Metal Oxide Gas Sensor Array Research Articles

A novel headspace integrated E-nose and its application in discrimination of Chinese medical herbs

A novel headspace integrated E-nose based on metal oxide gas sensor arrays is reported. And its performance to distinguish six groups of Chinese medical herbs which might be mixed easily by appearance is presented. The headspace integrated E-nose is mainly consisted of gas sensing module, signal procession module and human-machine interaction module. The E-nose, integrated with an innovative headspace diffusion sampling way, has a compact structure with an approximate size of Φ 48×100mm. For the characteristics of the small size and the headspace diffusion sampling way, it is suitable for on-site volatiles analysis in food and raw material detection. Temperature modulation technology was combined with pattern recognition analysis to enhance the selectivity of the E-nose. Principal components analysis and Sammon mapping were used in the feature extraction, while the sample was discriminated by Fisher discriminant analysis. The correct classification rate of the headspace integrated E-nose for discriminating thirteen species of herbs is 100%, which shows the potential application of the E-nose for on-site volatiles analysis.

Evaluation of calibration transfer strategies between Metal Oxide gas sensor arrays

Inherent variability of chemical sensors makes necessary individual calibration of chemical detection systems. This shortcoming has traditionally limited usability of systems based on Metal Oxide (MOX) sensor arrays and prevented mass-production for some applications. Here, aiming at exploring transfer calibration between electronic nose systems, we exposed five identical 8-sensor detection units to controlled gas conditions. Our results show that a calibration model provides more accurate predictions when the tested board is included in the calibration dataset. However, we show that previously built calibration models can be extended to other units using a reduced number of measurements. While baseline correction seems imperative for successful baseline correction, among the different tested strategies, piecewise direct standardization provides more accurate predictions.

Ultra‐dense and ultra‐low power microhotplates using silica aerogel

A thin film of silica aerogel has been spin coated on silicon wafers to investigate its super thermal insulating capability. The temperature of the thin film heaters fabricated on the aerogel coated wafer is measured at different applied powers. Simulations are performed to compare the aerogel coated wafers with the micromachined wafers. Both experimental and simulation results show better heat insulation for aerogel coated wafers as compared with simulated micromachined wafers. The measurement and simulation results of aerogel coated wafers are in good agreement. The aerogel film can replace the air cavity created by conventional micromachining of wafers to save power and chip area in metal oxide gas sensor arrays.

Probabilistic gas quantification with MOX sensors in Open Sampling Systems—A Gaussian Process approach

Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations.

Detection of Smell Change of Flue-Cured Tobacco Based on an Electronic Nose

In order to make the flue-cured tobacco represent better aroma quality, an electronic nose with an array of metal-oxide gas (MOS) sensors are used to monitor the smell change during tobacco curing process. The design of dedicated electronic nose and experiment steps are described in detail. After signal preprocessing, the smell synthesized curve, of which four obvious peaks were detected, was obtained by the use of principal component analysis (PCA). The results indicate that, the peak time would be of important significance to the smell synthesized curve, which can clearly reflect the overall trend of smell during tobacco curing process. Therefore, back-propagation neural network (BPNN) is proposed to forecast peak time. This paper presents a study on smell change based on electronic nose during flue-cured tobacco process and detection of obvious peaks on the basis of sensor outputs. The law of smell changes obtained from analysis is in agreement with flue-cured tobacco theory.

Algorithmic mitigation of sensor failure: Is sensor replacement really necessary?

Data-driven models built on metal oxide (MOX) gas sensor arrays are broadly used to detect and discriminate a wide variety of chemical substances. Usually, a system composed of gas sensors and data processing algorithms is initially trained under controlled conditions with the aim to make accurate predictions of the new samples acquired. However, MOX gas sensors undergo two major impairments in the forms of sensor failures and drift that deteriorate the predictions’ capabilities of the previously calibrated models. While a variety of algorithms has been proposed to cope with the sensor drift problem, sensor failures have received much less attention despite their recurrent appearance after a certain time of operation. In this paper we propose a novel methodology based on multiple kernels to increase the robustness of e-nose systems against sensor failures. We built various multi-kernel models using multiple subsets of sensors and analyzed their performance when increasing the number of faulty sensors. Using an 8-sensor array module exposed to six different gases, we show that our proposed multi-kernel approach significantly increases the robustness of the system. In particular, we estimate that the percentage of multi-kernels free of faulty sensors has to be of at least 50% to maintain the performance of the classifier stable. The main conclusion drawn from this analysis is that instead of identifying which specific sensor fails, which is a nontrivial computational task and can be just temporal, the most convenient way would be to design a more robust system that requires minimal human intervention. Since the multi-kernel strategy copes with the sensor failure by itself, we claim that the lifetime of chemo-sensory systems, particularly their constituent chemical sensor devices, can be extended before the replacement of any particular chemical sensor is required.

