Rapid static headspace sampler for automated odour analysis

Craven, Mark A.; Gardner, Julian W.

doi:10.1177/014233129802000203

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Efforts to provide sufficient variability in sensor technology and operation for proper array optimization include the modeling of reactions in the most popular chemiresistive sensor, the tin oxide sensor [1], manipulation of such design parameters as electrode geometry (in phthalocyanine [2] and metal oxide thin films [3]), use of multiple modes of operation for each sensor to minimize the physical size of a heterogeneous array architecture [4], the use of molecular sieves at the front end of the sensor array to provide prefiltering (by size) of molecules of interest [5], and the development of improved supporting infrastructure in the form of improved headspace samplers [6]. Various (heterogeneous) combinations of metal oxide and conducting polymer sensors have been used to differentiate single analytes such as toluene, n-propanol, n-octane, methanol, ethanol, 2-propanol and 1-butanol [8].…”

Section: Use Of Chemical Sensor Arraysmentioning

confidence: 99%

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

et al. 2002

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Physical sensors, defined by their direct chemical interaction with the sensing environment, are a valuable and often essential contribution to the solution of stringent chemical sensing problems. Methods for conditioning signals from these sensors to optimize their presentation to subsequent decision-making models in the signal processing flow are presented. The assembly of sensors into an array and the preprocessing of these signals are the two primary techniques for conditioning a chemical image for concentration detection, chemical discrimination, and, in some cases, odor localization. Array optimization involves locating the point at which adding additional sensors to an array generates more noise than information and is highly dependent on the application and number of analytes to be sensed or differentiated. Signal preprocessing techniques include noise reduction, feature extraction, the reduction of array inputs, and the scaling of individual sensor signals and provide a means by which to reconstruct an array of chemical sensor signals into a subset of information that enables a decisionmaking model to do its job with greater efficiency and accuracy. In this chapter, surveys of methods are accompanied by representative examples employing a wide variety of sensors including ChemFETs, chemiresistors, acoustic wave devices, and surface plasmon resonance sensors.

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Section: Use Of Chemical Sensor Arraysmentioning

confidence: 99%

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

et al. 2002

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“…Even so, the prediction accuracy would be improved by a few percent with either better temperature control of the sensor chamber (current systems now employ closed-loop control to f O . 1 "C [14]) or parametric compensation via an input to the neural network.…”

Section: Discussionmentioning

confidence: 99%

Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors

Gardner

Hines

Molinier

et al. 1999

IEE Proc., Sci. Meas. Technol.

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The authors report on the use of a sampling devitz to collect the breath from individual members of a herd of dairy cattle during a two-week period. The response of an array of six semiconducting oxide gas sensors to the breath samples has been recorded and subsequently modelled by a time-dependent, linear, second-order system. Four characteristic sensor parameters have been estimated using a neural network, and these parameters have been used to train a predictive multilayer perceptron network. The results show that either a static response parameter (based on the difference in the signal from zero time) or a single time constant can be used to predict reasonably well the health of the cow as judged against blood samples. In both cases, the identification rate of unknown samples being about 76%. Further improvements may be possible through the use of network compensation of variations in sample temperature and humidity.

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Rapid static headspace sampler for automated odour analysis

Cited by 2 publications

References 3 publications

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors

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