Baseline Correction Algorithm for Mass Spectrometry Based on Wavelet Transform

The mass spectrometer is an accurate measurement instrument widely used in life sciences, food safety, environmental monitoring, industrial analysis, state security and other fields. Data processing is the critical link that affects the analysis results of mass spectrometry. In order to reduce the baseline drift during data acquisition, a baseline correction algorithm based on wavelet transform is proposed. Firstly, carry out single-layer wavelet decomposition to the original mass spectral signal several times, perform reconstruction to single-layer wavelet at the same time, calculate the signal-to-noise ratio of each single-layer, and acquire the mass spectral signal after noise reduction through the signal-to-noise ratio comparison method. Secondly, carry out single-layer wavelet decomposition to this signal several times to obtain wavelet details and the wavelet approximated frequency of each layer, divide the two frequencies to get the ratio and compare the ratio values of each layer, select the layer with the largest value as the best layer of wavelet decomposition. Finally, reset the wavelet approximated coefficient under the best layer of wavelet decomposition to zero and carry out wavelet reconstruction to obtain the mass spectral signal after baseline correction. Through the verification of experiments, this algorithm can precisely get the best layer of wavelet decomposition. Based on the retaining of real mass spectral signal as much as possible, remove the distribution of low frequency mass spectral baseline and high frequency noises; the baseline correction is sufficient and good results are achieved.