From image processing to classification: IV. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for breadmaking.
The end-use quality of products made from doughs consisting of wheat flour and water is often dependent upon the storage (gluten) proteins of the grain endosperm. Today the electrophoretic patterns of the high molecular weight (HMW) glutenin subunits are used for quality selections in wheat breeding programs in several countries. In this study, we used two multivariate techniques to classify digitized patterns from isoelectric focusing of gliadins and glutenins: a two-layered neural network architecture consisting of a self-organizing feature map and a feed-forward classifier [1], and discriminant analysis [2,3]. Three groups of seven wheat varieties (Triticum aestivum L.), associated with poor, medium or good properties in relation to bread-making quality, were used. The best classification results were obtained by the neural network model, based on data from the gliadin fraction: it was possible to classify varieties associated with poor or good quality, with recognition rates of 70 and 69%, respectively. The statistical method was better suited to solve the classification problem when the data was based on the glutenin fraction: if a specific variety was already known to be non-poor, this method enabled us to classify the medium- and good-quality classes with recognition rates of 90 and 88%, respectively. The results obtained were confirmed by correlation coefficients.