Identification of wheat varieties using matrix-assisted laserdesorption/ionisation time-of-flight mass spectrometry and anartificial neural network

A novel tool for variety identification of wheat (Triticum aestivum L.) has been developed: an artificialneural network (ANN) is used to classify the gliadin fraction analysed by matrix-assisted laserdesorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS). The robustness of this novelmethod with respect to various experimental parameters has been tested. The results can be summarised: (i)With this approach 97% of the wheat varieties can be classified correctly with a corresponding correlationcoefficient of 1.0, (ii) The method is fast since the time of extracting gliadins from flour can be reduced to20 min without significant decrease in overall performance, (iii) The storage of flour or extracts understandard conditions does not influence the classification ability (i. e. the generalisation ability) of themethod, and (iv) The classification obtained is not influenced by the identity of the operator making theanalysis. This study demonstrates that a combination of an ANN and MALDI-TOFMS analysis of thegliadin fraction provides a fast and reliable tool for the variety identification of wheat. Copyright 1999 JohnWiley & Sons, Ltd.