The Biologically Active (Bioactive) Compounds in Tomato (Lycopersicon esculentum Mill.) as a Function of Genotype

1 University of Kragujevac, Faculty of Agronomy, Cacak, Serbia
2 Institute for Vegetable Crops, Smederevska Palanka, Serbia


MLADENOVIC, J., G. ACAMOVIC–ÐOKOVIC, R. PAVLOVIC, M. ZDRAVKOVIC, Z. GIREK and J. ZDRAVKOVIC, 2014. The biologically active (bioactive) compounds in tomato (Lycopersicon esculentum Mill.) as a function of genotype. Bulg. J. Agric. Sci., 20: 877-882


The 15 cherry tomato genotypes from the Institute for Vegetable Crops, Smederevska Palanka were grouped in order to define the start material for breeding. Genotypes were grouped according to: average content of carotenoids (lycopene, β-carotene), total sugars and total acidity in tomato fruits, through PCA (Principal Component Analysis). Correlation matrix showed low, both positive and negative correlation among the researched traits. The level of lycopene was negatively correlated to β-carotene, L-ascorbic acid and total acidity, while it was in positive correlation with total sugars. β-carotene was negatively correlated to l-ascorbic acid and total sugars, but in positive correlation with total acidity, while it was negatively correlated with total sugars. Total sugars were negatively correlated with total acids. The first three components explained 78.55%, while first two components explained 62.07% of total variability. Genotypes on the positive side of both main components (PC1 and PC2) had the highest mean values of L-ascorbic acid (GK67, GK64, GK33, GK19), while genotypes with highest mean values of total sugars were on the negative side of both components (GK153, GK71, GK2, GK75, GK91). Genotypes GK70 and GK1 had high level of β-carotene and total acidity, low level of L-ascorbic acid and minimal mean values of total sugars. GK10, GK20, GK88 and GK74 stood out in the quadrant of negative values of the first main component and positive values of the second main component.

Key words: cherry tomato, 15 genotypes, quality attributes, Principal Component Analysis

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