Total luminescence spectroscopy with pattern recognition for classification of edible oils

Hdl Handle:
http://hdl.handle.net/10149/58360
Title:
Total luminescence spectroscopy with pattern recognition for classification of edible oils
Authors:
Scott, S. M. (Simon); James, D. (David); Ali, Z. (Zulfiqur); O'Hare, W. T. (Liam); Rowell, F. J. (Frederick)
Affiliation:
University of Teesside. School of Science and Technology; University of Sunderland. School of Health, Natural and Social Sciences.
Citation:
Scott, S. M. et al. (2003) 'Total luminescence spectroscopy with pattern recognition for classification of edible oils', The Analyst, 128 (7), pp.966-973.
Publisher:
Royal Society of Chemistry
Journal:
The Analyst
Issue Date:
May-2003
URI:
http://hdl.handle.net/10149/58360
DOI:
10.1039/b303009a
Abstract:
Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.
Type:
Article
Keywords:
total luminescence spectroscopy; edible oils; extra virgin olive; EVO; non-virgin olive; NVO; sunflower; rapeseed; simplified fuzzy adaptive resonance theory mapping; traditional back propagation; radial basis function
ISSN:
1364-5528
Rights:
Author can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 26/12/09]
Citation Count:
11 [Scopus, 18/12/09]

Full metadata record

DC FieldValue Language
dc.contributor.authorScott, S. M. (Simon)-
dc.contributor.authorJames, D. (David)-
dc.contributor.authorAli, Z. (Zulfiqur)-
dc.contributor.authorO'Hare, W. T. (Liam)-
dc.contributor.authorRowell, F. J. (Frederick)-
dc.date.accessioned2009-04-01T10:50:02Z-
dc.date.available2009-04-01T10:50:02Z-
dc.date.issued2003-05-
dc.identifier.citationThe Analyst; 128 (7): 966-973-
dc.identifier.issn1364-5528-
dc.identifier.doi10.1039/b303009a-
dc.identifier.urihttp://hdl.handle.net/10149/58360-
dc.description.abstractTotal luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.-
dc.publisherRoyal Society of Chemistry-
dc.rightsAuthor can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 26/12/09]-
dc.subjecttotal luminescence spectroscopy-
dc.subjectedible oils-
dc.subjectextra virgin olive-
dc.subjectEVO-
dc.subjectnon-virgin olive-
dc.subjectNVO-
dc.subjectsunflower-
dc.subjectrapeseed-
dc.subjectsimplified fuzzy adaptive resonance theory mapping-
dc.subjecttraditional back propagation-
dc.subjectradial basis function-
dc.titleTotal luminescence spectroscopy with pattern recognition for classification of edible oils-
dc.typeArticle-
dc.contributor.departmentUniversity of Teesside. School of Science and Technology; University of Sunderland. School of Health, Natural and Social Sciences.-
dc.identifier.journalThe Analyst-
ref.assessmentRAE 2008-
ref.citationcount11 [Scopus, 18/12/09]-
or.citation.harvardScott, S. M. et al. (2003) 'Total luminescence spectroscopy with pattern recognition for classification of edible oils', The Analyst, 128 (7), pp.966-973.-
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