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

Hdl Handle:
http://hdl.handle.net/10149/97986
Title:
Radial basis neural network for the classification of fresh edible oils using an electronic nose
Authors:
Ali, Z. (Zulfiqur); Scott, S. M. (Simon); James, D. (David); O'Hare, W. T. (Liam); Rowell, F. J. (Frederick)
Affiliation:
University of Teesside. School of Science and Technology.
Citation:
Ali, Z. et. al. (2003) 'Radial basis neural network for the classification of fresh edible oils using an electronic nose', Journal of Thermal Analysis and Calorimetry, 71 (1), pp.147-154.
Publisher:
Springer Verlag
Journal:
Journal of Thermal Analysis and Calorimetry
Conference:
29th International Vacuum Microbalance Techniques Conference, University of Teesside, September 5-7 2001
Issue Date:
Jan-2003
URI:
http://hdl.handle.net/10149/97986
DOI:
10.1023/A:1022222402328
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.
Type:
Article
Language:
en
Keywords:
edible oils; electronic nose; neural network; piezoelectric quartz; radial basis function
ISSN:
1388-6150; 1572-8943
Rights:
Author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 05/05/2010]
Citation Count:
0 [Web of Science and Scopus, 05/05/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorAli, Z. (Zulfiqur)en
dc.contributor.authorScott, S. M. (Simon)en
dc.contributor.authorJames, D. (David)en
dc.contributor.authorO'Hare, W. T. (Liam)en
dc.contributor.authorRowell, F. J. (Frederick)en
dc.date.accessioned2010-05-05T15:30:31Z-
dc.date.available2010-05-05T15:30:31Z-
dc.date.issued2003-01-
dc.identifier.citationJournal of Thermal Analysis and Calorimetry; 71(1):147-154en
dc.identifier.issn1388-6150-
dc.identifier.issn1572-8943-
dc.identifier.doi10.1023/A:1022222402328-
dc.identifier.urihttp://hdl.handle.net/10149/97986-
dc.description.abstractAn 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.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.rightsAuthor can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 05/05/2010]en
dc.subjectedible oilsen
dc.subjectelectronic noseen
dc.subjectneural networken
dc.subjectpiezoelectric quartzen
dc.subjectradial basis functionen
dc.titleRadial basis neural network for the classification of fresh edible oils using an electronic noseen
dc.typeArticleen
dc.contributor.departmentUniversity of Teesside. School of Science and Technology.en
dc.identifier.journalJournal of Thermal Analysis and Calorimetryen
dc.identifier.conference29th International Vacuum Microbalance Techniques Conference, University of Teesside, September 5-7 2001-
ref.citationcount0 [Web of Science and Scopus, 05/05/2010]en
or.citation.harvardAli, Z. et. al. (2003) 'Radial basis neural network for the classification of fresh edible oils using an electronic nose', Journal of Thermal Analysis and Calorimetry, 71 (1), pp.147-154.-
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