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.
|Journal||Journal of Thermal Analysis and Calorimetry|
|Publication status||Published - Jan 2003|
|Event||29th International Vacuum Microbalance Techniques Conference - Teesside University, Middlesbrough, United Kingdom|
Duration: 5 Sep 2001 → 7 Sep 2001
Conference number: 29
Ali, Z., James, D., O'Hare, W. T. L., Rowell, F. J., & Scott, S. M. (2003). Radial basis neural network for the classification of fresh edible oils using an electronic nose. Journal of Thermal Analysis and Calorimetry, 71(1), 147-154. https://doi.org/10.1023/A:1022222402328