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

Zhang Chun-Yun; Li Xueshu; Keil Stietz Kimberly P.Sethi SunjayYang WeizhuMarek Rachel F.Ding XinxinLein Pamela J.Hornbuckle Keri C.Lehmler Hans-Joachim

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

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

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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.

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《Environmental Science & Technology: ES&T》 |2022年第18期|13169-13178|共10页
作者
Zhang Chun-Yun; Li Xueshu; Keil Stietz Kimberly P.Sethi SunjayYang WeizhuMarek Rachel F.Ding XinxinLein Pamela J.Hornbuckle Keri C.Lehmler Hans-Joachim;
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作者单位

The University of Iowa;

University of California Davis;

University of Arizona;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类环境科学、安全科学;
关键词
Article; Ecotoxicology and Public HealthOH-PCBs; GC-MS/MS method; model prediction; relative retention time; relative response factor;

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

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