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Quick prediction of the retention of solutes in 13 thin layer chromatographic screening systems on silica gel by classification and regression trees
Author(s) -
Komsta Łukasz
Publication year - 2008
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.200800237
Subject(s) - context (archaeology) , linear regression , retention time , calculator , regression analysis , chromatography , regression , chemistry , decision tree , mathematics , computer science , statistics , data mining , paleontology , biology , operating system
The use of classification and regression trees (CART) was studied in a quantitative structure–retention relationship (QSRR) context to predict the retention in 13 thin layer chromatographic screening systems on a silica gel, where large datasets of interlaboratory determined retention are available. The response (dependent variable) was the rate mobility (RM) factor, while a set of atomic contributions and functional substituent counts was used as an explanatory dataset. The trees were investigated against optimal complexity (number of the leaves) by external validation and internal crossvalidation. Their predictive performance is slightly lower than full atomic contribution model, but the main advantage is the simplicity. The retention prediction with the proposed trees can be done without computer or even pocket calculator.