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The Mineral Profile of Polish Beers by Fast Sequential Multielement HR CS FAAS Analysis and Its Correlation with Total Phenolic Content and Antioxidant Activity by Chemometric Methods

Beer is the most common alcoholic beverage worldwide, and is an excellent source of macro- and microelements, as well as phenolic compounds. In this study, a fast method for the determination of Na, K, Mg, Ca, Fe, Mn, and Cu in beer was developed using flame atomic absorption spectrometry. The preci...

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Detalles Bibliográficos
Autores principales: Zambrzycka-Szelewa, Elżbieta, Nalewajko-Sieliwoniuk, Edyta, Zaremba, Mariusz, Bajguz, Andrzej, Godlewska-Żyłkiewicz, Beata
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436273/
https://www.ncbi.nlm.nih.gov/pubmed/32727164
http://dx.doi.org/10.3390/molecules25153402
Descripción
Sumario:Beer is the most common alcoholic beverage worldwide, and is an excellent source of macro- and microelements, as well as phenolic compounds. In this study, a fast method for the determination of Na, K, Mg, Ca, Fe, Mn, and Cu in beer was developed using flame atomic absorption spectrometry. The precision of this method was between 0.8 and 8.0% (as the relative standard deviation (RSD)), and limits of detections were in the range of 0.45 (Mn)–94 µg/L (Na). Among the macroelements tested in the beer samples, K was found at the highest concentration, whereas Na was found at the lowest concentration level. Beer also turned out to be a good source of Mg and K. The total phenolic content (TPC) was determined by the Folin–Ciocalteu method, while the antioxidant activity was estimated by the ABTS method. The results show remarkable variations in the mineral content, TPC, and antioxidant activity across the beer types and brands. Moreover, the relations between the type, color, refraction index, antioxidant activity, extract, alcohol, mineral, and the total phenolic contents were investigated using the factor analysis of mixed data (FAMD) combined with hierarchical clustering on principal components (HCPC).