Untargeted
screening using high resolution mass spectrometry (HRMS)
is a promising approach for screening the food supply for contaminants,
but the sheer amount of information inherent to the HRMS data set
presents analytical challenges. Red apples, collected during the U.S.
FDA’s Total Diet Study, were studied to determine whether bioinformatic
software can be used to distinguish spiked model compounds from those
native to apples. A workflow was created, in which initial data sets
of over 44,000 features in each of the two spiked samples were reduced
by several orders of magnitude to a scale suitable for visual inspection.
After visual inspection to address degeneracy and data quality, the
final data sets contained 30 and 2 suspect compounds, respectively.
To the best of our knowledge, this is the largest scale case-control
study on food matrices to date and the first use of market basket
samples as references in an untargeted screening study.