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A new retrospective, multi‐evidence veterinary drug screening method using drift tube ion mobility mass spectrometry
Author(s) -
Xu Zhenzhen,
Li Jianzhong,
Chen Ailiang,
Ma Xin,
Yang Shuming
Publication year - 2018
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.8154
Subject(s) - retrospective cohort study , workflow , false positive paradox , veterinary drug , retention time , chemistry , analytical chemistry (journal) , medicine , chromatography , computer science , database , surgery , artificial intelligence
Rationale The retrospectivity (the ability to retrospect to a previously unknown compound in raw data) is very meaningful for food safety and risk assessment when facing new emerging drugs. Accurate mass and retention time based screening may lead to false positive and false negative results so a new retrospective, reliable platform is desirable. Methods Different concentration levels of standards with and without matrix were analyzed using ion mobility (IM)‐quadrupole‐time‐of‐flight (QTOF) mass spectrometry to collect retrospective accurate mass, retention time, drift time and tandem mass spectrometry (MS/MS) evidence for identification in a single experiment. The isomer separation ability of IM and the four‐dimensional (4D) feature abundance quantification abilities were evaluated for veterinary drugs for the first time. Results The sensitivity of the IM‐QTOF workflow was obviously higher than that of the traditional database searching algorithm [find by formula (FbF) function] for QTOF. In addition, the IM‐QTOF workflow contained most of the results from FbF and removed the false positive results. Some isomers were separated by IM and the 4D feature abundance quantitation removed interferences with similar accurate masses and showed good linearity. Conclusions A new retrospective, multi‐evidence platform was built for veterinary drug screening in a single experiment. The sensitivity was significantly improved and the data can be used for quantification. The platform showed its potential to be used for food safety and risk assessment.