CLASSIFICATION OF BARLEYS BASED ON MALTING QUALITY BY IMAGE-ANALYSIS

Computerised image analysis was investigated as an automated quality c ontrol technique for rapid and precise classification of barleys accor ding to their malting qualities. The nineteen samples used in this stu dy were two-rowed winter type barleys. Six of these were assigned to G roup 1 and the rest was included into Group 2 according to their perfo rmance in a micro-malting system. Two sets of kernels from each barley sample were recorded. one for training and the other for testing purp ose. To be able to have functions yielding the best discrimination sco res in the training set, classification analysis was initially perform ed by the selection of each feature alone as an independent variable. Correct overall classification scores ranged between 52.9% and 78.1% w hen individual features were used in discriminant analyses. In the sec ond step, the combination of features which yielded the best classific ation 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.