Adaptive micro-area x-ray spectroscopy peak finding based on symmetric zero-area method

In micro-area spectroscopic analysis, precise identification of characteristic X-ray peak positions holds paramount importance. Analyzing the energy information of these characteristic X-ray peaks, enables the determination of elements types present in the sample and their relative abundances. However, spectral graphs often present intricate challenges such as overlapping, weak spurious peaks, complicating the peak-finding process significantly. To tackle these hurdles in micro-area X-ray spectroscopy, this paper proposes an adaptive threshold symmetric zero-area peak-finding algorithm. In comparison to traditional methods like the simple comparison method, derivative method, and Gaussian fitting method, the symmetric zero-area peak-finding algorithm demonstrates superior advantages in detecting overlapping, weak, and spurious peaks. Through peak-finding experiments conducted on original spectral data from various samples, and comparing results with the standard characteristic X-ray element tables and other classical algorithms, it’s evident that symmetric zero-area algorithm not only accurately distinguish each individual peak but also effectively distinguishes real peaks from noise-induced false peaks, proving feasibility of the symmetric zero-area peak-finding algorithm in micro-area spectroscopy.