The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (t_R) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R²) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure–ion intensity relationship (QSIIR) model.

由于污染物代谢物作为人类暴露于母体化合物的生物标志物的重要性，因此非目标分析和鉴定污染物代谢物（例如邻苯二甲酸盐的代谢物）及其在人类生物流体样品中的替代物，构成了人类生物监测领域中一个正在发展的研究领域。高分辨率质谱（HRMS）与高效液相色谱（HPLC）结合使用此应用程序可以提供快速分离和灵敏的分析。但是，人类样品中潜在代谢物（尤其是异构体）的多样性使基于质谱的结构鉴定非常具有挑战性，即使具有高分辨率和精确质量也是如此。在这项研究中，我们提出了保留时间（t _R）基于定量结构-保留关系（QSRR）的预测模型。该模型可以预测包括异构体在内的邻苯二甲酸酯给定结构的保留时间。在多元线性回归QSRR模型的开发中使用了二十三个分子描述符。回归系数（R ²26种训练集化合物的预测保留时间与实验保留时间之间的比率为0.9912。保留时间预测模型与通过精确质量搜索和目标MS / MS频谱解释进行识别的结合可以在缺乏参考标准的情况下提高识别的可信度。使用该模型在人尿液中鉴定出两种以前未报告的邻苯二甲酸酯代谢物。这项研究的结果表明，开发的QSRR模型可能是预测未知邻苯二甲酸酯及其替代品在未来的非靶向筛查分析中保留时间的有用工具。还使用定量结构-离子强度关系（QSIIR）模型估算了这两种未知化合物的浓度。