色谱

色谱 ›› 2026, Vol. 44 ›› Issue (4): 432-443.DOI: 10.3724/SP.J.1123.2025.07011

• 研究论文 • 上一篇    下一篇

全氟及多氟烷基物质非靶向识别中液相色谱-高分辨率质谱数据处理方法评价

张博暄1,2, 何钦文1,3, 韩宝苍1, 马存地1, 罗竹君1, 孟祥周1,2,*()   

  1. 1.同济大学环境科学与工程学院，上海 200092
    2.嘉兴同济环境研究院，浙江 嘉兴 314051
    3.上海市公安局物证鉴定中心，上海 200083
  • 收稿日期:2025-07-14 出版日期:2026-04-08 发布日期:2026-04-13
  • 通讯作者: * E-mail：xzmeng@tongji.edu.cn.
  • 基金资助:
    国家自然科学基金项目(42177378);国家重点研发计划项目(2024YFC3712002);嘉兴市公益性研究计划项目(2023AY11053);上海市法医学重点实验室暨司法部司法鉴定重点实验室开放课题项目(KF202422)

Assessment on data processing methods for nontarget identification of per- and polyfluoroalkyl substances using liquid chromatography-high-resolution mass spectrometry

ZHANG Boxuan1,2, HE Qinwen1,3, HAN Baocang1, MA Cundi1, LUO Zhujun1, MENG Xiangzhou1,2,*()   

  1. 1. College of Environmental Science and Engineering，Tongji University，Shanghai 200092，China
    2. Jiaxing Tongji Environmental Research Institute，Jiaxing 314051，China
    3. Institute of Forensic Science，Shanghai Municipal Public Security Bureau，Shanghai 200083，China
  • Received:2025-07-14 Online:2026-04-08 Published:2026-04-13
  • Supported by:
    National Natural Science Foundation of China(42177378);National Key Research and Development Program of China(2024YFC3712002);Jiaxing Public Welfare Research Project of Jiaxing Science and Technology Bureau(2023AY11053);Shanghai Key Laboratory of Forensic Medicine and Key Laboratory of Forensic Science, Ministry of Justice(KF202422)

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摘要：

借助非靶向分析技术识别未知全氟及多氟烷基物质（per- and polyfluoroalkyl substances， PFAS）是环境领域的研究热点之一。液相色谱-高分辨率质谱是实施非靶向分析技术的主要手段。质谱数据的采集模式、峰提取算法及解卷积算法均会影响非靶向识别结果，但尚缺乏系统性评估。本研究基于超高效液相色谱-静电场轨道阱高分辨率质谱联用技术，通过基质加标样品，分别对比了两种软件（MS-DIAL和MZmine）的峰提取效果和两类算法（MS2Dec和IonDecon）的解卷积性能，评价了数据依赖性采集（DDA）与数据非依赖性采集（DIA）模式对PFAS非靶向识别结果的影响。结果表明，MS-DIAL的峰提取效果优于MZmine，检出全部34种目标物；针对DIA模式下的质谱数据，MS2Dec算法的解卷积效果优于IonDecon算法。DDA模式下识别结果的真阳性率随目标物浓度升高而提升，且阳性预测值保持在较高水平。相比之下，DIA模式下识别结果的真阳性率为100%，但其阳性预测值会随浓度升高而下降。最后，基于评价结果优化了DDA和DIA模式下的数据处理方法，并应用于3个实际电镀污泥样品检测，共鉴定出10类共计36种PFAS，其主要来源为电镀工艺中铬雾抑制剂的使用。本研究中的质谱数据处理方法可用于复杂环境基质中未知PFAS的识别。

关键词: 全氟及多氟烷基物质, 高分辨率质谱, 数据依赖性采集, 数据非依赖性采集, 峰提取, 解卷积, 非靶向识别

Abstract:

