Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

Abstract

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.