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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机译：总发光光谱与模式识别相结合已被用于区分四种不同类型的食用油，即特级初榨橄榄油（EVO），非初榨橄榄油（NVO），向日葵（SF）和菜籽油（RS）。简化的模糊自适应共振理论映射（SFAM），传统的反向传播（BP）和径向基函数（RBF）神经网络为120个样本提供了100％的分类，发现SFAM是最有效的。调查范围扩大到EVO中v / v SF或RS百分比从5％到90％的掺假，共产生480个样品。发现SFAM在掺假水平分类方面比RBF和BP更准确。 SFAM的所有错误分类均发生在5％v / v的水平，导致正确分类的油样品总数达到99.375％。掺假百分比可以通过RBF网络（2.435％RMSE）或EVO掺假中v / v RS的主成分分析（PCA）分数的简单欧氏距离关系（2.977％RMSE）来描述。
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