A multisensor system for beer flavour monitoring using an array of conducting polymers and predictive classifiers

Gardner, Julian W.; Pearce, Timothy Charles; Friel, Sharon; Bartlett, Philip N.; Blair, Neil

doi:10.1016/0925-4005(94)87089-6

Cited by 86 publications

(40 citation statements)

References 2 publications

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“…Examples include the discrimination of (1) coffees [9], (2) tainted water [10], (3) alcohols, tobacco blends, and beverages [11], (4) banana ripeness [12], (5) types of beer [13,14], and (6) paper quality [15]. In combination with the University of Neuchâtel, the Warwick group has also made strides toward reducing power in these arrays to 30 mW per sensor and enhancing the ability to maintain stable operating temperature, both important steps toward microarray implementations of these same arrays [16].…”

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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“…However, in every case the correct There are three presentations each of three samples: row 1, 87 octane gasoline (g87 1 , g87 2 , g87 3 ); row 2, paint thinner (pth 1 , pth 2 , pth 3 ); row 3, xylene (xyl 1 , xyl 2 , xyl 3 ). In each subplot, the horizontal axis is the sensor number (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). The vertical axis is time, running from t = 0 at the top to t = 51 sample intervals at the bottom.…”

Section: Example: Nonlinear Signal Processing Using Recurrentmentioning

confidence: 99%

“…Hardware-based signal processing directed at signal acquisition has often been catalyzed by limitations in existing data acquisition equipment rather than by fundamental limitations in the scanning process itself. For example, PCB modules are used in an array of 19 conducting polymer films to monitor temperature and humidity in the immediate sensing environment, convert the sensor signals to their digital equivalents, and multiplex these signals into the next stage of computation [1]. However, outside of the research environment, these PCB modules would require limited customization in the product development stage.…”

Section: Hardware-based Signal Processing Architecturesmentioning

confidence: 99%

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Signal Processing Architectures for Chemical Sensing Microsystems

Wilson

Roppel

2002

Sensors Update

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This paper reviews several levels of signal processing and associated architectures for chemical sensing microsystems that use either arrays of optical or of physical sensors. Many chemical sensors, because of their interaction and vulnerability to the environment, have been eliminated from inclusion in sensing systems that require high precision and accuracy. This discussion evaluates parametric vs. nonparametric techniques and linear vs. nonlinear signal processing approaches for addressing chemical classification problems using imperfect sensing technologies. Hardware implementations of signal processing and biologically inspired signal processing are also reviewed. Future research into the development of more accurate chemical classification systems demands the customization of current approaches, so that underlying principles of chemical sensors and associated interfering influences do not overburden the computational space, thereby allowing higher accuracy rates.

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“…Response of a QCM sensor to exposition in analyzed atmosphere may be represented by сontinuous monotonic or nonmonotonic (depending on the sample preparation technique) function of time. In the case of monotonic function asymptotically approaching the steady state different characteristic parameters of the curve may be taken as single sensor response measure, e. g. maximum frequency shift [9], maximum value and the slopes of ascending and descending parts of the curve [10], integral value over the specifically chosen interval (so called iV-parameter) [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Linear Classifiers for Analysis of the Sensor Arrays Descriptiveness for Detection of the Volatile Compounds Molecules

Pavluchenko¹,

Kazantseva²,

Koshets³

et al. 2014

SEMST

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In this work some approaches to improvement of the chemical images classification performance due to preprocessing and dimensionality reduction of the multisensor array responses are studied. A criterion for linear separability of the analyte classes is proposed, allowing to choose an optimal method of responses processing for the particular classification task without the need of cross-validation involving multiple classifier retraining.

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A multisensor system for beer flavour monitoring using an array of conducting polymers and predictive classifiers

Cited by 86 publications

References 2 publications

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

Signal Processing Architectures for Chemical Sensing Microsystems

Application of Linear Classifiers for Analysis of the Sensor Arrays Descriptiveness for Detection of the Volatile Compounds Molecules

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