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.当质谱法（Mass Spectrometry, MS）的质量分辨率超过500,000时，可通过其在质谱中的特征性同位素峰型，明确鉴定含硫（Sulfur, S）化合物。然而，诸多开展非靶向分析的环境研究实验室，通常依赖四极杆飞行时间质谱仪（Quadrupole Time-of-Flight Mass Spectrometry, QTOF MS）这类高分辨质谱仪（High-Resolution Mass Spectrometry, HRMS）。此类高分辨质谱仪的质量分辨率通常低于50,000，在此分辨率限制下，可靠识别含硫同位素峰型颇具挑战。 本研究开发了一种机器学习（Machine Learning, ML）策略，可在低至25,000的质量分辨率下，对高分辨质谱数据中的含硫化合物进行识别并预测其硫原子数目。我们在实验数据集上对该机器学习策略开展基准测试：将200种含硫标准品混入复杂环境样品中。在正电喷雾电离（Electrospray Ionization, ESI）模式下，该机器学习策略对含硫化合物识别的准确率介于87.4%至95.0%，对硫原子数目预测的准确率介于86.3%至96.6%。值得注意的是，该机器学习方法在负电喷雾电离模式下同样表现优异。 我们进一步在外部水环境实验数据集上对该方法进行评估：模型可正确识别24种此前已报道的2-巯基苯并噻唑消毒副产物（Disinfection Byproducts, DBPs）中的含硫成分。本研究开发的机器学习策略已被集成至R程序SulfurFinder中，以实现高分辨质谱数据的自动化数据清洗、含硫化合物识别及硫原子数目预测。 结合高效液相色谱-高分辨质谱联用（High Performance Liquid Chromatography-High Resolution Mass Spectrometry, HPLC-HRMS）分析废水样品时，通过SulfurFinder初步鉴定出169个潜在含硫特征峰。其中3个特征峰被确认为含硫药物。另有1种含硫药物通过分子网络分析被推定注释。 在暴露组学（Exposomics）时代，SulfurFinder的开发显著提升了常规高分辨质谱技术应对含硫化合物识别难题的能力，可为诸多环境领域应用提供支撑。