Conventional approaches to chemical sensors have
traditionally made use of a “lock-and-key” design,
wherein a specific receptor is synthesized in order to
strongly and highly selectively bind the analyte of
interest.1-6 A related approach involves exploiting a
general physicochemical effect selectively toward a
single analyte, such as the use of the ionic effect in
the construction of a pH electrode. In the first
approach, selectivity is achieved through recognition
of the analyte at the receptor site, and in the second,
selectivity is achieved through the transduction
process in which the method of detection dictates
which species are sensed. Such approaches are appropriate
when a specific target compound is to be
identified in the presence of controlled backgrounds
and interferences. However, this type of approach
requires the synthesis of a separate, highly selective
sensor for each analyte to be detected. In addition,
this type of approach is not particularly useful for
analyzing, classifying, or assigning human value
judgments to the composition of complex vapor
mixtures such as perfumes, beers, foods, mixtures of
solvents, etc.

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Cross-reactive chemical sensor arrays.

This paper reviews the range of sensors used in electronic nose (e‐nose) systems to date. It outlines the operating principles and fabrication methods of each sensor type as well as the applications in which the different sensors have been utilised. It also outlines the advantages and disadvantages of each sensor for application in a cost‐effective low‐power handheld e‐nose system.

A review of gas sensors employed in electronic nose applications

Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.

Pattern analysis for machine olfaction: a review

Sensor drift remains to be the most challenging problem in chemical sensing. To address this problem we have collected an extensive dataset for six different volatile organic compounds over a period of three years under tightly controlled operating conditions using an array of 16 metal-oxide gas sensors. The recordings were made using the same sensor array and a robust gas delivery system. To the best of our knowledge, this is one of the most comprehensive datasets available for the design and development of drift compensation methods, which is freely reachable on-line. We introduced a machine learning approach, namely an ensemble of classifiers, to solve a gas discrimination problem over extended periods of time with high accuracy rates. Experiments clearly indicate the presence of drift in the sensors during the period of three years and that it degrades the performance of the classifiers. Our proposed ensemble method based on support vector machines uses a weighted combination of classifiers trained at different points of time. As our experimental results illustrate, the ensemble of classifiers is able to cope well with sensor drift and performs better than the baseline competing methods.

Chemical gas sensor drift compensation using classifier ensembles

Computational methods for the analysis of chemical sensor array data from volatile analytes.

Drift is one of the most serious impairments afflicting gas sensors. It can be seen as a gradual change in the sensor response over a long period of time when the external conditions an constant. T ...

Drift correction for gas sensors using multivariate methods

Selection of variables for interpreting multivariate gas sensor data

In this study a sensor array and pattern recognition routines (an electronic nose) were used to monitor a sausage fermentation in order to follow the changes in emitted volatile compounds during the fermentation process and to compare the electronic nose results with a sensory analysis From the sensor array responses the fermentation time could be predicted using different methods, where principal component regression and an artificial neural network based on all sensors in the electronic nose performed best A sensory panel evaluated the final product and these results were compared with the electronic nose measurements in the early stage of the process and on the final sausages A principal component analysis showed that one of the sausage batches clearly deviated from the other using both the sensory panel data and the electronic nose responses The deviating batch was different already after 4 h and the difference was consistent during the process

Monitoring sausage fermentation using an electronic nose

Enhanced selectivity of MOSFET gas sensors by systematical analysis of transient parameters

A distributed chemical sensor system consists of a flow cell with a catalyst and a number of gas sensors mounted along the flow direction in the proximity of the catalyst. In such a system, the catalytic combustion is independent of the chemical sensing. The reaction rate on the catalyst varies for different molecules, which might be utilized for increasing the information about the gas mixture. In this work, the properties of such a system were studied, especially regarding the possibility of gas identification/quantification. It was shown that different molecules gave different response patterns from the sensors in the cell. The components in a binary mixture, hydrogen, and ethanol, could be quantified applying the sensor responses from the distributed chemical sensor system as inputs to an artificial neural network. It was thus found that by employing a number of gas sensors in combination with a catalytic surface, the ability to discriminate between different molecules could be increased.

Tomas Eklöv

Papers

Drift correction for gas sensors using multivariate methods

Selection of variables for interpreting multivariate gas sensor data

Monitoring sausage fermentation using an electronic nose

Enhanced selectivity of MOSFET gas sensors by systematical analysis of transient parameters

Gas mixture analysis using a distributed chemical sensor system