文献类型： 外文期刊

作者： Wang, Xing-Cai ¹ ; Zhang, Jia-Ni ² ; Zhao, Juan-Juan ² ; Guo, Xiao-Meng ² ; Li, Shu-Fang ³ ; Zheng, Qing-Xia ⁴ ; Liu, Ping-Ping ⁴ ; Lu, Peng ⁴ ; Fu, Hai-Yan ⁵ ; Yu, Yong-Jie ² ; She, Yuanbin ¹ ;

作者机构： 1.Zhejiang Univ Technol, Coll Chem Engn, State Key Lab Breeding Base Green Chem Synth Techn, Hangzhou 310032, Peoples R China

2.Ningxia Med Univ, Coll Pharm, Yinchuan 750004, Peoples R China

3.Henan Acad Agr Sci, Inst Qual Stand & Testing Technol Agro Prod, Zhengzhou 450002, Peoples R China

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

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

期刊名称：ANALYTICAL CHEMISTRY （ 影响因子：7.4； 五年影响因子：7.0 ）

ISSN： 0003-2700

年卷期： 2023 年 95 卷 2 期

页码：

收录情况： SCI

摘要： Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.