Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees
Put, R., Van Gyseghem, E., Coomans, D., and Vander Heyden, Y. (2005) Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees. Journal of Chromatography A, 1096 (1-2). pp. 187-198.
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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.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||auto-associative multivariate regression; orthogonal reversed-phase HPLC systems; orthogonal chromatographic systems; CART; univariate regression trees; multivariate regression trees; method development; unsupervised|
|Date Deposited:||12 Jun 2009 05:29|
|FoR Codes:||01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
03 CHEMICAL SCIENCES > 0301 Analytical Chemistry > 030106 Quality Assurance, Chemometrics, Traceability and Metrological Chemistry @ 50%
|SEO Codes:||97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%|
|Citation Count from Web of Science||