CHARACTERIZATION OF SERBIAN MONOFLORAL HONEY ACCORDING TO THEIR MINERAL CONTENT USING ICP-OES

1. Factor scores Factor Loadings 1.50 1.00 22 17 18 2 16 20 21 Mg 10 19 11 0.50 0.50 3 9 15 14 Loading1 12 13 Score1 7 -0.50 0.00 K 1 2 Mn 3 8 -1.50 4 -0.50 5 Na 1 6 Cu Ca Fe Zn Cr -2.50 Ni -1.00 -2.00 -1.13 -0.25 0.63 1.50 -1.00 -0.50 0.00 0.50 1.00 Score2 Loading2 Table 2. Average element levels and range between minimal and maximal value in analyzed honey samples (mg/kg) Table 1. Wavelength for individual elements for ICP-OES determination Figure 1. The PCA loadings Table3. The eigenvalues and the ratios of the variance explained by four components using covariance matrix CHARACTERIZATION OF SERBIAN MONOFLORAL HONEY ACCORDING TO THEIR MINERAL CONTENT USING ICP-OES Kristina Lazarević1, Milica Jovetić1, Ivan Andjelković2, Dušanka Milojković-Opsenica2 1 Center for Food Analysis, Zmaja od Noćaja 11, Belgrade, Serbia 2 Faculty of Chemistry, University of Belgrade, Studentki trg 12-16, Belgrade, Serbia The characterization of three types of Serbian honeys (sunflower, Acacia and linden) was carried out on the basis of their mineral content (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Ni and Cr). About 0.6-0.7 g of fresh honey was treated with 7 ml of 65% HNO3 (w/w) (Merck, Darmstadt, Germany) and 1 ml of 35% (w/w) H202 (Merck, Darmstadt, Germany) in PTFE vessels. The microwave closed digestion system (ETHOS 1, MILESTONE, Italy) was used for mineralization process. The final clear solution was made up to 50ml with DWI water. All mineral elements indigested solutions were determined using a ICP-OES (iCAP 6500 Duo ICP, Thermo Scientific, UK). Wavelengths for the determination of individual elements are given in Table 1. Determined levels of elements in samples are given in Table 2. Principal component analysis (PCA) was performed on the data matrix by the use of a computer program (http://www.ncss.com). It displays objects (honey) in a reduced space by finding a direction (principal components) that best preserves the scatter of the observations (elements) in the multidimensional space. Table 3 lists the eigenvalues of the covariance matrix, ordered from largest to smallest, the third column of this table shows the difference between each eigenvalues and the next smaller eigenvalue and the fourth column shows the proportion. These results suggest a significant four component model, which explained 96.60% of the total variance (information), considering only the eigenvalue higher than one. Loading plot shows the first PCA component dominated by the concentration of Mg on the positive (high) side and the concentrations of Na, Ca, Fe, Cu, Zn, Cr, Ni on negative (low) side; and the second component being a mixture of the concentrations of Na, Cu, Zn, Mg on the positive side and concentration of K, Fe on the negative side. Graphing scores onto the plane described by PC1 and PC2 we obtain three classes, i.e. the honey samples could be divided into three different groups according to their origin – sunflower honey, acacia honey and linden honey (Figure 2). Figure 2. The PCA scores: 1 – sunflower honey; 2 – acacia honey; 3 – linden honey