In the present work, an artificial neural network (ANN) model was used to study the quantitative structure retention relationship (QSRR) of retention index (RI) of some volatile compounds in natural cocoa and conched chocolate powder. Molecular structural descriptors are selected using genetic algorithm to construct the nonlinear QSRR models, kernel partial least squares PLS (KPLS) and Levenber...

In this paper, a novel approach is described for the a priori prediction of protein retention in ion exchange systems. Quantitative structure retention relationship (QSRR) models based on a genetic algorithm/partial least squares approach were developed using experimental chromatographic data in concert with molecular descriptors computed using protein crystal structures. The resulting QSRR mod...

Quantitative structure–retention relationships (QSRR) have been derived for prediction of RP-HPLC retention of peptides obtained by online digestion of myoglobin. To characterize the structure of a peptide quantitatively, and then to predict its retention time under gradient HPLC conditions, the structural descriptors used were: the logarithm of the sum of retention times of the amino acids of ...

In this study, quantitative structure-retention relationship (QSRR) methodology employed for modeling of the retention times of 16 banned pesticides in nano-liquid chromatography (nano-LC) column. Genetic algorithm-multiple linear regression (GA-MLR) method employed for developing global and consensus QSRR models. The best global GA-MLR model was established by adjusting GA parameters. Three de...

Gas chromatography (GC) and two-dimensional GC (GC-GC) are widely used for separation, structure elucidation and quantitative analysis. In GC and GC-GC, the chemical structure is elucidated by comparing the measured retention time of each compound with the database. But, structure elucidation is infeasible when the retention time is not available from the database. Thus, quantitative structure ...

The performance of several previously designed model series of test analytes has been tested to characterize in an objective, quantitative manner modern stationary phases for reversed-phase high-performance liquid chromatography (RP-HPLC) using quantitative structure-retention relationships (QSRRs). Three QSRR approaches and three respective series of test analytes recommended for studies of th...

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromatographic retention time to the chemical structure of a solute. A QSRR study has been carried out on the reversed-phase high-performance liquid chromatography retention times (log tR) of 62 diverse drugs (painkillers) by using molecular descriptors. Multiple linear regression (MLR) is u...

This study examines the effect of different salt types on protein retention and selectivity in anion exchange systems. Particularly, linear retention data for various proteins were obtained on two structurally different anion exchange stationary-phase materials in the presence of three salts with different counterions. The data indicated that the effects are, for the most part, nonspecific, alt...

The retention time of 23 barbituric acid derivatives was determined on an narrow-bore porous graphitized carbon (PGC) column employing water-dioxane mixtures as mobile phases. The retention factor (k), theoretical plate number (N), and asymmetry factor (AF) were computed for each analyte in each mobile phase. Quantitative structure-retention relationship (QSRR) calculations using stepwise regre...

Organic compounds containing heteroatoms or multiple bonds can be conveniently represented as vertex- and edge-weighted molecular graphs. These atom and bond parameters can be computed for any organic compound with two parameter sets that we have recently defined, namely, the relative electronegativity X and the relative covalent radius Y weighting schemes. Structural descriptors computed with ...