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Characterization and Classification of Serbian Honey by Physicochemical Parameters Introduction Experimental K. Lazarević 1, M. Jovetić 1, D. Milojković-Opsenica.

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Presentation on theme: "Characterization and Classification of Serbian Honey by Physicochemical Parameters Introduction Experimental K. Lazarević 1, M. Jovetić 1, D. Milojković-Opsenica."— Presentation transcript:

1 Characterization and Classification of Serbian Honey by Physicochemical Parameters Introduction Experimental K. Lazarević 1, M. Jovetić 1, D. Milojković-Opsenica 2, F. Andrić 2, Ž. Tešić 2 1 Center for Food Analysis, Zmaja od Noćaja 11, Belgrade, Serbia 2 Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, P.O. Box, Belgrade Serbia has a very long tradition of beekeeping. Good climate and variety of botanical species give a great potential for development of apiculture. In recent years production of honey is t per year, however, Serbia has much greater potential. Most often produced monofloral honeys are acacia (Robinia pseudoacacia), sunflower (Helianthus annuus) and linden (Tilia cordata) honey. Serbian honey has excellent characteristics and it could be very interesting for EU market, so it is very important to verify its compliance with the quality specifications of European Union Honey samples were of various geographical origins from Serbia, but were similarly aged. The following three certified monofloral honey categories were included: acacia (Robinia pseudo acacia), linden (Tillie Cordata)and sunflower (Helianthus annuus), and polifloral honey. The following physicochemical characteristics were analyzed: moisture, electrical conductivity, pH and free acidity. All the analyses were performed according to the methods in agreement with EU legislation (Bogdanov et al., 1997). The results are presented in Table 1. Table 1: physicochemical characteristic of Serbian honey Principal component analysis (PCA) was performed on the dataset representing four main physico-chemical properties of 372 samples of domestic honeys using the PLS_Toolboxsoftware for the MATLAB (v , Eigenvector Research,Inc. All three components include cumulatively 97.7% of the total data variability, with the first principal component explaining 62.18% of the overallvariability. Plotting the scores of the first two principalcomponents shows that the main discrimination among the samples was achieved along the first principal component, forming the two clusters of honeys samples- acacia honey and polyfloral honey (Figure 1 and 3), while the discrimination along the second principal component is not so obvious. The loading plots (Figure 2 and 4) suggest that the main variables influencing the second principal component in the positive sense are pH and specific optical rotation, while conductivity and acidity affect it in the negative manner. All four variables have a strong positive influence on the first principal component, though the condifuctivity and specific rottation affect it slightly stronger. Therefore, it is expected that samples of acacia honey show significantly lower values of these two factors (average cond. = 0.157, average specific rotation = ) compared to the samples of polyfloral honey (average cond. = 0.518,average specific rotation = ). Additionally, these two classes are discriminated by the total acidity as well. Average acidity in the first case is while for polyfloral honey it goes up to the average value of Figure 1. PC scores for the first two principal components. Dispositon of the samples A – acacia honey, B – polyfloral honey, Class 0 – unclassified samples Figure 2. Loading vectors of PC1 and PC2 Figure 3. PC scores for the first and third principal components. Dispositon of the samples: A – acacia honey, B – polyfloral honey, Class 0 – unclassified samples Figure 4. Loading vectors of PC1 and PC3


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