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.
Bibliographical noteAuthor can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 26/12/09]
Scott, S. M., James, D., Ali, Z., O'hare, W. T., & Rowell, F. J. (2003). Total luminescence spectroscopy with pattern recognition for classification of edible oils. The Analyst, 128(7), 966-973. https://doi.org/10.1039/b303009a