Combining targeted and nontargeted data analysis for liquid chromatography/high-resolution mass spectrometric analyses

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 si...

Full description

Saved in:
Bibliographic Details
Published in:Journal of separation science 2013-03, Vol.36 (5), p.971-979
Main Authors: Croley, Timothy R., White, Kevin D., Wong, Jon, Callahan, John H., Musser, Steven M., Antler, Margaret, Lashin, Vitaly, McGibbon, Graham A.
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Automatic data processing
Chemistry
Chromatographic methods and physical methods associated with chromatography
Chromatography
Chromatography, Liquid - instrumentation
Computer programs
Contaminants
Data Mining
Data processing
Exact sciences and technology
Focusing
Foods
High-resolution mass spectrometry
Lists
Mass spectrometry
Mass Spectrometry - instrumentation
Nontargeted analysis
Other chromatographic methods
Software
Spectroscopy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1615-9306
1615-9314
DOI:10.1002/jssc.201200348