Initial development of a linear regression model to determine the copper (ii) ion content via a photometric method

Abstract

Introduction: This study, which was first conducted in Vietnam, aimed to develop a multivariable and simple-variable linear regression model from the direct measurement of the UV‒Vis absorption of copper(II) ions in aqueous solution without using other reagents (chelating agents and solvents), which reduces environmental pollution and analysis fees.

Methods: Simple-variable and multivariable linear regression models were developed from UV‒Vis spectral data of copper(II) ion solutions with concentrations ranging from 0.2 to 50 ppm.

Results: Four multivariable regression models were developed and modified, and the optimal simple variable regression model was selected. This study analyzed the suitability of single and multivariable models for the analysis of copper(II) ions in aqueous solution at low concentrations.

Conclusion: This study successfully built and adjusted linear regression models for predicting the copper(II) ion content in aqueous solution via a photometric method. The multivariable model with odd variables (model No. 2’) and the simple-variable model at a wavelength of 221 were optimized for use in the prediction of the concentration at an acceptable level of 0.5 ppm. These results were verified by the graph of the correlation between the true concentration and the predicted concentration in both selected models. In particular, the multivariate model yields significantly more accurate prediction results than does the simple-variable model.