Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

György Barkó, József Hlavay

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η = 0.15), momentum term (μ = 0.9), and the sigmoid parameter (β = 1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.

Original languageEnglish
Pages (from-to)2237-2245
Number of pages9
JournalTalanta
Volume44
Issue number12
DOIs
Publication statusPublished - Dec 1 1997

Keywords

  • Artificial neural network
  • Identification of organic compounds
  • Piezoelectric sensor array

ASJC Scopus subject areas

  • Analytical Chemistry

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