Eco-physiological research to improve tomato fruit quality for processing and human health

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Dumas, Y. | Bertin, Nadia | Borel, Charlotte | Bussieres, Philippe | Gautier, Hélène

Edité par CCSD ; International Society for Horticultural Science -

International audience. The quality of tomato fruit is important for processing (fruit size, dry matter content, colour, viscosity, etc.) and consumer health and pleasure (nutrient composition, soundness, taste). This paper highlights research to develop understanding of how the various aspects of quality are determined by the complex interaction of genetics, the environment and crop management. Many recent studies have been conducted to improve tomato quality genetically or by cultural techniques. The interactions between the various quality criteria are often complex, so that they must be considered jointly in studies on the underlying physiological processes. Models can assist, for example, in building a systemic vision to explain fruit growth through dry matter and water input, sugar and acid accumulation. Models are presently available or under development to describe cell division, DNA endoreduplication (cell expansion) and sap fluxes (water and carbon). Tomato fruit are also rich in anti-oxidant compounds that have been recognised as beneficial for human health. Variation in the concentration of these compounds has been the focus of much recent research, and once again models can assist in describing how genetics and cultural management contribute to fruit composition. Once the physiological and environmental basis for tomato fruit quality is understood, research should consider genetic aspects using contrasting and well-characterised genotypes. Genetic variability in quality characteristics can be linked to QTL's (Quantitative Trait Loci) made up of one or more genes. If their selection is based on model parameters, OTL's should be more stable than those directly obtained from quality criteria. Thus, the coupling of eco-physiological and genetic approaches should enable the prediction of quality potential in known genotypes as well as demonstrate the predictive capability of associating QTL and Model approaches

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