Metabolomics driven analysis of six Nigella species seeds via UPLC-qTOF-MS and GC-MS coupled to chemometrics

Abstract

Nigella sativa, commonly known as black cumin seed, is a popular herbal supplement that contains numerous phytochemicals including terpenoids, saponins, flavonoids, alkaloids. Only a few of the ca. 15 species in the genus Nigella have been characterized in terms of phytochemical or pharmacological properties. Here, large scale metabolic profiling including UPLC-PDA-MS and GC-MS with further multivariate analysis was utilized to classify 6 Nigella species. Under optimized conditions, we were able to annotate 52 metabolites including 8 saponins, 10 flavonoids, 6 phenolics, 10 alkaloids, and 18 fatty acids. Major peaks in UPLC-MS spectra contributing to the discrimination among species were assigned as kaempferol glycosidic conjugates, with kaempferol-3-O-[glucopyranosyl-(1→2)-galactopyranosyl-(1→2)-glucopyranoside, identified as potential taxonomic marker for N. sativa. Compared with GC-MS, UPLC-MS was found much more efficient in Nigella sample classification based on genetic and geographical origin. Nevertheless, both GC-MS and UPLC-MS support the remote position of Nigella nigellastrum in relation to the other taxa.

Keywords: Black cumin; GC; GC–MS; HCA; MS; Metabolomics; N. arvensis; N. damascena; N. hispanica; N. nigellastrum; N. orientalis; N. sativa; NH; NS; Natural products; Nigella hispanica; Nigella sativa; PCA; Principal component analysis; QC; UPLC; UPLC–MS; gas chromatography; hierarchical cluster analysis; mass spectrometry; principal component analysis; quality control; ultra performance liquid chromatography.