Separation of Overlapping Voltammetric Peaks Based on Sharpening and Snake Optimisation Algorithms

Overlapping peaks are a common problem in studies of electrochemical detection of heavy metal ions, posing difficulties for qualitative and quantitative analysis of heavy metal ions. The overlapping voltammetric peaks can be regarded as the superposition of a series of Gaussian models, and the subpeaks can be separated by searching the optimal parameters of each model. In this paper, we propose an overlapping peak separation method based on the sharpening algorithm and snake optimisation algorithm, i.e., firstly, the sharpening algorithm is used to determine the number of sub-peaks and reduce the value space of three parameters, namely, peak height, peak position and peak width, so as to improve the convergence speed of the subsequent iterative optimisation and the accuracy of the overlapping peaks separation; and later, we use the snake optimisation algorithm to iterate on the initial parameters, and use the bi-paradigm number of the original overlapping peaks and the proposed overlapping peaks as the adaptive value to search for the global optimal solution, to search for the global optimal solution. The separation of simulated double- and triple-overlapping peaks is achieved by this separation method, and the results show that the relative error reductions of peak height, peak position and peak width after the separation of double-overlapping peaks by the sharpening algorithm can be up to 0.323%, 0.033% and 0.600%, and the relative error reductions of peak height, peak position and peak width after the separation of triple-overlapping peaks can be up to 4.106%, 1.600% and 7.756%, respectively. 7.756%. Therefore, after the sharpening algorithm reduces the value space, the separation accuracy for the overlapping of less than four peaks can be significantly improved, and the method is expected to be widely applied to the separation of overlapping voltammetric peaks.