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dc.contributor.author Smith, Aileen R.
dc.contributor.author Goodacre, Royston
dc.contributor.author Johnson, Helen E.
dc.contributor.author Broadhurst, David I.
dc.date.accessioned 2008-12-16T14:43:41Z
dc.date.available 2008-12-16T14:43:41Z
dc.date.issued 2003-03-01
dc.identifier.citation Smith , A R , Goodacre , R , Johnson , H E & Broadhurst , D I 2003 , ' Metabolic fingerprinting of salt-stressed tomatoes ' Phytochemistry , pp. 919-928 . , 10.1016/S0031-9422 , 10.1016/S0031-9422(02)00722-7 en
dc.identifier.other PURE: 93626
dc.identifier.other dspace: 2160/1721
dc.identifier.uri http://hdl.handle.net/2160/1721
dc.description Helen E. Johnson, David Broadhurst, Royston Goodacre and Aileen R. Smith (2003). Metabolic fingerprinting of salt-stressed tomatoes. Phytochemistry, 62 (6), 919-928. Sponsorship: EU / BBSRC RAE2008 en
dc.description.abstract The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof Phytochemistry en
dc.title Metabolic fingerprinting of salt-stressed tomatoes en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/S0031-9422
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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