensors were studied during the burning-in process and during exposure to different gases at different temperatures. The analysis of resistance fluctuations in the gas sensors during and after exposure to gases indicates a strongly enhanced sensitivity and selectivity.

Micromachined twin gas sensor for CO and O 2 quantification based on catalytically modified nano-SnO 2

In this work we present a micromachined twin sensor that can distinguish between carbon monoxide (CO) and O 2 gas taking advantage of the high sensitivity of SnO 2 to these gases. The SnO 2 nanoparticles of both sensors are catalytically modified with different Pd loadings that act as active filters. In this way, one sensor response is turned to present a higher sensitivity to CO than to O 2, whereas the other sensor is mainly turned for detecting O 2 variations. The twin sensor has two membranes in the same die of micromachined silicon. Each membrane works independently from the other without any cross talk of temperature. The resistance data obtained from this twin sensor as a function of CO and O 2 concentrations is parametrized. Then, two functions are calculated for the quantification of CO/O 2 gas mixtures. A comparison with commercial gas sensors is shown.

High performance of gas identification by wavelet transform-based fast feature extraction from temperature modulated semiconductor gas sensors

Temperature modulation of semiconductor gas sensors is a powerful strategy to improve selectivity and stability of gas sensors in applications of identifying different gases. This paper presents a new strategy to extract features from the response of a thermally modulated semiconductor sensor for gas identification. This strategy contains two main steps. First, make the sensor work under temperature modulation and pre-process the sensor's response signal. The presented pre-processing method can suppress the influence of environmental temperature or humidity on sensor response. Secondly, apply wavelet decomposition to extract features of the pre-processed sensor's response curves. The rules of selecting the resulted wavelet coefficients as the characteristic variables for gas identification are also presented. Experiments results show that the strategy proposed here allows accurate discrimination between gases studied (hydrogen, carbon monoxide and their binary mixture) over a wide concentration range, from 10 to 1000 ppm, by using only one commercial metal oxide gas sensor and owns good ability of restraining the influence of gas concentration variation, sensor's cross sensitivity and sensor drift on the identification result. Experiments also show that the proposed strategy outperforms FFT for the featured extraction from the response of thermally modulated semiconductor gas sensor.

PDA를 이용한 휴대용 Electronic Nose 시스템 개발

We have designed a portable electronic nose (e-nose) system using an array of commercial gas sensors and personal digital assistants (PDA) for the recognition and analysis of volatile organic compounds (VOC) in the field. Field screening of pollutants has been a target of instrumental development during the past years. A portable e-nose system was advantageous to localize the special extent of a pollution or to find pollutants source. The employment of PDA improved the user-interface and data transfer by Internet from on-site to remote computer. We adapted the Lavenberg-Marquardt algorithm based on the back-propagation and proposed the method that could predict the concentration levels of VOC gases after classification by separating neural network into two parts.

필드 스크린을 위한 휴대용 전자코 시스템의 구현

There is currently much interest in the development of instruments that emulate the senses of humans. Increasingly, there is demand for mimicking the human sense of smell, which is a sophisticated chemosensory system. An electronic nose system is applicable to a large area of industries including environmental monitoring. We have designed a protable electronic nose system using an array of commercial chemical gas sensors for recognizing and analyzing the various odours. In this paper, we have implemented a portable electronic nose system using an array of gas sensors for recognizing and analyzing VOCs (Volatile Organic Compounds) in the field. The accuracy of a portable electronic nose system may be lower than an instrument such as GC/MS (Gas Chromatography/Mass Spectrometer). However, a portable electronic nose system could be used on the field and showed fast response to pollutants in the field. Several different algorithms for odours recognition were used such as BP (Back-Propagation) or LM-BP (Levenberq-Marquardt Back-Propagation). We applied RBF (Radial Basis Function) Network for recognition and quantifying of odours, which has simpler and faster compared to the previously used algorithms such as BP and LM-BP.

High performance solvent vapor identification with a two sensor array using temperature cycling and pattern classification

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 200ppm 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.

A portable electronic nose based on embedded PC technology and GNU/Linux: hardware, software and applications

This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented.

