Søndergaard I, Jensen K, Krath B N
Department of Biochemistry and Nutrition, Technical University of Denmark, Lyngby.
Electrophoresis. 1994 May;15(5):584-8. doi: 10.1002/elps.1150150181.
Classification of wheat varieties, using isoelectric focusing patterns of the gliadins, image processing and neural networks, is described. The method was compared to a statistical classification method, discriminant analysis. The isoelectric point and the area of each band were calculated by image processing. Different methods of presenting the electrophoretic patterns to the neural network were studied. The most effective method was transformation of the electrophoretic pattern to a small (11 x 47 pixels) representation of the original digitized image, which was presented to the neural network as a vector. The neural network was trained with a number of patterns and tested with new patterns from different electrophoretic runs of the same wheat varieties. In this study we used ten different wheat varieties and the neural network was able to classify 95.5% of the patterns correctly. The statistical classification method classified the same data set 91.8% correctly. We conclude that both the neural network and discriminant analysis were able to classify the patterns correctly with a high degree of certainty. The patterns that were misclassified were indistinguishable by visual inspection.
本文描述了利用醇溶蛋白的等电聚焦图谱、图像处理和神经网络对小麦品种进行分类的方法。该方法与统计分类方法判别分析进行了比较。通过图像处理计算每个条带的等电点和面积。研究了将电泳图谱呈现给神经网络的不同方法。最有效的方法是将电泳图谱转换为原始数字化图像的小尺寸（11×47像素）表示形式，并作为向量呈现给神经网络。神经网络用多个图谱进行训练，并用来自相同小麦品种不同电泳运行的新图谱进行测试。在本研究中，我们使用了10个不同的小麦品种，神经网络能够正确分类95.5%的图谱。统计分类方法对同一数据集的正确分类率为91.8%。我们得出结论，神经网络和判别分析都能够高度准确地正确分类图谱。通过目视检查无法区分被错误分类的图谱。
