Total luminescence spectroscopy with pattern recognition for classification of edible oils

Simon M. Scott, David James, Zulfiqur Ali, William T. O'hare, Fred. J. Rowell

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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.
Original languageEnglish
Pages (from-to)966-973
JournalThe Analyst
Volume128
Issue number7
DOIs
Publication statusPublished - 1 May 2003

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Author can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 26/12/09]

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