In this study we propose a new approach to monitoring of the removal of luminescent nanocomposites and their components with urine using artificial neural networks. A complex multiparametric problem of optical imaging of synthesized nanocomposites - nanometer graphene oxides, covered by the poly(ethylene imine)–poly(ethylene glycol) copolymer and by the folic acid in a biomaterial is solved. The proposed method is applicable for optical imaging of any fluorescent nanoparticles used as imaging nanoagents in biological tissue.
The principle possibility of extraction of fluorescence of nanoparticles in the presence of background autofluorescence of a biological environment using neural network algorithms is demonstrated. It is shown that the methods used allow detection of carbon nanoparticles fluorescence against the background of the autofluorescence of egg white with a sufficiently low concentration detection threshold (not more than 2 μg/ml for carbon dots and 3 μg/ml for nanodiamonds). It was also shown that the use of the input data compression can further improve the accuracy of solving the inverse problem by 1.5 times.