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Combining targeted and nontargeted data analysis for liquid chromatography/high‐resolution mass spectrometric analyses
Author(s) -
Croley Timothy R.,
White Kevin D.,
Wong Jon,
Callahan John H.,
Musser Steven M.,
Antler Margaret,
Lashin Vitaly,
McGibbon Graham A.
Publication year - 2013
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201200348
Subject(s) - software , limiting , mass spectrometry , resolution (logic) , vendor , analyte , data processing , high resolution , chemistry , chromatography , identification (biology) , computer science , analytical chemistry (journal) , process engineering , database , engineering , artificial intelligence , remote sensing , mechanical engineering , botany , marketing , business , biology , programming language , geology
Increasing importation of food and the diversity of potential contaminants have necessitated more analytical testing of these foods. Historically, mass spectrometric methods for testing foods were confined to monitoring selected ions (SIM or MRM), achieving sensitivity by focusing on targeted ion signals. A limiting factor in this approach is that any contaminants not included on the target list are not typically identified and retrospective data mining is limited. A potential solution is to utilize high‐resolution MS to acquire accurate mass full‐scan data. Based on the instrumental resolution, these data can be correlated to the actual mass of a contaminant, which would allow for identification of both target compounds and compounds that are not on a target list (nontargets). The focus of this research was to develop software algorithms to provide rapid and accurate data processing of LC/MS data to identify both targeted and nontargeted analytes. Software from a commercial vendor was developed to process LC/MS data and the results were compared to an alternate, vendor‐supplied solution. The commercial software performed well and demonstrated the potential for a fully automated processing solution.