The widespread use， persistence， bioaccumulation， and toxicity of per- and polyfluoroalkyl substances （PFAS） have raised global concern. The number of PFAS types continues to grow， driven by changing industrial demands and regulatory environments. Non-target analysis using high-resolution mass spectrometry （HRMS） is an effective methodology for identifying novel and unknown PFAS in environmental matrices. The efficacy of non-target analysis is critically influenced by the data acquisition mode， peak picking algorithm， and deconvolution strategy. Using ultra-high performance liquid chromatography coupled with an Orbitrap mass spectrometer （UHPLC-Orbitrap MS）， this study aims to systematically evaluate data processing methods for non-targeted PFAS identification under data-dependent acquisition （DDA） and data-independent acquisition （DIA） modes. A clean sludge sample was spiked with 34 PFAS standards at three levels to assess method performance， alongside the analysis of three electroplating sludge samples. To compare the identification performance between DDA and DIA modes， a multi-step evaluation process was employed. Firstly， we assessed the peak picking capabilities of two widely used data processing software packages， MS-DIAL and MZmine. The key parameters for peak picking process are MS1 mass tolerance of 0.002 5 Da， MS2 mass tolerance of 0.01 Da， minimum peak height of 1 000， and retention time alignment tolerance of 0.1 min. Secondly， a comparison was made regarding DIA data deconvolution， specifically between MS2Dec algorithm and IonDecon algorithm. Finally， FluoroMatch was utilized to compare the true positive rate （TPR） and positive predictive value （PPV） of PFAS identification in both DDA and DIA datasets. In the spiked samples， the ［M-H］- precursor ions for 33 PFAS standards and the ［M-CO2-H］- ion for HFPO-DA were successfully detected and manually verified across all three levels. For peak picking， MS-DIAL demonstrated superior performance， achieving a 100% detection rate in all spiked samples， outperforming MZmine. When comparing deconvolution performance for DIA data， MS2Dec algorithm and the IonDecon algorithm showed similar efficacy， although MS2Dec algorithm exhibited slightly better results for low-concentration samples. In DDA mode， the true positive rate for PFAS identification increased from 80% to 100% with rising analyte concentration， accompanied by a minimal decrease in positive predictive value. Conversely， in DIA mode， the true positive rate remained at 100% across all concentrations， but positive predictive value decreased as concentration increased， primarily due to interferences from in-source fragmentation and adduct ions. The degree of in-source fragmentation of perfluorocarboxylic acids （PFCAs） decreases with increasing carbon chain length. However， the proportion of adduct ions remains nearly constant across different PFAS， leading to false positive identification of hydrogen-substituted PFAS. Based on the evaluation results， the data processing methods for DDA and DIA modes were optimized. These methods were then applied to three electroplating sludge samples， leading to the identification of 36 PFAS species belonging to 10 classes， including eight perfluorocarboxylic acids （PFCAs）， eight perfluorosulfonic acids （PFSAs）， one hydrogen-substituted perfluorosulfonic acid （H-PFSA）， five unsaturated perfluorosulfonic acids （UPFSAs）， one carbonyl perfluorosulfonic acid （KPFSA）， one chlorine-substituted perfluorosulfonic acid （Cl-PFSA）， one n∶2 fluorotelomer sulfonic acid （n∶2 FTSA）， five chlorinated polyfluoroethersulfonic acids （Cl-PFESAs）， two hydrogen-substituted polyfluoroethersulfonic acids （H-PFESAs）， and four polyfluoroethersulfonic acids （PFESAs）. Their presence was largely attributed to the use of chrome mist suppressants in the electroplating process. Combining DDA and DIA data for FluoroMatch input captured more information on unknown PFAS， possibly because the inclusion of multiple samples improves peak extraction. Based on the performance of PFAS identification in spiked and real samples， we developed a processing method that couples DDA and DIA data. This method can generate a composite list of identified PFAS while keeping data files independent， increasing the true positive rate and efficiency of identification. This study systematically evaluated nontargeted PFAS data processing methods， clarifying the optimal combination of tools for key steps （acquisition mode， peak picking， and deconvolution）， and validating its application potential in complex environmental matrices.

Key words: per- and polyfluoroalkyl substances （PFAS）, high-resolution mass spectrometry （HRMS）, data-dependent acquisition （DDA）, data-independent acquisition （DIA）, peak picking, deconvolution, nontarget identification

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