Using method integration transfer learning for neural network solution of an inverse problem in optical spectroscopy

Igor Isaev; Ismail Gadzhiev; Olga Sarmanova; Sergey Burikov; Tatiana Dolenko; Kirill Laptinskiy; Sergey Dolenko

doi:10.1117/12.2626358

29 April 2022 Using method integration transfer learning for neural network solution of an inverse problem in optical spectroscopy

Igor Isaev, Ismail Gadzhiev, Olga Sarmanova, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy, Sergey Dolenko

Author Affiliations +

Proceedings Volume 12193, Laser Physics, Photonic Technologies, and Molecular Modeling; 121930Y (2022) https://doi.org/10.1117/12.2626358
Event: XXV Annual Conference Saratov Fall Meeting 2021; and IX Symposium on Optics and Biophotonics, 2021, Saratov, Russian Federation

Abstract

In the previous studies, the integration of optical spectroscopy methods was investigated in order to increase the accuracy of the solution obtained by machine learning methods. The joint use of Raman spectroscopy and optical absorption spectroscopy to determine the concentration of heavy metal ions in water by artificial neural networks was considered. Direct training of neural networks on the data of both types of spectroscopy did not allow us to improve the result in comparison with the individual use of absorption spectroscopy data. In this study, we consider the adaptation of transfer learning approach to the integration of optical spectroscopy methods, which consists in initial training of the neural networks on the data of only the weaker method (Raman spectroscopy), followed by additional training on the data of two methods (Raman and absorption spectroscopy).

Citation Download Citation

Igor Isaev, Ismail Gadzhiev, Olga Sarmanova, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy, and Sergey Dolenko "Using method integration transfer learning for neural network solution of an inverse problem in optical spectroscopy", Proc. SPIE 12193, Laser Physics, Photonic Technologies, and Molecular Modeling, 121930Y (29 April 2022); https://doi.org/10.1117/12.2626358

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