QSPR study on GC relative retention time of organic pesticides on different chromatographic columns

Abstract

In the present study, Lu index and distance-based atom type topological index (DAI) previously developed in our team, were introduced and combined with molecular electronegativity chi ep to characterize quantitative structure-property relationship of GC relative retention time (RRT) for several types of structurally diverse organic pesticides on the four kinds of chromatographic columns. Using multiple linear regression technique, four several-variable models are obtained with the estimations correlation coefficient (R(2)) being between 0.9655 and 0.9285, and the correlation coefficient (R(2)cv) in the leave-one-out cross-validation procedure are between 0.9560 and 0.9143, respectively. The results in this study indicate that the three topological indices Lu index, DAI, and molecular electronegativity chi ep can predict the gas chromatographic RRT of organic pesticides with diverse hetero-atoms.

An Urgent Need for Sustainable Agricultural Practices: A Review of Natural, Organic Ayurvedic and Homa Farming over Chemical Farming

“…Using multiple linear regression technique, four severalvariable models are obtained with the estimations correlation coefficient R (2) being between 0.9655 and 0.9285, and the correlation coefficient (R (2) cv) in the leave-one-out crossvalidation procedure are between 0.9560 and 0.9143, respectively. The results in this study indicate that the three topological indices Lu index, DAI, and molecular electronegativity chip can predict the gas chromatographic RRT of organic pesticides with diverse hetero-atoms [23].…”

Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography–mass spectroscopy

“…Ghasemi and his coworkers used multiple linear regression and partial least squares regression to QSRR study of the gas chromatography retention time of 38 diverse chlorinated pesticides, herbicides and organohalides by using molecular descriptors [14]. Hu et al developed a QSPR model based on multiple linear regression for the prediction of GC relative retention time of organic pesticides on four different chromatographic columns [15]. Hadjmohammadi et al estimated the retention time behavior of some pesticides on the DB-5 ms fused-silica column in gas chromatography using MLR and ANN methods [16].…”

Considerations for the automated collection of thermodynamic data in gas chromatography

“…Many models exist to predict specific gas chromatographic retention parameters such as retention index or relative retention for 1D GC separations. These models can be grouped into several different varieties, such as quantitative structure retention or property relationships (QSRRs or QSPRs) , additive models , and boiling‐point‐to‐retention‐time correlations . Kovats retention indices and linear temperature‐programed retention indices (LTPRI) are the most popular retention metrics to model .…”