4.6 Article

Comparative Study of the Bioactive Properties and Elemental Composition of Red Clover (Trifolium pratense) and Alfalfa (Medicago sativa) Sprouts during Germination

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APPLIED SCIENCES-BASEL
卷 10, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/app10207249

关键词

trace-elements; atomic absorption spectroscopy; antioxidant activity; isoflavones; nutraceuticals

资金

  1. Operational Programme Human Capital of the Ministry of European Funds [51668/09.07.2019, 124705]

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Considering the growing interest in functional foods, the identification of the individual species of elements is of great importance in understanding specific nutraceutical properties. The present study aims to compare the dynamic of the elemental content (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Ni, and Se), total polyphenols, and antioxidant activity of Trifolium pratense L. and Medicago sativa L. sprouts in different germination stages. The elemental profile was established by atomic absorption spectroscopy after the microwave acid digestion of the samples, while total polyphenols and anti-radical activity were evaluated by UV-Vis spectroscopic methods. Phenolic compounds and anti-radical activity of both alfalfa and red clover sprouts varied with germination stages. Germination can significantly increase the anti-radical activity in the first 3 days of germination, followed by a decline in the following days. An increase in total polyphenols was noticed, starting from the second day of germination in both plant species. There were significant (p < 0.05) differences for Ca, Na, Fe, K, Zn, and Mg contents among the sprouts, depending on the germination stage and plant species. The calcium contents of alfalfa ranged between 200.74 mu g/g DW (raw seeds) and 2765.31 mu g/g DW (sprouted), while in red clover between 250.83 mu g/g DW and 601.59 mu g/g DW. Maximum selenium content in alfalfa sprouts, reached in the 3rd day of germination (11.42 mu g/g DW), exceeded the maximum value measured in red clover (9.42 mu g/g DW). The data were subject to statistical processing using analysis of variance (ANOVA), multivariate analysis (PCA) and hierarchical clustering analysis (HCA).

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