The study considers the application of artificial neural networks to solve the inverse problem of fluorescence (FL) spectroscopy for monitoring the elimina

A solution of spectroscopic inverse problems, implying determination of target parameters of the research object via analysis of spectra of various origins, is an overly complex task, especially in case of strong variability of the research object. One of the most efficient approaches to solve such tasks is use of machine learning (ML) methods, which consider some unobvious information relevant to the problem that is present in the data. Here, we compare ML approaches to the problem of nanocomplex concentrations determination in human urine via optical absorption spectra, perform preliminary analysis of the data array, find optimal parameters for several of the most popular ML methods, and analyze the results.

In this study, to create a carbon dots-based multimodal nanosensor of metal ions, a new approach to solving the inverse problem of fluorescence spectroscop

This study is devoted to the problem of controlling the removal of carbon nanocomplexes from the body via neural networks. We model the renal excretion of