NANOSYSTEMS: PHYSICS, CHEMISTRY, MATHEMATICS, 2014, 5 (1), P. 195–202
USING ARTIFICIAL NEURAL NETWORKS FOR ELABORATION OF FLUORESCENCE BIOSENSORS ON THE BASIS OF NANOPARTICLES
S. A. Burikov – Moscow M.V. Lomonosov State University, Physics Department, Moscow, Russia; burikov@lid.phys.msu.ru
S.A. Dolenko – D. V. Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, Russia; dolenko@srd.sinp.msu.ru
K.A. Laptinskiy – Moscow M.V. Lomonosov State University, Physics Department, Moscow, Russia; laptinsky@lid.phys.msu.ru
I.V. Plastinin – Moscow M.V. Lomonosov State University, Physics Department, Moscow, Russia
A. M. Vervald – Moscow M.V. Lomonosov State University, Physics Department, Moscow, Russia; vervald@lid.phys.msu.ru
I.I. Vlasov – General Physics Institute RAS, Moscow, Russia; vlasov@nsc.gpi.ru
T.A. Dolenko – Moscow M.V. Lomonosov State University, Physics Department, Moscow, Russia; tdolenko@lid.phys.msu.ru
In this study, the results for the solution of the pattern recognition problem are presented — extraction of fluorescence contribution for carbon dots used as biomarkers from the background signals of natural fluorophores and the determination of relative nanoparticle concentration. To solve this problem, artificial neural networks were used. The principal opportunity for solution of the given problem was demonstrated. The used architectures for neural networks allow the detection of carbon dot-based fluorescence within the background of native fluorescent egg protein with sufficiently high accuracy (not lower than 0.002 mg/ml).
Keywords: fluorescence, carbon dots, biomarkers, egg protein, autofluorescence, artificial neural networks.
PACS 61.46.+w, 87.64.-t, 07.05.Kf, 87.85.fk