Determining optimal ambient ionization mass spectrometry data pre-processing parameters in neurosurgery

Radical tumor resection is still the most effective treatment method for brain tumors. The problems of intraoperative monitoring are currently solved using positron emission tomography, magnetic resonance imaging, and histochemical analysis, however, these require using expensive equipment by highly qualified personnel and are therefore still not widely available. As an alternative, it is possible to use mass spectrometry methods without sample preparation and then the analysis of mass spectrometry data involving the use of machine learning methods. The spectra that are more rich and diverse in terms of peak number are typical for mass spectrometry without sample preparation, therefore the use of this method requires specific pre-processing of experimental data. The study was aimed to develop the methods to determine the optimal parameter values for pre-processing of the data acquired by ambient ionization mass spectrometry. The paper presents two such methods and provides specific parameter values for the data acquired using the Thermo LTQ XL Orbitrap ETD mass spectrometer.