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Vol. 11, Issue 9 (2022)

Comparative analysis of Zinc and Iron content estimated by destructive and non-destructive methods in selected rice genotypes

Author(s):
Shrikant Yankanchi, Vinay Premi, Dayanad Navrang, Devidas Thomabre and Ajit Kumar Mannade
Abstract:
Iron and Zinc are the two essential micronutrients required in minute amounts for proper human nutrition as they take part in body’s metabolism and immunity development. Nutrient analyses of plant samples are carried out by destructive and non-destructive methods. Iron (Fe) and Zinc (Zn) content of polished rice grains from twenty known rice genotypes was quantified using (Energy Dispersive X-ray Fluorescence) ED-XRF and the results were compared with standard Atomic Absorption Spectrometer (AAS). Average concentration, standard deviation and relative standard deviations were calculated for the comparison of both the methods. The mean grain Zn content of 20 rice genotypes analyzed by AAS and ED-XRF was 21.30 and 20.67 with standard deviation 4.739 and 4.417. Similarly the mean grain Fe content of 20 rice genotypes analyzed by AAS and ED-XRF was 4.54 and 4.4045 respectively with standard deviation 42.043 and 1.582. Results showed significant higher positive correlations between AAS and ED-XRF values for both Zn (0.921**) as well as Fe (0.940**) content. The Zn and Fe values obtained from AAS and ED-XRF for all 20 rice genotypes were close to each other with minor variations by 0.25 ± 2.45 ppm. Finally concluded that both analytical techniques, AAS and ED-XRF are very good and highly precise however, non-destructive ED-XRF is considered to be the easiest, fast, cost effective as well as eco-friendly as compared to destructive methods (AAS).
Pages: 2606-2609  |  443 Views  320 Downloads


The Pharma Innovation Journal
How to cite this article:
Shrikant Yankanchi, Vinay Premi, Dayanad Navrang, Devidas Thomabre, Ajit Kumar Mannade. Comparative analysis of Zinc and Iron content estimated by destructive and non-destructive methods in selected rice genotypes. Pharma Innovation 2022;11(9):2606-2609.

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