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Issue 7, 2003
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Total luminescence spectroscopy with pattern recognition for classification of edible oils

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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.

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Publication details

The article was received on 17 Mar 2003, accepted on 23 Apr 2003 and first published on 22 May 2003

Article type: Paper
DOI: 10.1039/B303009A
Citation: Analyst, 2003,128, 966-973

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    Total luminescence spectroscopy with pattern recognition for classification of edible oils

    S. M. Scott, D. James, Z. Ali, W. T. O'Hare and Fred. J. Rowell, Analyst, 2003, 128, 966
    DOI: 10.1039/B303009A

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