Heavy Metal Levels and Mineral Nutrient Status of Natural Walnut (Juglans regia L.) Populations in Kyrgyzstan: Nutritional Values of Kernels.
Ibrahim Ilker OzyigitMehmet Emin UrasIbrahim Ertugrul YalcinZeki SeverogluGoksel DemirBakyt BorkoevKalipa SalievaSevil YucelUmran ErturkAli Osman SolakPublished in: Biological trace element research (2018)
In this study, mineral nutrient and heavy metal (Al, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, and Zn) contents of the walnut kernels and their co-located soil samples collected from the four different zones of natural walnut forests (Sary-Chelek, Arslanbap, and Kara-Alma in Jalal-Abad Region and Kara-Shoro in Osh Region) in Kyrgyzstan were investigated. The highest concentrations for all elements determined in the soil samples were observed in the Sary-Chelek zone whereas the Arslanbap zone was found to be having the lowest concentrations except Fe and Zn. The highest concentrations in the kernels of walnut samples were found to be in the Sary-Chelek zone for Ca, Fe, K, Mg, and Zn; in the Kara-Shoro zone for Cu; in the Arslanbap zone for Mn; and in the Kara-Alma zone for Na whereas the lowest concentrations were found to be in the Arslanbap zone for Ca, Fe, K, Mg, Na, and Zn and in the Sary-Chelek zone for Cu and Mn, respectively. Also, the levels of Al, Cd, Ni, and Pb in kernel samples could not be detected by ICP-OES because their levels were lower than the threshold detection point (10 μg.kg-1). Additionally, our data indicated that the walnut kernels from Kyrgyzstan have higher values for RDA (recommended daily allowances) in comparison with the walnut kernels from other countries.
Keyphrases
- heavy metals
- metal organic framework
- aqueous solution
- risk assessment
- health risk assessment
- physical activity
- room temperature
- sewage sludge
- climate change
- machine learning
- transition metal
- mass spectrometry
- big data
- electronic health record
- deep learning
- high speed
- ionic liquid
- sensitive detection
- artificial intelligence