As datasets are becoming larger, a solution to the problem of variable prediction, this
problem is becoming harder. The problem is to define which subset of variables produces …

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the
chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) …

Abstract Quantitative Structure-Retention Relationship (QSRR) methodology is a useful tool
in chromatography of all kinds, allowing the prediction of analyte retention time and …

In QSRR the retention is modeled as a function of structural or molecular descriptors. Since
the structural datasets can be very large a selection of informative variables is often …

The use of the classification and regression tree (CART) methodology was studied in a
quantitative structure–retention relationship (QSRR) context on a data set consisting of the …

We improve a recently developed Replacement Method (RM) for the selection of an optimal
set of molecular descriptors from a much greater pool of such regression variables. Our …

Basic chemometric methods for making empirical regression models for QSPR/QSAR are
briefly described from a user's point of view. Emphasis is given to PLS regression, simple …

In metabolomics, retention prediction methods have been developed based on the structural
and physicochemical characteristics of analytes. Such methods employ regression models …

Quantitative structure-retention relationships (QSRRs) have successfully been developed for
naturally occurring phenolic compounds in a reversed-phase liquid chromatographic …

The selection of the most relevant variable is a frequent problem in the analysis of chemical
data, especially now considering the large amounts of data created by the increased …