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Journal of Thermal Analysis and Calorimetry

, Volume 71, Issue 1, pp 147–154 | Cite as

Radial basis neural network for the classification of fresh edible oils using an electronic nose

  • Z. AliEmail author
  • D. James
  • W. T. O'Hare
  • F. J. Rowell
  • S. M. Scott
Article

Abstract

An electronic nose utilising an array of six-bulk acoustic wave polymer coated Piezoelectric Quartz (PZQ) sensors has been developed. The nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), Non-virgin olive oil (OI) and Sunflower oil (SFO), were used over a period of 30 days. The sensor responses were then analysed producing an architecture for the Radial Basis Function Artificial Neural Network (RBF). It was found that the RBF results were excellent, giving classifications of above 99% for the vegetable oil test samples.

edible oils electronic nose neural network piezoelectric quartz radial basis function 

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Copyright information

© Kluwer Academic Publishers/Akadémiai Kiadó 2003

Authors and Affiliations

  • Z. Ali
    • 1
    Email author
  • D. James
    • 1
  • W. T. O'Hare
    • 1
  • F. J. Rowell
    • 2
  • S. M. Scott
    • 2
  1. 1.School of Science and TechnologyUniversity of TeessideMiddlesbrough
  2. 2.School of SciencesUniversity of Sunderland, Sunderland Tyne and WearUK

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