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Improving compound identification results by automatically recognizing in-source fragment ions in HRMS with AntDAS: A study on accurate pesticide screening in complex food samples

文献类型： 外文期刊

作者： Ma, Hui 1 ; Zhai, Meng 1 ; Tang, Li-Hua 2 ; Wang, Xing-Cai 3 ; Han, Lu 1 ; Li, Shu-Fang 4 ; Lv, Yi 2 ; Zheng, Qing-Xia 5 ; Liu, Ping-Ping 5 ; Fu, Hai-Yan 6 ; Yu, Yong-Jie 1 ; She, Yuanbin 3 ;

作者机构： 1.Ningxia Med Univ, Coll Pharm, Yinchuan 750004, Peoples R China

2.Ningxia Food Testing & Res Inst, Key Lab Qual & Safety Wolfberry & Wine State Adm M, Yinchuan 750004, Peoples R China

3.Zhejiang Univ Technol, Coll Chem Engn, Hangzhou 310032, Peoples R China

4.Henan Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Zhengzhou 450002, Peoples R China

5.CNTC, Zhengzhou Tobacco Res Inst, Zhengzhou 450001, Peoples R China

6.South Cent Minzu Univ, Sch Pharmaceut Sci, Wuhan 430074, Peoples R China

7.Minist Educ, Key Lab Protect Dev & Utilizat Med Resources Liupa, Yinchuan 750004, Peoples R China

关键词： In-source fragment ion; Accurate compound identification; UHPLC-HRMS; AntDAS; Pesticide screening

期刊名称：JOURNAL OF CHROMATOGRAPHY A （ 影响因子：4.0； 五年影响因子：3.6 ）

ISSN： 0021-9673

年卷期： 2025 年 1746 卷

页码：

收录情况： SCI

摘要： Ultrahigh-performance liquid chromatography combined with high-resolution mass spectrometry has been extensively used for compound identification in an untargeted manner. However, current methods still cannot accurately screen and identify compounds in the presence of in-source fragment ions. Here, we proposed an insource fragment ion-based strategy to precisely recognize compounds. First, raw data files were analyzed with our newly developed AntDAS-DDA to perform feature detection and annotation. Second, in-source fragment ions from a compound were recognized based on tandem mass spectrometry (MS/MS) spectra and the similarity of chromatograms. Last, the MS/MS spectra of each fragment ion were compared with those in an in-house library to benefit compound identification. The performance of the developed strategy was comprehensively investigated using the aforementioned in-house library containing 478 pesticides. Simulated and real samples suggested that compounds were accurately identified. The match factor obtained from the classic method was improved by at least 4.3 times by the developed strategy. The developed strategy may provide a new solution for compound identification in complex food samples.

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