Put R, Van Gyseghem E, Coomans D, Vander Heyden Y
Department of Pharmaceutical and Biomedical Analysis, Pharmaceutical Institute, Vrije Universiteit Brussel-VUB, Laarbeeklaan 103, B-1090 Brussels, Belgium.
J Chromatogr A. 2005 Nov 25;1096(1-2):187-98. doi: 10.1016/j.chroma.2005.03.138.
In order to select chromatographic starting conditions to be optimized during further method development of the separation of a given mixture, so-called generic orthogonal chromatographic systems could be explored in parallel. In this paper the use of univariate and multivariate regression trees (MRT) was studied to define the most orthogonal subset from a given set of chromatographic systems. Two data sets were considered, which contain the retention data of 68 structurally diversive drugs on sets of 32 and 38 chromatographic systems, respectively. For both the univariate and multivariate approaches no other data but the measured retention factors are needed to build the decision trees. Since multivariate regression trees are used in an unsupervised way, they are called auto-associative multivariate regression trees (AAMRT). For all decision trees used, a variable importance list of the predictor variables can be derived. It was concluded that based on these ranked lists, both for univariate and multivariate regression trees, a selection of the most orthogonal systems from a given set of systems can be obtained in a user-friendly and fast way.
为了在给定混合物分离的进一步方法开发过程中选择待优化的色谱起始条件，可以并行探索所谓的通用正交色谱系统。本文研究了单变量和多变量回归树（MRT）的使用，以从给定的一组色谱系统中定义最正交的子集。考虑了两个数据集，分别包含68种结构多样的药物在32个和38个色谱系统上的保留数据。对于单变量和多变量方法，构建决策树只需要测量的保留因子，不需要其他数据。由于多变量回归树是以无监督的方式使用的，因此它们被称为自关联多变量回归树（AAMRT）。对于所有使用的决策树，可以得出预测变量的变量重要性列表。得出的结论是，基于这些排名列表，无论是单变量还是多变量回归树，都可以以用户友好且快速的方式从给定的一组系统中选择最正交的系统。
