Skip to main content
. 2020 Apr 28;11:2057. doi: 10.1038/s41467-020-15960-z

Fig. 3. Relative fragment intensity prediction for collision-energy optimization.

Fig. 3

The workflow is connecting the assay development with LipidCreator (1), lipid standards measurement (2) and transition extraction from the measured data to create feature tables (3) for the training of fragment-specific nonlinear regression models for collision-energy-dependent, relative intensity prediction using nonlinear regression models (4). The subsequent QC and visualization step (5) supports model fit quality inspection and selection of the parameterized model parameters. LipidCreator uses these model parameters to calculate the optimal collision energy for a lipid based on the selected fragments (6) for further assay refinement and integration with the MS acquisition workflow.