PFAS: Streamlining Environmental Data Analysis: A Deep Learning Approach
Per- and polyfluoroalkyl substances (PFAS), a class of emerging persistent organic pollutants (POPs), are present at trace levels not only in the environment (water, soil, and air) but also in food. The quantitative analysis of PFAS is typically conducted using liquid or gas chromatography-tandem mass spectrometry (LC-MS/MS, GC-MS/MS). Despite the high sensitivity of these instruments, PFAS analysis remains challenging.
In this work, we tested a complete workflow integrating several DL architectures, tailored for liquid chromatography-tandem mass spectrometry (LC-MS/MS) data in multiple reaction monitoring (MRM) mode for the quantitative analysis of PFAS. Several convolutional neural network (CNN)-based architectures and a transformer-based model are evaluated and their performance compared. Preliminary results show that both models improve upon existing automatic integration algorithms. When a trained DL model is deployed, data review time can be significantly reduced by eliminating most of the manual data analysis steps, on a compound-by-compound basis.
Presenter: Ruoji Luo (Application Scientist, Agilent)
