“Long storage” tomato is a crop traditionally cultivated in the Mediterranean area under no water supply, that recently has attracted the interest of breeders for its high tolerance to drought and as potential genetic source in breeding programs for water stress resistance. A collection of 28 genotypes of “long storage” tomato (Solanum lycopersicum L.) was studied for carotenoid and polyphenol profile and content, vitamin C, and other physico-chemical traits of fruits. Tomato carotenoids and polyphenols were identified and quantified using high-performance liquid chromatography coupled with diode array detection and electrospray-mass spectrometry (HPLC/DAD/ESI-MS); nineteen different phenolic compounds and six different carotenoids, for a total of 25 markers, have been detected, quantified and used to discriminate among the different landraces to find out which could be the best candidate for a medium-to-large scale cultivation. Different statistical approaches (ANOVA, Principal Components Analysis, Cluster Analysis) have been used for data analysis.

Targeted secondary metabolic and physico-chemical traits analysis to assess genetic variability within a germplasm collection of “long storage” tomatoes

Rizzo, Valeria;Cosentino, Salvatore Luciano;
2018

Abstract

“Long storage” tomato is a crop traditionally cultivated in the Mediterranean area under no water supply, that recently has attracted the interest of breeders for its high tolerance to drought and as potential genetic source in breeding programs for water stress resistance. A collection of 28 genotypes of “long storage” tomato (Solanum lycopersicum L.) was studied for carotenoid and polyphenol profile and content, vitamin C, and other physico-chemical traits of fruits. Tomato carotenoids and polyphenols were identified and quantified using high-performance liquid chromatography coupled with diode array detection and electrospray-mass spectrometry (HPLC/DAD/ESI-MS); nineteen different phenolic compounds and six different carotenoids, for a total of 25 markers, have been detected, quantified and used to discriminate among the different landraces to find out which could be the best candidate for a medium-to-large scale cultivation. Different statistical approaches (ANOVA, Principal Components Analysis, Cluster Analysis) have been used for data analysis.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/363900
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