A practical approach for fish freshness determinations using a portable electronic nose

A portable electronic nose was developed and applied to fish freshness determinations of Argentinean hake. The applied methodology differs from the typical ones based on the headspace method or glass syringe manipulation. In the present study, a weighted piece of fish is introduced into a sensor’s chamber and the signals of the sensors due to the fish emissions are recorded as function of time. Commercial gas sensors, based on tin dioxide, were used. Analysis of the individual sensor signals and of the array’s pattern was performed. An increase of the signals was observed with increasing storage days and the mass of the hake pieces (up to 50 g). The obtained response patterns are independent of the weight but change after some days of storing, indicating the rottenness of the sample. Two different patterns are obtained, associated to rotten and non-rotten samples, respectively. These patterns are independent of storage conditions; for example, the pattern of a rotten sample is the same independently if the piece was stored several days in a refrigerator or if was kept out for 1 day. Hence, it is quick and easy to identify between fresh and old samples. These results were confirmed by performing principal component analysis (PCA).

Qualitative Determination of the Degree of Doping of Commercial SnO2 Semiconductor Sensors from the Nature of Their Reaction with Hydrogen

The interaction of commercial gas sensors TGS 109, TGS 812, TGS 813, and TGS 815D, manufactured by Figaro, Inc., Japan, with hydrogen has been studied to explain the degree to which they are doped with palladium as a catalyst and the results of those studies are reported here. The method used in the study is based on finding the exponent of the power-law dependence of the sensor conductivity on the hydrogen concentration in the air.

Application of radiometric temperature determination methods to semiconductor gas sensors

A radiometric method for the determination of the operating surface temperature of very small objects to within ±5 K has been applied to commercial semiconductor gas sensors operated in a static atmosphere. The method involves the collection of the infrared (IR) emission spectrum of the sensor at regular intervals over the operating heater voltage range, followed by the fitting of ambient-corrected Planck functions to determine the temperature at each voltage. At least one independent calibrating temperature measurement (to eliminate the effects of sensor emissivity and spectrometer response) is obtained using the known melting point of an inorganic salt. The operating voltage–temperature relationships for two popular Figaro tin oxide sensors were found to be pseudo-linear and are reported as T=103 V+214±3 K for the Figaro TGS813 sensor with its base removed, T=101 V+224±5 K for the TGS813 with its base attached, and T=106 V+238±5 K for the Figaro TGS2611 sensor. (The temperature of the sensors does not rise appreciably above ambient when they are operated below 0.5 V.) These results indicate that sensor temperatures are significantly higher than most previously reported estimates, particularly those made using infrared thermometers (IRTs). A heat loss model for the sensors is discussed, and the calibration of the heater resistance–temperature relationship is achieved for the TGS2611.

Discrimination of carbon dioxide and fluorocarbon using semiconductor gas sensors

In this paper, we present the discrimination of two antagonist gases, using an array composed of five commercial gas sensors (two TGS 832, two TGS 813, one TGS 800). From the characterisation of the sensor array under forane R134a, carbon dioxide and mixtures of these two gases in dry synthetic air, we have extracted two main parameters, transient and steady-state conductance values. In general, only the steady-state conductance value is taken. However, we have characterised the sensor transient behaviour by using the measurement of the slope of the conductance when there is a step change in the gas concentration. Thus, discriminant factorial analysis (DFA) is carried out to show qualitatively that the selectivity is improving when taking into account simultaneously the two parameters. This makes possible the determination of the nature of several gases present in the studied atmosphere.

Use of a simple tin oxide sensor array to identify five malodours collected in the field

A laboratory-made malodour sensing system including 12 commercial tin oxide gas sensors (Figaro Engineering) is used to identify five typical sources of olfactive annoyance: printing houses, paint shop in a coachbuilding, wastewater treatment plant, urban waste composting facilities and rendering plant. In this work, all the samples are collected in the field from real malodours in uncontrollable conditions. The ability of the system to predict the origin of unknowns odoriferous samples is investigated. The test of various pre-processing data algorithms shows that the best classification results are obtained with a parameter free of the sensor base-line. The differences in sensor responses among the five odours are shown by icon plots and confirmed by principal component analysis, which highlights four representative clusters. Classification models calibrated by discriminant analysis and artificial neural network are validated on unknowns samples. Chemical relationships between the sensors and the classification results proves that the recognition is not fortuitous. In spite of the influence of environmental parameters, results demonstrate the ability of a simple system to detect and identify typical olfactive annoyances.