Abstract
Chemical exposures may affect human metabolism and contribute to the etiology of neurodegenerative disorders such as Alzheimer's disease. Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can substantially degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with a greater than 150-fold higher intensity of noise ions than true fragment ions. For human plasma samples from patients with Alzheimer's disease that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.5-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides.