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

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).