ABSTRACT
A new, to the best of our knowledge, internal reference method has been developed for the study of the upconversion luminescence of nanoparticle suspensions. This method provides correct analysis and comparison of the luminescent signals obtained under different conditions. To excite the echo signals of samples, it is proposed to use the radiation from an optical parametric oscillator at two wavelengths for the simultaneous excitation of the upconversion luminescence of particles and the Raman scattering signal of the medium in the Stokes region of the spectrum. Due to the linear dependence of the intensity of the Raman scattering of the medium on the excitation power density, the normalization of the upconversion luminescence signal of particles to the intensity of the Raman scattering of the medium makes it possible to eliminate the influences of the instability of the intensity of the laser radiation, light scattering by the medium, inaccuracies in alignment, etc. on the luminescence signal.
ABSTRACT
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.
