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Updated: Jul 11, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from
Jahanbakhsh Ghasemi1, Shahin Ahmadi
1Chemistry Department, Faculty of Sciences, Razi University, Kermanshah, Iran. jahan.ghasemi@gmail.com
Abstract:
Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study, a genetic algorithm (GA) was applied as a variable selection method in QSPR analysis. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that PLS has better prediction ability than MLR.
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