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

• Published in: Transactions of the Institute of Measurement and Control 2004-01, Vol.26 (1), p.3-18
• Main Authors: James, D., Scott, S. M., O’Hare, W. T., Ali, Z., Rowell, F. J.
• Format: Article
• Language: English
• Subjects: Helianthus; Olea
• ISSN: 0142-3312; 1477-0369
• DOI: 10.1191/0142331204tm0102oa

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.