The TOM model is a neural net which was initially developed for speech recognition. It can be trained to recognise words in speech by reference to the sound pattern over a sequence of time steps.
Electrophoresis creates a set of bands in the gel, caused by migration of protein from the seed.
Each seed variety generates a characteristic pattern. The bands are made visible by staining. They can then be imaged and digitised to create an input to a TOM, which treats the variation with distance along the lane in the same way as the time sequence for which it was originally employed. In this way the characteristic signature of a seed variety can be recognised. Details of the design and training of the network are given.