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The content and bioaccumulation of trace (Ag, Se, As) and major elements (Ca, Mg, Na and K) in wild edible mushroom Macrolepiota procera and its corresponding soil substrates, collected from five sites in the Rasina region in central Serbia, were investigated. The content of Ag, As and Se was determined by inductively coupled plasma mass spectrometer (ICP-MS) while the amount of Ca, Mg, Na and K was determined by inductively coupled plasma optical emission spectrometer (ICP-OES). The concentrations of major elements in the mushrooms were at typical levels. As far as trace elements are concerned, M. procera bioaccumulates silver although all samples were collected from unpolluted sites. It was found that the content of Ag depended on the geographical origin and the density of fruiting body on the certain site. Principal component analysis distinguished the mushroom samples from different geographical areas and revealed the influence of soil composition on metal content in fruiting bodies. Also, a linear regression correlation test was performed to investigate correlations between Ag, Cd, Se, Pb and As in caps and stipes at different geographic sites separately. In addition, our results indicated that M. procera could serve as a good dietary source of Mg, K and Se. The content of Ag and As was low, so it could not pose a health risk for consumers.

The elemental profiles of 63 red and white wine samples from four different regions in Serbia were investigated. Twenty-one elements were analysed (Ca, Mg, Na, K, Fe, Mn, Cu, Zn, Co, Se, Cr, V, Ni, Cd, As, Al, Sb, Pb, Ba, Rb, and Be) by inductively coupled plasma quadrupole mass spectrometry (ICP-Q-MS) and inductively coupled plasma with optical emission spectrometry (ICP-OES). A pattern recognition method was applied in order to classify and differentiate type, seasonal variability, and geographical origin of the wine. Dietary mineral intake for elements was calculated in order to assess their contribution to daily intake. The most important descriptors for discrimination among red and white wine samples were Be, Al, Rb, Mg, K, Cu, Mn, and Na, in descending order. The variables Cd, Pb, As, Sb, V, Na, K, and Zn have the highest influence on vintage-to-vintage classification of red wines. Furthermore, the model revealed the existence of three groups of descriptors for different regions of production. All obtained statistical models confirmed that data from the elemental content of wine samples could be used for accurate prediction of wine type, seasonal variability, and regional origin.