Application of the Racial Algorithm in Energy Dispersive X-Ray Fluorescence Overlapped Spectrum Analysis

In the energy dispersive X-ray fluorescence spectrum analysis, scintillation detector such as NaI (Tl) detector usually has a low energy resolution at around 8%. The low energy resolution causes problems in spectral data analysis especially in the high background and low counts condition, it is very limited to strip the overlapped spectrum, and the more overlapping the peaks are, the more difficult to peel the peaks, and the qualitative and quantitative analysis can't be carried out because we can't recognize the peak address and peak area. Based on genetic algorithm and immune algorithm, we build a new racial algorithm which uses the Euclidean distance as the judgment of evolution, the maximum relative error as the iterative criterion to be put into overlapped spectrum analysis, then we use the Gaussian function to simulate different overlapping degrees of the spectrum, and the racial algorithm is used in overlapped peak separation and full spectrum simulation, the peak address deviation is in +/- 3 channels, the peak area deviation is no more than 5%, and it is proven that this method has a good effect in energy dispersive X-ray fluorescence overlapped spectrum analysis.