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Wild-Growing and Conventionally or Organically Cultivated Sambucus nigra Germplasm: Fruit Phytochemical Profile, Total Phenolic Content, Antioxidant Activity, and Leaf Elements.

Theodora PapagrigoriouPanagiota IliadiMilan N MitićJelena M MrmošaninKaterina PapanastasiEleftherios KarapatzakEleni MaloupaAlexia V GkourogianniAnastasia V BadekaNikos KrigasDiamanto Lazari
Published in: Plants (Basel, Switzerland) (2023)
European elder or elderberry ( Sambucus nigra L., Viburnaceae) is a plant species with known high pharmaceutical and nutritional value. However, the Greek native germplasm of S. nigra has not been adequately utilized to date as in other regions. This study evaluates the fruit antioxidant potential (total phenolic content and radical scavenging activity) of wild-growing and cultivated germplasm of Greek S. nigra. In addition, nine cultivated Greek S. nigra genotypes were evaluated regarding the effects of fertilization (conventional and organic) on fruit phytochemical and physicochemical potential (total flavonoids, ascorbic acid content, pH, total soluble solids, and total acidity), as well as on the antioxidant potential (total phenolic content and radical scavenging activity) of fruits and leaves. Additionally, an analysis of macro- and micro-elements in the leaves of the cultivated germplasm was performed. The results demonstrated comparatively higher total phenolic contents of fruits of cultivated germplasm. The genotype was the decisive factor in the fruits' phytochemical potential and leaves' total phenolic content of cultivated S. nigra germplasm. Similarly, fertilization regime effects were found to be genotype-dependent, affecting fruit phytochemical and physicochemical attributes. The trace element analysis results were similar, with genotypes varying significantly in their concentrations of macro- and micro-elements. The current work builds on previous domestication attempts for Greek S. nigra , providing new data on the phytochemical potential of this important nutraceutical species.
Keyphrases
  • oxidative stress
  • heavy metals
  • deep learning