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Targeted secondary metabolic and physico-chemical traits analysis to assess genetic variability within a germplasm collection of “long storage” tomatoes
•Long storage tomato is a crop typical of South Mediterranean areas.•Polyphenol and carotenoid profiles have been studied in long storage tomatoes.•25 markers within polyphenols and carotenoids have been identified.•Principal Components Analysis allowed to identify the most discriminating compounds....
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Published in: | Food chemistry 2018-04, Vol.244, p.275-283 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Long storage tomato is a crop typical of South Mediterranean areas.•Polyphenol and carotenoid profiles have been studied in long storage tomatoes.•25 markers within polyphenols and carotenoids have been identified.•Principal Components Analysis allowed to identify the most discriminating compounds.•Cluster analysis highlighted the occurrence of three homogeneous groups.
“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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ISSN: | 0308-8146 1873-7072 |
DOI: | 10.1016/j.foodchem.2017.10.043 |