کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9748557 | 1493784 | 2005 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Chromatography A - Volume 1096, Issues 1â2, 25 November 2005, Pages 187-198
Journal: Journal of Chromatography A - Volume 1096, Issues 1â2, 25 November 2005, Pages 187-198
نویسندگان
R. Put, E. Van Gyseghem, D. Coomans, Y. Vander Heyden,