Multicomponent fluorescence lifetime and component proportion estimation method for TCSPC (time-correlated single photoncounting)

Abstract

The invention relates to a multicomponent fluorescence lifetime and component proportion estimation method applied to TCSPC (time-correlated single photoncounting). Firstly, acquired histogram data are combined to form a new histogram with the smaller interval, so that the influence of partial noise is eliminated, and the computation burden of the algorithm is greatly reduced. Autocorrelation computation is performed on interval data of the new histogram, and an autocorrelation matrix is smoothed according to characteristics of TCSPC. Eigenvalue decomposition is performed on the autocorrelation matrix, and signal subspace is obtained. According to the possible fluorescence life span, a search vector is formed, the fluorescence lifetime is estimated according to a searched spectral peak position, and content of various components is estimated with the least square method. With the adoption of the method, fluorescence lifetime information of all the components can be computed in one step without knowing general fluorescence lifetime information and general component content information of the multiple components.

Description

technical field [0001] The invention is a method for estimating multi-component fluorescence lifetime and component ratio for time-correlated single photon counting, the main function realized is to estimate the fluorescence lifetime and component ratio of each component from the data of time-correlated single-photon technology system . Background technique [0002] Time-correlated single photon counting Time-correlated single photon counting (TCSPC) was first established by Bollinger, Bennett, and Koechlin in the 1960s to detect the luminescence of scintillators excited by rays, and was gradually applied to the measurement of fluorescence lifetime. The advantages of TCSPC technology are as follows: high time resolution, high sensitivity, high measurement accuracy, large dynamic range, digital output data, and easy computer storage and processing. It has been widely used in modern physics, chemistry, biology and other fields, especially in time-resolved laser scanning micro...

AI Technical Summary

Problems solved by technology

Due to the huge amount of calculation of the fitting method, and it is easy to converge to the local extremum, the credibility of the estimation result is poor

In recent years, the Prony method, IEM method, CMM method, and Phasor method have been developed with a small amount of computation, but most of these methods are suitable for single components. The algorithm uses an approximate method, and there is a deviation in the estimated result, so the result needs to be corrected

Images