Heavy Metals Potentiometric Sensitivity Prediction by Firefly-Support Vector Machine Modeling Method

Pourbasheer, Eslam; Mahmoudzadeh Laki, Reza; Sarafraz Khalifehlou, Mohammad

doi:10.22034/abec.2024.715433

Heavy Metals Potentiometric Sensitivity Prediction by Firefly-Support Vector Machine Modeling Method

Document Type : Original Article

Authors

Eslam Pourbasheer

Reza Mahmoudzadeh Laki

Mohammad Sarafraz khalifehlou

Department of Chemistry, Faculty of Science, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran

10.22034/abec.2024.715433

Abstract

The quantitative structure-property relationship (QSPR) method is an efficient and elegant method for estimating the critical parameters of a wide range of compounds. In this work, the QSPR data set included the structures of 45 modified diphenyl phosphoryl acetamide ionophores along with their sensitivity to Cd²⁺, Cu²⁺, and Pb²⁺. The data set was divided into the training set, including 36 compounds, and the test set, including 9 compounds. The stepwise -multiple linear regressions (SW-MLR), firefly multiple linear regressions (FA-MLR), and firefly-support vector machine (FA-SVM) models were produced on the training set with sensitivity of ionophores for Cd²⁺, Cu²⁺, and Pb²⁺ for predicting the potentiometric sensitivity of plastic polymer membrane sensors. The FA-SVM model showed good statistical results for all three cations. Internal and external validation was done to ensure the performance of the model. The results showed acceptable accuracy of the proposed method in identifying important descriptors in QSPR. The results of this study and the interpretation of the descriptors entered in the model can help to design new selective ligands.

Keywords

Ion-selective electrode

Heavy metals

QSPR

FireFly

Support vector machine

Subjects