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Joint application of Raman and optical absorption spectroscopy to determine concentrations of heavy metal ions in water using artificial neural networks

For many methods of optical spectroscopy, there is no analytical and/or direct numerical solution for the problem of determination of concentrations of each component in multi-component solutions by spectra. Therefore, recently, the application of machine learning methods to solve these problems has been actively investigated. In this study, it is suggested to use an ensemble of optical spectroscopy methods to increase the accuracy and noise resilience of the solution obtained by machine learning methods. We consider joint use of Raman spectroscopy and optical absorption spectroscopy methods to determine the concentrations of heavy metal ions in water. This complex inverse problem is solved by artificial neural networks as a machine learning method. It is demonstrated that when one of the methods is strong by its results, and the other is weak, their joint application does not allow one to improve the results of the strong method. Some other observations regarding the solution of the studied problem are reported.