Unsupervised adaptive filter for baseline thresholding and elimination in liquid chromatography-mass spectrometry via approximation of the standard deviation of baseline distribution in retention time domain

The omitting presence of a baseline (background, systemic noise, mobile phase) in liquid chromatography-mass spectrometry (LC-MS) measurements impedes objective analysis. Therefore, there is a demand to remove its contribution from the signal response. Elimination of baseline contribution is justified by increasing the data mining output, both qualitatively and quantitatively. Behavior of the baseline content is not perfectly constant on the time axis, and it is often necessary to experiment with gradient changes. However, the behavior could be parametrized using a technique derived from statistical moments. In this article, we propose adaptive thresholding as an unsupervised method for baseline removal from the measurement data. Results of a real analyte measurement are discussed to illustrate its efficiency.