Abstract
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 sevaral
countries. In this study, we used two multivariate techniques to
classify digitized patterns from isoelectric focusing og 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 [riticum 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
enebled 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.
Original language | English |
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Journal | Electrophoresis |
Volume | 17 |
Pages (from-to) | 694-698 |
ISSN | 0173-0835 |
Publication status | Published - 1996 |