An adaptive classification model based on the Artificial Immune System for chemical sensor drift mitigation

Abstract Multivariate statistics is the standard instrument to analyze the data of chemical sensor arrays. However, the non-reproducibility of sensors and in some cases the drift of signals make the prediction of multivariate classifiers rather uncertain. In recent years, adaptive classifiers that can follow the evolution of sensor signals have been introduced for many pattern recognition applications. The performance of these methods is sometimes strongly dependent on the frequency of class occurrence of the problem. In particular, the adaptation fails for those classes characterized by a scarce rate of occurrence. But for chemical sensor arrays the detection of such classes is often the goal of the application. A suggestion to overcome this problem is offered by a set of algorithm inspired by the natural immune system. In this paper a modified version of an Artificial Immune System algorithm is introduced. This algorithm can achieve a classification model that is substantially immune to the drift of the sensors. Noteworthy, the immunity is independent from the occurrence frequency of the classes of the problem. The algorithm properties are demonstrated with both synthetic and real data. In the case of real data a 5 classes problem with an array of metal-oxide gas sensors operated for 18 months has been considered.

Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors

We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.

Environmental monitoring with a multisensor platform on polyimide foil

A multisensor platform on plastic foil for environmental monitoring has been produced and its gas sensing performance, investigated. It is an array of conductometric metal-oxide (MOX) and capacitive polymer gas sensors integrated with a resistive platinum thermometer on a polyimide sheet substrate. The feasibility of simultaneous measurement of oxidizing and reducing gases, volatile organic compounds (VOCs), humidity and temperature has been demonstrated. MOX signals comparable with those of the devices realized on ceramic substrates have been obtained. Due to its structure, the platform is very versatile and, by using different sensor configurations and sensing materials, it allows the detection of a broad spectrum of gaseous analytes over wide concentration ranges. From the raw signals, temperature and humidity-corrected gas responses have been inferred which have been used for the calibration of the platform sensors. All the integrated devices were stable and gave reproducible signals for more than two months of operation, even when the MOXs ran continuously at 300 degrees C. The performed investigation proved the device concept viability and the reliability of its practical implementation. (C) 2012 Elsevier B.V. All rights reserved.

Bio-inspired solutions to the challenges of chemical sensing

Bio-inspired solutions to the challenges of chemical sensing

Employment of Electric Potential to Build a Gas-Selective Response of Metal Oxide Gas Sensor Array

This paper presents an approach to design multisensor microarrays for electronic nose instruments employing a metal oxide thin film whose spatial gas-sensitive properties are locally differentiated by electric potential. The measurement of local potential as a sensor signal over the metal oxide film serves to build a gas discriminating pattern which can be selectively identified by pattern recognition techniques. The gas sensitivity studies have revealed a higher gas response on the film areas, which are activated by positive potential, than that at earth potential. The gas-recognition capability of potential patterns is, at least, comparable with the one of conductance patterns recorded at the same microarrays differentiated by nonhomogeneous spatial heating. The experimental data are discussed in terms of oxide surface charging by adspecies coming through the ambient air. The suggested approach could be considered for designing reproducible multisensor systems on a single-chip platform.

