Fluorescence Spectroscopy Combined with Chemometrics for the Investigation of the Adulteration of Essential Oils
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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.
KeywordsEssential oils Adulteration Excitation-emission matrix fluorescence Artificial neural networks
The authors gratefully acknowledge support of this work, through NET-45 and OEA-AC-71 projects, by the Abdus Salam International Centre for Theoretical Physics (ICTP, Trieste, Italy). The authors also thank Professor Souad Lahmar for providing the best conditions for the accomplishment of this work.
Compliance with Ethical Standards
Conflict of Interest
William Mbogning Feudjio declares that he has no conflict of interest. Hassen Ghalila declares that he has no conflict of interest. Mama Nsangou declares that he has no conflict of interest. Youssef Majdi declares that he has no conflict of interest. Yvon Mbesse Kongbonga declares that he has no conflict of interest. Nejmeddine Jaïdane declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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