Jump to main content
Jump to site search

Issue 7, 2003
Previous Article Next Article

Total luminescence spectroscopy with pattern recognition for classification of edible oils

Author affiliations

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.

Back to tab navigation

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
  •   Request permissions

    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

Search articles by author

Spotlight

Advertisements