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Integration of Data from Various Physical Methods in Solving Inverse Problems of Spectroscopy by Machine Learning Methodsстатья Исследовательская статья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.
- Авторы: Guskov Artem, Isaev Igor, Burikov Sergey, Dolenko Tatiana, Laptinskiy Kirill, Dolenko Sergey
- Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research VII
- Серия: Studies in Computational Intelligence
- Том: 1120
- Год издания: 2023
- Издательство: Springer Nature
- Местоположение издательства: Switzerland
- Первая страница: 445
- Последняя страница: 454
- DOI: 10.1007/978-3-031-44865-2_47
- Аннотация: This article presents the results of solving an inverse problem in spectroscopy using integration of optical spectroscopy methods. The studied inverse problem is determining the concentrations of heavy metal ions in multicomponent solutions by Raman spectra, infrared spectra and optical absorption spectra. It is shown that the joint use of data from various physical methods make it possible to reduce the error of spectroscopic determination of concentrations. If the integrated methods differ significantly by their accuracy, then their integration is not effective. These effects are observed using various machine learning methods: random forest, gradient boosting and artificial neural networks – multilayer perceptrons. A series of experiments with solutions based on river water are also performed to estimate the variability of the fluorescence of natural waters in Moscow. A significant increase in the error level relative to solutions prepared in distilled water is observed. This indicates the need to develop new methods to improve the quality of solution of the investigated problem for diagnostics of real river waters.
- Добавил в систему: Доленко Сергей Анатольевич