Abstract
Genetic programming, in conjunction with advanced
analytical instruments, is a novel tool for the
investigation of complex biological systems at the
whole-tissue level.
In this study, samples from tomato fruit grown
hydroponically under both high- and low-salt conditions
were analysed using Fourier-transform infrared
spectroscopy (FTIR), with the aim of identifying
spectral and biochemical features linked to salinity in
the growth environment.
FTIR spectra are not amenable to direct visual
analysis, so supervised machine learning was used to
generate models capable of classifying the samples
based on their spectral characteristics. The genetic
programming (GP) method was chosen, since it has
previously been shown to perform with the same accuracy
as conventional data modelling methods, but in a
readily-interpretable form.
Examination of the GP-derived models showed that there
was a small number of spectral regions that were
consistently being used. In particular, the spectral
region containing absorbances potentially due to a
cyanide/nitrile functional group was identified as
discriminatory. The explanatory power of the GP models
enabled a chemical interpretation of the biochemical
differences to be proposed. The combination of FTIR and
GP is therefore a powerful and novel analytical tool
which, in this study, improves our understanding of the
biochemistry of salt tolerance in tomato plants.
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