Artificial Neural Network (ANN)-Based Determination of Fractional Contributions from Mixed Fluorophores using Fluorescence Lifetime Measurements.

ABSTRACT: Here we present an artificial neural network (ANN)-approach to determine the fractional contributions P_i from fluorophores to a multi-exponential fluorescence decay in time-resolved lifetime measurements. Conventionally, P_i are determined by extracting two parameters (amplitude and lifetime) for each underlying mono-exponential decay using non-linear fitting. However, in this case parameter estimation is highly sensitive to initial guesses and weighting. In contrast, the ANN-based approach robustly gives the P_i without knowledge of amplitudes and lifetimes. By experimental measurements and Monte-Carlo simulations, we comprehensively show that accuracy and precision of P_i determination with ANNs and hence the number of distinguishable fluorophores

SUBMITTER: Netaev A 

PROVIDER: S-EPMC10808714 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature