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Artificial intelligence/fuzzy logic method for analysis of combined signals from heavy metal chemical sensors

  • The cross-sensitivity of chemical sensors for several metal ions resembles in a way the overlapping sensitivity of some biological sensors, like the optical colour receptors of human retinal cone cells. While it is difficult to assign crisp classification values to measurands based on complex overlapping sensory signals, fuzzy logic offers a possibility to mathematically model such systems. Current work goes into the direction of mixed heavy metal solutions and the combination of fuzzy logic with heavy metal-sensitive, silicon-based chemical sensors for training scenarios of arbitrary sensor/probe combinations in terms of an electronic tongue. Heavy metals play an important role in environmental analysis. As trace elements as well as water impurities released from industrial processes they occur in the environment. In this work, the development of a new fuzzy logic method based on potentiometric measurements performed with three different miniaturised chalcogenide glass sensors in different heavy metal solutions will be presented. The critical validation of the developed fuzzy logic program will be demonstrated by means of measurements in unknown single- and multi-component heavy metal solutions. Limitations of this program and a comparison between calculated and expected values in terms of analyte composition and heavy metal ion concentration will be shown and discussed.

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Document Type:Conference Object
Author:M. Turek, W. Heiden, A. Riesen, T. A. Chhabda, J. Schubert, W. Zander, P. Krüger, M. Keusgen, M. J. Schoening
Parent Title (English):Electrochimica Acta
First Page:6082
Last Page:6088
Date of first publication:2009/03/24
Papers from the 7th International Symposium (EMNT 2008) 15-18 September 2008, Ein-Gedi, Israel
Tag:Chalcogenide glass sensor; Cross-sensitivity; Electronic tongue; Fuzzy logic; Multi-component heavy metal solution
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Institut für funktionale Gen-Analytik (IfGA)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/04/02