The Relationship Between the Thin Layer Chromatographic Retention Values and the Molecular Structures of a Group of Amino Acids by Using Back Propagation Artificial Neural Networks
反向传播人工神经网络用于薄层色谱中氨基酸的结构与保留关系的研究

The relationship between the thin layer chromatographic retention values and the molecular structures of fifteen amino acids was studied by using back propagation artificial neural networks (ANNs). In this paper, firstly, lots of parameters of amino acids have been determined, accumulated and computed. Then, correlation coefficients of all parameters were computed by taking advantage of correlation analysis. Taking the correlation coefficient approaching one as the criteria in the correlation analysis, all parameters were classified into three kinds. Three parameters were selected from each kind, respectively, to consist of one group. Then, the optimized groups of parametes, which have clearer physicochemical meanings, were used as the inputting parameters of artificial neural networks. Correlation coefficients of experimental retardation values and those calculated by using ANNs were computed and showed good agreement. The present work shows that the ANNs method may take an important role in the study of the relationship between TLC behavior and compound structure.