Total luminescence spectroscopy with pattern recognition for classification of edible oils

• Published in: Analyst (London) 2003-07, Vol.128 (7), p.966-973
• Main Authors: SCOTT, Simon M, JAMES, David, ALI, Zulfiqur, O'HARE, William T, ROWELL, Fred J
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Biological and medical sciences; Chemistry; Computational Biology - methods; Exact sciences and technology; Fat industries; Food industries; Fundamental and applied biological sciences. Psychology; Fuzzy Logic; Pattern Recognition, Automated; Plant Oils - classification; Spectrometric and optical methods; Spectrometry, Fluorescence - methods
• ISSN: 0003-2654; 1364-5528
• DOI: 10.1039/b303009a

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.