Untargeted Screening in a Case Control Study Using Apples as a Matrix

Abstract

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.

Assessing Chemical Migration from Plastic Food Packaging into Food Simulant by Gas and Liquid Chromatography with High-Resolution Mass Spectrometry

“…Additional examples of food contaminant identification and parameter optimization have been reported with these workflows. 30,31 Compound Discoverer is newly available for GC data analysis, and we recently published a GC-HRMS workflow for identification of chemical migration in food packaging. 20,29 Peaks were generated based on mass tolerance (<5 ppm), signal-to-noise threshold (>3), TIC threshold (1e6), and ion overlap (98%).…”

“…It performs peak picking and retention time alignment across files, grouping of common features, and then peak annotation via user-defined mass lists, commercial databases, and predicted elemental compositions. Additional examples of food contaminant identification and parameter optimization have been reported with these workflows. , …”