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.",