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.
Acknowledgments
This study was supported by TUBİTAK (Project no. 107 T 6862). The author thanks TÜBİTAK for the financial support and the various individuals for their assistance throughout this project. We would like to thank Fatima Sales Professor (COI-Portugal), Theophanis Constantinidis and Theophanis Karamplianis (Greece), Ayşe Cantaş and Meltem Yorgancı (Turkish Republic of Northern Cyprus), Basam Alsamman (Syria), Magda Bou Dagher Kharrat and Farah Abdel Samad (Lebanon) for their valuable supports to the field works in their countries.
Disclosure statement
No potential conflict of interest was reported by the author(s).