Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods

Iron is one of the trace elements that plays a vital role in the human immune system, especially against variants of SARS-CoV-2 virus. Electrochemical methods are convenient for the detection due to the simplicity of instrumentation available for different analyses. The square wave voltammetry (SQWV) and differential pulse voltammetry (DPV) are useful electrochemical voltammetric techniques for diverse types of compounds such as heavy metals. The basic reason is the increased sensitivity by lowering the capacitive current. In this study, machine learning models were improved to classify concentrations of an analyte depending on the voltammograms obtained alone. SQWV and DPV were used to quantify the concentrations of ferrous ions (Fe+2) in potassium ferrocyanide (K4Fe(CN)(6)), validated by machine learning models for the data classifications. The greatest classifier algorithms models Backpropagation Neural Networks, Gaussian Naive Bayes, Logistic Regression, K-Nearest Neighbors Algorithm, K-Means clustering, and Random Forest were used as data classifiers, based on the data sets obtained from the measured chemical. Once competed to other algorithms models used previously for the data classification, ours get greater accuracy, maximum accuracy of 100% was obtained for each analyte in 25 s for the datasets.

Automated summary

Iron, as a trace element, has a crucial role in the human immune system, particularly in combating SARS-CoV-2 variants. Electrochemical methods, such as square wave voltammetry (SQWV) and differential pulse voltammetry (DPV), are convenient for detecting various compounds, including heavy metals, due to their simplicity. This study improved machine learning models to classify concentrations of an analyte based solely on obtained voltammograms. SQWV and DPV were used to quantify the concentrations of ferrous ions (Fe+2) in potassium ferrocyanide (K4Fe(CN)(6)), and the machine learning models validated the data classifications. The highest accuracy of 100% was achieved for each analyte in 25 seconds using our models, outperforming previously used algorithms for data classification.