Multivariate analysis as a reliable tool for segregating strawberry cultivars with the best antioxidant capacity

Abstract

During 2018 and 2019 we ad hoc investigated fruit weight, chemical composition and antioxidant activity of five strawberry cultivars (Irma' as a day-neutral cultivar, and 'Clery', 'Roxana' Asia' and an 'Unknown' genotype as short-day strawberries). By ANOVA and multivariate analysis (PCA), differences were observed between cultivars based on the fruit weight, contents of soluble solids, acidity, maturity indexes, main phenolic compounds and antioxidant activity. Results showed that 'Roxana' had the largest fruit at the same growing and environmental conditions. The highest soluble solids content (SSC), acidity and ripening and taste indexes, phenolic compounds had 'Clery' and Asia', respectively. Asia' is the cultivar with the highest antioxidant capacity. The poorest evaluated properties were shown by the 'Unknown' genotype, followed by 'Irma'. Season (year) had more influence on fruit weight, acidity, phenolic compounds and antioxidant activity whereas effect on other properties was not significant. Interaction cultivar x year had significant effect on all evaluated properties with exception of fruit weight. Correlation matrix indicated significant positive or negative correlations between most primary and secondary metabolites in fruit of strawberries. Principal component analysis (PCA) can be a very good tool for segregating strawberry genotypes with the best nutritional value and health promoting compounds.