A Method of Feature Extraction on Recovery Curves for Fast Recognition Application With Metal Oxide Gas Sensor Array

In the fast recognition applications of electronic nose, not only the recognition time is important, another parameter response-recovery time also needs to be considered. The response-recovery time could be defined as the time from the beginning of measuring one sample to the state of being ready for new sample measurement. An electronic nose with nine metal oxide (MOX) gas sensors and a method of feature extraction on sensor recovery curves were presented in this paper. The electronic nose was designed to reduce the recognition time and the response-recovery time synchronously. In the sampling module of the electronic nose, there were two pumps, which could let the sensor quickly recovered. The feature extraction method could rapidly extract features from sensor recovery curves with robust information. Nine volatile organic compounds (VOCs) gas samples were measured with the electronic nose. The correct recognition ratios under 10 and 15 s recognition time are 91.0% and 95.8%, respectively. The mean response-recovery time of these sensors in the measurements was 33.5 s, which was about 42.7% of the response-recovery time in typical traditional gas sample measurements. The results show that the proposed feature extraction method could extract robust information with short recognition time and response-recovery time.

An electronic nose system based on a micro-machined gas sensor array to assess the freshness of sardines

An electronic nose system based on a four-element, integrated, micro-machined, metal oxide gas sensor array is used to assess, in an objective manner, the evolutionary stages of freshness in sardine samples stored up to 1-week at 4 °C. The sensors developed were based on tin oxide doped with Pt or Pd or Bi, and on tungsten oxide doped with Au. The selection of the gas sensitive materials was based on a previous identification and quantification of characteristic compounds found in the headspace of sardines determined by solid phase micro-extraction gas chromatography coupled to mass spectrometry. Principal component analysis performed on the responses of the sensor array revealed that sardine samples could be classified in three freshness states. This was in good agreement with the results of a microbiological analysis. A support vector machine-based classifier reached a 100% success rate in the identification of sardine freshness. The stability of the electronic nose classification ability was assessed by correctly classifying measurement databases gathered 1-month apart. By building and validating quantitative partial least squares models, which employed as input data the gas sensor responses, it was possible to predict with good accuracy the total viable counts (TVC) of aerobic bacteria present in sardine samples. For the validation dataset, the correlation coefficient between actual and predicted TVC was 0.91, which indicates that the electronic nose system developed is a simple and rapid technique for evaluating sardine freshness.

Metal oxide gas sensor array for the detection of diesel fuel in engine oil

We developed a novel method to detect the presence of unburned diesel fuel in lubricating oil for internal combustion engine. The method is based on the use of an array of different gas microsensors based on metal oxide thin films deposited by sol–gel technique on Si substrates. The sensor array, exposed to the volatile chemical species of different diesel fuel engine oil samples contaminated in different percentages by diesel fuel, resulted to be appreciably sensitive to them. Principal component analysis (PCA) and self-organizing map (SOM) applied to the sensor response data set gave a first proof of the sensor array ability to discriminate among the different diesel fuel diluted lubricating oils. Moreover, in order to get information about the headspace composition of the diesel fuel-contaminated engine oils used for gas-sensing tests, we analysed the engine oil samples by static headspace solid phase micro-extraction/gas chromatograph/mass spectrometer (SHS-SPME/GC/MS).

CMOS-Based Monolithic Controllers for Smart Sensors Comprising Micromembranes and Microcantilevers

This paper presents design considerations and implementations of monolithic controllers for smart sensors that make use of micromachined structures such as micromembranes and microcantilevers. Five control techniques are reviewed, and the classic control technique is selected for the design of the controllers. The circuit implementations of an analog proportional controller and a digital proportional-integral-derivative controller are described in detail. The first controller is used for controlling the microhotplate (micromembrane) temperature of a monolithic metal-oxide gas sensor array. The second controller is used for controlling the microcantilever deflection of a monolithic atomic force microscope. The controllers and microsensors are fabricated in standard 0.8-mum CMOS technology combined with post-CMOS micromachining. Chemical measurements and contact-mode imaging are performed to confirm the excellent performance of these monolithic controllers

New normalization procedure to improve signal pattern discrimination power in case of different concentration dependencies of the gas sensors in an array

Sample recognition with a metal oxide gas sensor array is often affected by a signal pattern dependence on the gas concentration, that blurs the class representation. The reason are differences of the sensor elements in the exponent of the power law that governs the concentration dependence of signals of the array elements. Under the power law the standard pattern normalization procedure using signal ratios eliminates the concentration dependence only if the two signals have the same power law exponent. A simple signal preprocessing algorithm is presented, which is able to suppress the differences in the non-linear concentration dependence of the array elements whereby concentration independent signal patterns can be obtained. Thereby the resolution of the pattern discrimination can be significantly improved resulting in improved gas recognition appropriate for a broad concentration range.

