Fluorescence Excitation‐Emission Matrices Decomposition Using Statistical Correction for Primary Inner Filtering

ABSTRACT Multi‐excitation fluorescence spectroscopy allows characterizing the distribution of fluorophores in samples of various natures and is a powerful tool for chemical, biological, and medical diagnostics. However, interpreting large sets of excitation‐dependent fluorescence spectra remains a significant challenge, especially when the fluorophores are initially unknown. In optically thin samples, spectral decomposition can be performed using several statistical methods. In practice, however, various light propagation effects cause deviations from the pure component spectra and prevent the direct application of these methods. A sample‐specific inner filtering correction is required prior to their use. Unfortunately, performing such a correction is not always feasible due to complex light propagation geometry or the difficulty of obtaining additional measurements. Here, we suggest an indirect procedure for optically thick samples with negligible fluorescence reabsorption that does not rely on any assumptions about the light illumination and collection geometry, by iteratively determining appropriate correction coefficients for excitation variations. Due to the correlated nature of fluorescence spectra in excitation‐emission matrices, this approach enables decomposition when other methods fail. Model experiments with simulated spectral data and real fluorophores showed good agreement between the calculated spectra and the actual fluorescence spectra of the corresponding substances.