Abstract
An investigation to determine the feasibility of differentiating whole kernel samples of winter wheat cultivars ( Triticum aestivum L.) by pyrolysis-gas chromatography-pattern recognition (Py-GC-PR) is presented. Seed samples from five cultivars of winter wheat (three soft white winter wheats and two hard red winter wheats) from two growing seasons (1986 and 1987) were used for Py-GC-PR analysis. The results show that Py-GC provides characteristic fingerprint information for each of the five winter wheat cultivars. When this information was processed by unsupervised pattern recognition (multivariate cluster and display methods), the five cultivars could be clearly discriminated. Variations between wheat from the two growing seasons were found to be as large as between cultivar variation, indicating the importance of seasonal growing conditions on classification. When supervised pattern recognition (discriminant function analysis) was applied to the data, the samples could be correctly classified to their respective cultivars regardless of the growing season, thus demonstrating the potential of Py-GC-PR for differentiating/classifying cultivars of winter wheat.
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