Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data.

ABSTRACT: Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared

SUBMITTER: Boelrijk J 

PROVIDER: S-EPMC9960388 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature