Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition

D. James, Simon Scott, W. T. O'Hare, Z. Ali, F. J. Rowell

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
93 Downloads (Pure)

Abstract

An electronic nose based on an array of six bulk acoustic wave polymer-coated piezoelectric quartz (PZQ) sensors with soft computing-based pattern recognition was used for the classi-fication of edible oils. The electronic nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), nonvirgin olive oil (NVO) and sunflower oil (SFO) were used over a period of 30 days. The sensor responses were visualized by plotting the results from principal component analysis (PCA). Classification of edible oils was carried out using fuzzy c-means as well as radial basis function (RBF) neural networks both from a raw data and data after having been preprocessed by fuzzy c-means. The fuzzy c-means results were poor (74%) due to the different cluster sizes. The result of RBF with fuzzy c-means preprocessing was 95% and 99% for raw data input. RBF networks with fuzzy c-means preprocessing provide the advantage of a simple architecture that is quicker to train.

Original languageEnglish
Pages (from-to)3-18
Number of pages16
JournalTransactions of the Institute of Measurement & Control
Volume26
Issue number1
DOIs
Publication statusPublished - 1 Jan 2004

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