Mineral Composition of Herbaceous Species Seseli rigidum and Seseli pallasii: a Chemometric Approach

Authors

  • Marija D. Ilic Laboratory Sector, Laboratory for Analytical Chemistry, Veterinary Specialized Institute "Niš", DimitrijaTucovića 175, Niš, 18106, Serbia
  • Violeta D. Mitić University of Niš, Faculty of Science and Mathematics, Department of Chemistry, Višegradska 33, Niš, 18000, Serbia
  • Snežana B. Tošić University of Niš, Faculty of Science and Mathematics, Department of Chemistry, Višegradska 33, Niš, 18000, Serbia
  • Aleksandra N. Pavlović University of Niš, Faculty of Science and Mathematics, Department of Chemistry, Višegradska 33, Niš, 18000, Serbia
  • Marija S. Marković University of Niš, Faculty of Science and Mathematics, Department of Biology and Ecology, Višegradska 33, Niš, 18000, Serbia
  • Gordana S. Stojanović University of Niš, Faculty of Science and Mathematics, Department of Chemistry, Višegradska 33, Niš, 18000, Serbia
  • Vesna P. Stankov Jovanović University of Niš, Faculty of Science and Mathematics, Department of Chemistry, Višegradska 33, Niš, 18000, Serbia

DOI:

https://doi.org/10.17344/acsi.2021.6755

Keywords:

Sesli rigidum, Seseli palasii, mineral composition, ICP-OES, multivariate statistics

Abstract

Nutrients play an essential role in many metabolic processes whose deficiency or excess can be harmful to the plant itself and through the food chain to both animals and humans. Medicinal plants used in the food and pharmaceutical industries can be contaminated with increased concentrations of heavy metals. The plant species Seseli rigidum and Seseli pallasii from the Balkan Peninsula are used in traditional medicine and spices in the diet, so it was necessary to determine the mineral composition to ensure their safe application. In this work, the mineral composition was determined in medicinal species of the genus Seseli using inductively coupled plasma with optical emission spectrometry (ICP-OES). Two multivariate statistic methods –principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to distinguish samples regarding their mineral composition. The mineral composition of both studied species is following the literature data. The results obtained using multivariate statistics methods agree and distinguish certain parts of the tested plants based on the highest content of micro, macro, or trace elements.

Downloads

Published

15.09.2021

Issue

Section

Analytical chemistry