Chemometric analysis of antioxidant activity and anthocyanin content of selected wild and cultivated small fruit from Serbia. (12th September 2014) - Mitic, Violeta; Stankov Jovanovic, Vesna; Dimitrijevic, Marija; Cvetkovic, Jelena; Simonovic, Strahinja; Nikolic Mandic, Snezana
<x content-type="archive" xml:space="preserve">Abstract</x> Introduction. The fruit types such as raspberries (Rubus idaeus), cherries (Prunus cerasus), blackberries (Rubus fructicosus), blackthorns (Prunus spinosa) and aronia (Aronia melanocarpa) are very common in Serbia. These fruit species are a valuable source of antioxidants. The goal of our work was to evaluate the antioxidant activities of ethyl acetate extracts of these five berries and to establish possible correlation between the content of anthocyanin and the antioxidant activity. Materials and methods. To determine antioxidant activity of the selected fruits, the DPPH (2, 2-diphenyl-1-picrylhydrazyl) radical scavenging activity, the ABTS (2, 2-azinobis-3 ethyl benzothiazoline-6-sulfonic acid) cation decolorization activity, the FRAP (ferric reducing antioxidant power), and the TRP (total reducing power) were measured. Results. The four methods selected for our assays showed strong antioxidant properties for blackberry samples. The total anthocyanin content was estimated using the pH differential method. The highest amount of anthocyanin was found in blackberry samples [(1063.53 ± 0.01) mg·kg–1 fresh fruit], while the lowest was in raspberry samples [(180.84 ± 0.02) mg·kg–1 fresh fruit]. The correlation between the content of anthocyanin and antioxidant activity was established using regression analysis. The highest correlation was found between total reducing power and total anthocyanin (r = 0.97, p < 0.05). Hierarchical cluster analysis divided selected fruit species into two statistically significant clusters. Conclusion. Our results confirmed that analyzed berries are rich in anthocyanins. A strong correlation among different assays as well as with anthocyanin content was observed. Cluster analysis can be used in food science, to classify different food types into groups, based on similarity among the results.