Metabolic fingerprinting of salt-stressed tomatoes

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Helen E. Johnson and David Broadhurst and 
                 Royston Goodacre and Aileen R. Smith",
  title =        "Metabolic fingerprinting of salt-stressed tomatoes",
  journal =      "Phytochemistry",
  year =         "2003",
  volume =       "62",
  number =       "6",
  pages =        "919--928",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/S0031-9422(02)00722-7",
  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
  notes =        "PMID: 12590119

                 See \cite{03_essa_153-163}",

Genetic Programming entries for Helen E Johnson David I Broadhurst Royston Goodacre Aileen R Smith