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Using Machine Learning to Estimate Concentrations of Non-Targeted Chemicals Without Analytical Standards

preprint
submitted on 07.11.2019 and posted on 15.11.2019 by Dimitri Abrahamsson, June-Soo Park, Marina Sirota, Tracey Woodruff
We developed two in silico quantification methods for chemicals analyzed with capillary electrophoresis electrospray ionization-mass spectrometry (CE-ESI-MS) using machine learning - a random forest (RF) and an artificial neural network (ANN). The algorithms can be used to predict chemical concentrations based on the chemicals’ relative response factors (RRFs) and their physicochemical properties. The RF and ANN predicted the measured concentrations with a mean absolute error of 0.2 log units and a coefficient of determination (R2) of about 0.85 for the testing set.

History

Email Address of Submitting Author

dimitri.abrahamsson@ucsf.edu

Institution

University of California, San Francisco

Country

United States

ORCID For Submitting Author

0000-0002-3402-7565

Declaration of Conflict of Interest

The authors declare no conflict of interest

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