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...

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Bibliographic Details
Published in:The Journal of supercomputing 2023, Vol.79 (11), p.12472-12491
Main Authors: Kayali, Devrim, Shama, Nemah Abu, Asir, Suleyman, Dimililer, Kamil
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Classification
Classifiers
Cluster analysis
Clustering
Compilers
Computer Science
Ferrous ions
Heavy metals
Immune system
Interpreters
Iron cyanides
Machine learning
Neural networks
Potassium
Processor Architectures
Programming Languages
Square waves
Trace elements
Vector quantization
Voltammetry
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Summary: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 (K 4 Fe(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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05137-y