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摘要
A hovel method for compound identification in liquid chromatography high resolution mass spectrometry (LC-HRMS) is proposed. The method, based on Bayesian statistics, accommodates all possible uncertainties involved, from instrumentation, lion up to data analysis into a single model yielding the probability of the compound of interest being present/absent in the sample. This approach differs from the classical methods in two ways. First, it is probabilistic (instead of deterministic); hence, it computes the probability that the compound is (or is not) present in a sample. Second,. it answers the hypothesis "the compound is present", opposed to answering the question "the compound feature is present", This second difference implies a Shift in the way data analysis is tackled, since the probability of interfering compounds (i.e., isomers and isobaric compounds), is also taken into account.
- 出版日期2016-10-4
