Akademik Veri Yönetim Sistemi
JOURNAL OF THE INSTITUTE OF BREWING, cilt.104, sa.6, ss.351-354, 1998 (SCI-Expanded)
Computerised image analysis was investigated as an automated quality control technique for rapid and precise classification of barleys according to their malting qualities. The nineteen samples used in this study were two-rowed winter type barleys. Six of these were assigned to Group 1 and the rest was included into Group 2 according to their performance in a micro-malting system. Two sets of kernels from each barley sample were recorded. one for training and the other for testing purpose. To be able to have functions yielding the best discrimination scores in the training set, classification analysis was initially performed by the selection of each feature alone as an independent variable. Correct overall classification scores ranged between 52.9% and 78.1% when individual features were used in discriminant analyses. In the second step, the combination of features which yielded the best classification score was determined by stepwise discriminant analysis. Overall success in various combinations was more than 83%. To test the unknown barley samples, the discriminant function produced by the combination of compactness and equivalent diameter features was selected. Correct classification scores of this combination ranged between 60.0% and 94.7%. The result shows that the discrimination of the unknowns is very close to that achieved in the training set.