Gas chromatography-mass spectrometry data analysis algorithm based on sparse model and its application in resolving severely overlapped peaks

Abstract

A Gas chromatography-mass spectrometry (GC-MS) data analysis algorithm is proposed. The mass spectrum at the top of the chromatographic peak is the spectrum to be solved. A certain amount of related reference spectra is retrieved from the spectral library, then, the equation of the chromatographic response value of each pure component is solved. A step by step strategy is used for the mass spectra retrieval. Firstly, an efficient indexing technology is used for rough selection, then, the "strong peak out with high probability" and the "extrusion" rules are used to exclude more unrelated mass spectra. A regression algorithm based on a sparse model is proposed to solve the equation of the chromatographic response value. Compared with the traditional algorithm, this algorithm can extract the main structure of the spectrum to be solved, and avoid over-fitting. The experimental results show that the proposed algorithm has a higher accuracy and smaller residual reference spectrum set, and the sparse model achieves satisfactory experimental results in the analysis of severely overlapped peaks. The proposed method provides an effective solution for resolving overlapped peaks, especially severely overlapped peaks, in GC-MS data.