Machine Learning-Assisted Recognition of Environmental Sulfur-Containing Chemicals in Nontargeted Mass Spectrometry Analysis of Inadequate Mass Resolution

Sulfur (S)-containing compounds can be unambiguously identified
by their distinctive isotope patterns in mass spectrometry (MS) when
the instrument has a mass resolution exceeding 500,000. However, many
environmental research laboratories that perform nontargeted analysis
rely on high-resolution mass spectrometry (HRMS) instruments, such
as quadrupole time-of-flight mass spectrometry (QTOF MS). These HRMS
instruments typically operate at a mass resolution of less than 50,000.
At such limited resolution, confidently recognizing sulfur isotope
patterns is challenging. This work develops a machine learning (ML)
strategy for recognizing and predicting the number of S present using
HRMS at a mass resolution as low as 25,000. We benchmarked our ML
strategy on experimental data, where 200 S-containing standard compounds
were mixed into complex environmental samples. In positive electrospray
ionization (ESI) mode, our ML strategy achieved accuracies ranging
from 87.4 to 95.0% for S recognition and accuracies ranging from 86.3
to 96.6% for S number prediction. Notably, the ML method performed
similarly well in negative ESI mode. Our ML strategy was further evaluated
on an external experimental water dataset where it correctly recognized
the presence of S for all 24 previously reported 2-mercaptobenzothiazole
disinfection byproducts (DBPs). The developed ML strategy was implemented
into SulfurFinder, an R program, to facilitate automated data cleaning,
S recognition, and S number prediction in HRMS data. SulfurFinder
combined with HPLC-HRMS analysis of a wastewater sample tentatively
identified 169 potential S-containing features. Of these, three were
confirmed as S-containing pharmaceuticals. An additional S-containing
drug was also putatively annotated using molecular networking. The
development of SulfurFinder significantly boosts the capability of
conventional HRMS to address the challenge of S recognition in the
era of exposomics, supporting a wide range of environmental applications.