Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples

Chun Yun Zhang, Xueshu Li, Kimberly P. Keil Stietz, Sunjay Sethi, Weizhu Yang, Rachel F. Marek, Xinxin Ding, Pamela J. Lein, Keri C. Hornbuckle, Hans Joachim Lehmler

Research output: Contribution to journalArticlepeer-review

Abstract

Laboratory studies of the disposition and toxicity of hydroxylated polychlorinated biphenyl (OH-PCB) metabolites are challenging because authentic analytical standards for most unknown OH-PCBs are not available. To assist with the characterization of these OH-PCBs (as methylated derivatives), we developed machine learning-based models with multiple linear regression (MLR) or random forest regression (RFR) to predict the relative retention times (RRT) and MS/MS responses of methoxylated (MeO-)PCBs on a gas chromatograph-tandem mass spectrometry system. The final MLR model estimated the retention times of MeO-PCBs with a mean absolute error of 0.55 min (n = 121). The similarity coefficients cos θ between the predicted (by RFR model) and experimental MS/MS data of MeO-PCBs were >0.95 for 92% of observations (n = 96). The levels of MeO-PCBs quantified with the predicted MS/MS response factors approximated the experimental values within a 2-fold difference for 85% of observations and 3-fold differences for all observations (n = 89). Subsequently, these model predictions were used to assist with the identification of OH-PCB 95 or OH-PCB 28 metabolites in mouse feces or liver by suggesting candidate ranking information for identifying the metabolite isomers. Thus, predicted retention and MS/MS response data can assist in identifying unknown OH-PCBs.

Original languageEnglish (US)
Pages (from-to)13169-13178
Number of pages10
JournalEnvironmental Science and Technology
Volume56
Issue number18
DOIs
StatePublished - Sep 20 2022

Keywords

  • GC-MS/MS method
  • model prediction
  • OH-PCBs
  • relative response factor
  • relative retention time

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry

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