Stochastic Bernstein (SB) approximation can tackle the problem of baseline drift correction of instrumentation data.This is demonstrated for spectral data: matrix-assisted Raised Toilet Seats laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) data.Two SB schemes for removing the baseline drift are presented: iterative and direct.Following an explanation of the origin of the MALDI-TOF baseline drift that sheds light on the inherent difficulty of its removal by chemical means, SB baseline drift removal is illustrated for both proteomics and genomics MALDI-TOF data sets.SB is an elegant signal processing method to obtain a numerically straightforward baseline shift removal method as it includes a free parameter that can be optimized for Compact Steam Oven different baseline drift removal applications.
Therefore, research that determines putative biomarkers from the spectral data might benefit from a sensitivity analysis to the underlying spectral measurement that is made possible by varying the SB free parameter.This can be manually tuned (for constant ) or tuned with evolutionary computation (for ).