Coupling fast variable selection methods to neural network-based classifiers: Application to multisensor systems

We address the problem of variable selection within the pattern recognition engine of multisensor systems. This problem arises when there is high dimensionality in the data used for training (e.g. because a high number of sensors are used, or because many features are extracted from the response of each sensor, or both). Different variable selection techniques (including deterministic and stochastic methods) have been coupled with neural network-based classifiers. The usefulness of each technique implemented is benchmarked by evaluating its performance in terms of three objective parameters: the success rate in classification, the dimensionality of the final set of variables used for training and the time needed to complete the variable selection procedure. The database consisted of 96 measurements of ammonia, acetone and o-xylene vapours and their binary mixtures gathered using a metal oxide gas sensor array. Each measurement was described by 120 variables (12 sensors and 10 parameters each). A new strategy for variable selection, which is based on a two-step approach, is introduced that leads to the building of parsimonious classification models based on either the fuzzy ARTMAP or the probabilistic neural networks. For example, a 91.66% success rate in the simultaneous identification and quantification of the species and their mixtures was obtained using nine input variables only (out of the 120 available). This process of variable selection was conducted in two-step, i.e. a coarse selection based on a variance criterion followed by a simulated annealing process. This two-step variable selection took about 10 min to complete in a Pentium 4 PC platform.

Polyimide membrane for micro-heated gas sensor array

Presented here is the fabrication of a polyimide (PI) membrane for potential use as a low loss microheated metal oxide (MOS) gas sensor array. Such membranes are often made from dielectric layers of silicon nitride or silicon oxide on bulk etched silicon 〈100〉 to fabricate low power sensors. Due to the fragility of these types of membranes, the yield is not good and the dielectric thickness and active area is limited. The area of PI membrane is not limited by the fragility of the material due to its rugged nature. The thickness of polyimide membrane used is in the region of 1–10μm. The elastic modulus of the material enables it to be used in any number of applications ranging from heated sensor support to a microwave antenna array in the communication field. The application described here uses the thin rugged polyimide membrane to reduce the power consumption of a metal oxide gas sensor. Along with the fabrication of the polyimide membrane, an array of micro heaters is integrated on top of the membrane. A sputtered platinum (Pt) meander is resistively used to heat up the sensing layer to its elevated temperature of up to 350°C.

Establishing the limit of detection and the resolution limits of odorous sources in the environment for an array of metal oxide gas sensors

Odour identification with gas sensor arrays relies on two main parameters: the limit of detection, defined as the concentration of the analyte corresponding to the lowest detectable signal for the sensors, and the limit of resolution, defined for pair-wise comparisons of vapours, as the concentration levels below which a vapour can no longer be reliably recognised from another one on the basis of its response pattern. For real-life environmental odours, however, it is no longer possible to express such limits in “concentration” terms, because they are complex gas mixtures composed of hundreds of various compounds. The paper tries to assess both limits for odorous sources in the environment firstly by finding an equivalence of the odour concentration, expressed in odorous unit per cubic meter (ou/m 3), and the concentration of a reference gas, here the ethanol, and secondly by working with a statistical metric, based on the magnitude and standard deviations along linear projections of clustered response data. The methodology is illustrated for a metal oxide gas sensor array and the odour generated by urban waste composting facilities, which is compared to the background air and to ethanol vapour.

Building parsimonious fuzzy ARTMAP models by variable selection with a cascaded genetic algorithm: application to multisensor systems for gas analysis

Considerable attention has been given to strategies for variable selection in gas analysis using multisensor systems. Of the stochastic methods, genetic algorithms (GAs) have been found to be useful for variable selection, although they do not prevent irrelevant variables from being selected. Here we introduce the cascaded genetic algorithm, a new and simple approach that removes initially selected irrelevant variables. We coupled the method to the fuzzy ARTMAP classifier and used it to analyze single vapors and binary mixtures of three volatile organic compounds using a 12-element metal oxide gas sensor array. The method allowed to reduce the number of variables used to build the classifiers from 120 down to nine, which significantly increased the generalization ability of the fuzzy ARTMAP classifier. Success rates of 91.67 and 88.33% were reached in the simultaneous identification, quantification of single vapors, and in the identification of single vapors and their binary mixtures, respectively.