An effective spectral unmixing algorithm for flow cytometry based on GA and least squares.

ABSTRACT

Spectral unmixing algorithm is one of the key technologies for spectral flow cytometer in biology, chemistry and medicine. The proposed algorithm can separate the overlapping spectra automatically without the premeasured single stained or un-stained samples as the basic pure spectra. Genetic algorithm is adopted to search the optimal positions and peak sharps of the basic spectra derived from the unknown components, and then the concentration of each component can be estimated simultaneously by least squares method. Compared with conventional methods, the proposed algorithm has a wider application scope, such as the multi-stained samples with unknown components or the samples with auto-fluorescence. In the simulation, the convergence rate, accuracy and stability of the proposed algorithm are evaluated under the conditions of completely and partly unknown components. In the experiment, the flow spectra of cyanobacteria are processed, and the results demonstrate the feasibility and effectiveness of the proposed algorithm.