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

Hdl Handle:
http://hdl.handle.net/10149/58383
Title:
Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition
Authors:
James, D. (David); Scott, S. M. (Simon); O'Hare, W. T. (Liam); Ali, Z. (Zulfiqur); Rowell, F. J. (Frederick)
Affiliation:
University of Teesside. School of Science and Technology; University of Sunderland. School of Health, Natural and Social Sciences.
Citation:
James, D. et al. (2004) 'Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition', Transactions of the Institute of Measurement and Control, 26 (1), pp.3-18.
Publisher:
Sage
Journal:
Transactions of the Institute of Measurement and Control
Issue Date:
2004
URI:
http://hdl.handle.net/10149/58383
DOI:
10.1191/0142331204tm0102oa
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 classification 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.
Type:
Article
Keywords:
edible oils; electronic nose; fuzzy c-means; piezoelectric quartz; radial basis function
ISSN:
0142-3312
Rights:
Subject to restrictions, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 1/12/09]
Citation Count:
5 [Scopus, 1/12/2009]

Full metadata record

DC FieldValue Language
dc.contributor.authorJames, D. (David)-
dc.contributor.authorScott, S. M. (Simon)-
dc.contributor.authorO'Hare, W. T. (Liam)-
dc.contributor.authorAli, Z. (Zulfiqur)-
dc.contributor.authorRowell, F. J. (Frederick)-
dc.date.accessioned2009-04-01T10:50:39Z-
dc.date.available2009-04-01T10:50:39Z-
dc.date.issued2004-
dc.identifier.citationTransactions of the Institute of Measurement and Control; 26 (1): 3-18-
dc.identifier.issn0142-3312-
dc.identifier.doi10.1191/0142331204tm0102oa-
dc.identifier.urihttp://hdl.handle.net/10149/58383-
dc.description.abstractAn 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 classification 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.-
dc.publisherSage-
dc.rightsSubject to restrictions, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 1/12/09]-
dc.subjectedible oils-
dc.subjectelectronic nose-
dc.subjectfuzzy c-means-
dc.subjectpiezoelectric quartz-
dc.subjectradial basis function-
dc.titleClassification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition-
dc.typeArticle-
dc.contributor.departmentUniversity of Teesside. School of Science and Technology; University of Sunderland. School of Health, Natural and Social Sciences.-
dc.identifier.journalTransactions of the Institute of Measurement and Control-
ref.assessmentRAE 2008-
ref.citationcount5 [Scopus, 1/12/2009]-
or.citation.harvardJames, D. et al. (2004) 'Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition', Transactions of the Institute of Measurement and Control, 26 (1), pp.3-18.-
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