Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples

Journal of Chromatography. a
M LopatkaGabriel Vivó-Truyols

Abstract

Accurate analysis of chromatographic data often requires the removal of baseline drift. A frequently employed strategy strives to determine asymmetric weights in order to fit a baseline model by regression. Unfortunately, chromatograms characterized by a very high peak saturation pose a significant challenge to such algorithms. In addition, a low signal-to-noise ratio (i.e. s/n<40) also adversely affects accurate baseline correction by asymmetrically weighted regression. We present a baseline estimation method that leverages a probabilistic peak detection algorithm. A posterior probability of being affected by a peak is computed for each point in the chromatogram, leading to a set of weights that allow non-iterative calculation of a baseline estimate. For extremely saturated chromatograms, the peak weighted (PW) method demonstrates notable improvement compared to the other methods examined. However, in chromatograms characterized by low-noise and well-resolved peaks, the asymmetric least squares (ALS) and the more sophisticated Mixture Model (MM) approaches achieve superior results in significantly less time. We evaluate the performance of these three baseline correction methods over a range of chromatographic conditions to dem...Continue Reading

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Citations

Sep 25, 2018·Journal of Analytical Methods in Chemistry·Long ChenZhuo Chen
Feb 26, 2020·Journal of Separation Science·Tijmen S BosBob W J Pirok
Apr 15, 2021·Journal of Chromatography. a·Alexander KensertDeirdre Cabooter

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