Abstract
Artificial neural networks (ANNs) were built with excitation-emission matrix fluorescence (EEMF) spectra of essential oils for the investigation of their adulteration. With self-organized maps (SOMs), the clusters formed by all the types of essential oils were visualized. Pure essential oils were globally separated from their adulterated samples. The nature of the adulterant (vegetable oil, essential oil, solvent) in adulterated essential oils was revealed by a multilayer perceptron (MLP) network which classified them with a percentage of correct classification of 92.31%. In the case of the adulteration of neroli essential oil by sunflower vegetable oil, with another multilayer perceptron network, the level of adulteration was globally well evaluated. The correlation coefficient between true and evaluated adulteration percentages was 0.951. The samples corresponding to the adulteration percentage of 5% were the worse evaluated.
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