Probability in HPLC-MS Metabolomics

Abstract

This article is pinpointing the importance of the probabilistic methods for the analysis of the HPLC-MS measurement datasets in metabolomics research. The approach presents the ability to deal with the different noise sources and the process of the probability assignment is demonstrated in its general form. The illustrative examples of the probability functions and propagation into subsequent processing and analysis steps consist of precision correction, noise probability, segmentation, spectra comparison, and biomatrices effects on calibration curve estimation. The possible advantages of probability propagation in more data handling are also discussed.