title: Total luminescence spectroscopy with pattern recognition for classification of edible oils.
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.
authors.affiliation: School of Science and Technology, University of Teesside, Middlesbrough, UK TS1 3BA.
authors.forename: Simon M
authors.initials: SM
authors.lastname: Scott
authors.forename: David
authors.initials: D
authors.lastname: James
authors.forename: Zulfiqur
authors.initials: Z
authors.lastname: Ali
authors.forename: William T O
authors.initials: WT
authors.lastname: O'Hare
authors.forename: Fred J
authors.initials: FJ
authors.lastname: Rowell
mesh_terms: D019295:Computational Biology / Q000379:methods; D017143:Fuzzy Logic; D010363:Pattern Recognition, Automated; D010938:Plant Oils / Q000145:classification*; D013050:Spectrometry, Fluorescence / Q000379:methods
chemical_list: D010938:Plant Oils