Thus, the task of eliminating the influence of optical properties on the method of analyzing the concentration of PS by fluorescence spectroscopy, as well as its analysis in the extended dynamic range, is very important. This problem has not been solved in a complex, but there are studies devoted to the solution of individual problems [
4–
6]. Currently available correction techniques fall into three broad categories: empirical techniques, measurement method-based techniques, and theory-based techniques [
4]. Empirical attenuation correction techniques use reflectance information to subtract or divide fluorescence signal from the reflectance signal [
7,
8]; spatially resolved reflectance, assuming the concentration of an exogenous fluorophore, can be recovered by determining the fluorescence–reflectance ratio at a specific distance [
9], or taking а ratio of fluorescence intensities at two emission wavelengths can be used to correct for changes in blood volume and oxygenation [
10]. Measurement method-based techniques use nontrivial approaches for signal registration such as confocal detection, single-fiber detection with a fiber radius of <100 µm, using polarized excitation light, and measuring the fraction of fluorescence and reflectance that retains polarization. This group of methods has a clear limitation in the area of tissue sample analysis. The information obtained from such small volume or depth can be non-relevant for the whole picture of metabolic changes and fluorophore concentration in the vicinity of this volume. Theory-based attenuation correction techniques use approximations to the energy transfer equation, i.e., modified Beer–Lambert law, Kubelka–Munk theory, and diffusion theory or Monte Carlo simulation techniques to eliminate the absorption and scattering effect on fluorescence signal. In this study, we combine empirical and theory-based techniques and use hardware capabilities. On the hardware level, it is necessary to take into account that the dynamic range of the spectrum analyzer is mainly determined by the characteristics of the matrix photodetector. At high levels of incident light on the cell of matrix photodetector, this cell and adjacent cells may undergo charge saturation; at low levels, the signal associated with the incident light may be hardly distinguishable against the background of hardware noise of the device (first of all, the noise of the matrix photodetector). Therefore, the dynamic range of known devices does not exceed two orders of magnitude. This can lead to the distortion of the shape of the spectra and to the dependence of their intensity on the content of PS in the studied biological tissue, which prevents the study of metabolic (biochemical) transformations of the PS, its pharmacokinetics, and biodistribution. One of the objectives of this study is to expand the dynamic range of a spectrum analyzer for pharmacokinetic and biodistribution studies.