Resolving severely overlapping ion mobility peaks using enhanced Fourier self-deconvolution

Abstract

Fourier self-deconvolution is an effective method for resolving overlapping spectra. However, the selection of the half-width for the deconvolving function is often subjective, which can lead to either excessive convolution or insufficient resolution enhancement. Additionally, ion mobility peaks exhibit tailing effects, which may be misinterpreted as new peaks when the deconvolving function is modelled with a Gaussian function. This paper proposes an improved Fourier self-deconvolution method based on continuous wavelet transform. The proposed method determines the signal half-width by calculating the horizontal distance between the peaks and troughs of the wavelet coefficients, offering a more accurate estimation. Furthermore, an asymmetric function is employed to optimize the peak shape of the deconvolving function, significantly reducing the likelihood of peak misidentification. The effectiveness of the proposed method is validated using both simulated and experimental ion mobility spectrometry data. Experimental results demonstrate that the proposed method effectively enhances peak resolution and resolves overlapping peaks. Moreover, compared with other peak segmentation algorithms based on Fourier self-deconvolution, the proposed method demonstrates lower parameter estimation error and higher computational efficiency, particularly for severely overlapping peaks.