Automated Isotopic Profile Deconvolution for High Resolution Mass Spectrometric Data (APGC-QToF) from Biological Matrices

An isotopic profile matching algorithm, the isotopic
profile deconvoluted chromatogram (IPDC), was developed to screen
for a wide variety of organic compounds in high-resolution mass spectrometry
(HRMS) data acquired from instruments with resolution power as low
as 22 000 fwhm. The algorithm initiates the screening process
by generating a series of C/Br/Cl/S isotopic patterns consistent with
the profiles of approximately 3 million molecular formulas for compounds
with potentially persistent, bioaccumulative, and toxic (PBT) properties.
To evaluate this algorithm, HRMS data were screened using these seed
profiles to isolate relevant chlorinated and/or brominated compounds.
Data reduction techniques included mass defect filtering and retention
time prediction from estimated boiling points predicted using molecular
formulas and reasonable elemental conformations. A machine learning
classifier was also developed using spectrometric and chromatographic
variables to minimize false positives. A scoring system was developed
to rank candidate molecular formulas for an isotopic feature. The
IPDC algorithm was applied to a Lake Michigan lake trout extract analyzed
by atmospheric pressure gas chromatography–quadrupole time-of-flight
(APGC-QToF) mass spectrometry in positive and negative modes. The
IPDC algorithm detected isotopic features associated with legacy contaminants
and a series of unknown halogenated features. The IPDC algorithm resolved
313 and 855 halogenated features in positive and negative modes, respectively,
in Lake Michigan lake trout.