Recognition of Environmental and Genetic Effects on Barley Phenolic Finger Prints by Neural Networks

High performance liquid chromatography (HPLC) spectra were analyzed by computer in order to find correlations between secondary metabolites and growth conditions of six varieties of barley. Five of the six varieties were selected according to their partial resistance to mildew, while one was chosen due to its mildew resistance gene. HPLC spectra are not easy to interpret and it is in general hard to assess the interdependence of the spectral components, and their correlation to properties of the material analysed. Using data from experiments in the field we formulated three classification tasks, where the spectra served as input to artificial neural network algorithms. The output corresponded to classes of growth conditions: varieties fertilized by nitrogen or not, and varieties treated by fungicide or not. In a third task the spectra were classified according to variety. All the classification tasks could successfully be solved by a single neuron only --- the simplest "network" possib..