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

被引：0
作者：
Z. Ali
D. James
W. T. O'Hare
F. J. Rowell
S. M. Scott
机构：
[1] University of Teesside,School of Science and Technology
[2] University of Sunderland,School of Sciences
[3] Sunderland Tyne and Wear,undefined
来源：
Journal of Thermal Analysis and Calorimetry | 2003年 / 71卷
关键词：
edible oils; electronic nose; neural network; piezoelectric quartz; radial basis function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：147 / 154
页数：7
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