Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research

Abstract

A CNN was developed to classify extracted features from nontarget mass spectrometry workflows. The CNN accuracy ranged from 85% to 100%. These tools will be important in data-driven research enabling rapid processing of large volume and complex datasets.