Comparison of ethanol-soluble proteins from different rye (Secale cereale) varieties by two-dimensional electrophoresis.

The major storage proteins from six rye varieties, grown under the same conditions in 1997 and 1998 in Rønhave, Denmark, were analyzed by two-dimensional (2-D) polyacrylamide gel electrophoresis. The proteins were extracted from ground rye kernels with 70% ethanol and separated by 2-D electrophoresis. The gels were scanned, compared using ImageMaster software and the data sets were analyzed by principal component analysis (PCA) using THE UNSCRAMBLER software. Afterwards MATLAB was used to make a cluster analysis of the varieties based on PCA. The analysis of the gels showed, that the protein patterns (number of different proteins and their isoelectric points and molecular weights) from the six rye varieties were different. Based on the presence of unique cultivar-specific spots it was possible to differentiate between all six varieties if the two harvest years were investigated separately. When the results were combined from the two years five varieties could be differentiated. The results from the PCA confirmed the finding of the unique spots and cluster analysis was made in order to illustrate the results. The combination of the results from 2-D electrophoresis and other grain characteristics showed that one protein spot was located close to the parameters bread volume and bread height.

Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis.

The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.