Abstract
ABSTRACT The genera Nigella and Garidella are two members of the Nigelleae tribe. Among all the taxa of the tribe, black cumin (Nigella sativa L.) is one of the most important plants concerning economic and medicinal uses. In this study, volatiles were analyzed in detail to elucidate the phytochemical profiles and to understand the taxonomic patterns of secondary metabolites for 19 taxa of Nigella and Garidella collected from Turkey and the neighboring countries. Volatiles were analyzed by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC/MS) using different polarity columns. Seventeen Nigella and two Garidella taxa were investigated, with 130 volatile oil components characterized. Structural similarities of the compounds were clustered by a machine learning algorithm using the molecular fingerprinting method. Our results depict that the phytochemicals produced by Nigelleae can be classified independently of their chemical families and significantly contribute to genera delimitation by their structural distances.
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