Non-Targeted Polysorbate Analysis
Polymers and chemicals that form series are essential in every day products including plastics, emulsifiers, lubricants, and detergents. The physicochemical properties and health impacts of a product are determined by the properties of molecules in each mixture, such as number of repeating units, chemical moieties and degree of unsaturation. Thus, it is essential to be able to characterize these mixtures at the molecular level. The repeating nature of chemical moieties means that mass spectra and chromatographic data of polymers and chemical series have obvious patterns, which when discerned can be used to comprehensively characterize a sample. After the success of the LipidMatch and FluoroMatch workflows for non-targeted analysis which take advantage of these unique spectral patterns, we present a new workflow for polysorbates and related species.
The PolyMatch software data analysis workflow starts by importing data collected using MS, and MS/MS data dependent (DDA), iterative exclusion MS/MS, or targeted MS/MS modes from individual, pooled and blank samples. PolyMatch algorithms cover file conversion, blank filtering, feature annotation, and visualization via PolyVI (visualization software). One can also generate your own libraries using PolyMatch Generator from your own series of compounds. The PolyMatch Software also directly imports data processed initially using Agilent’s Mass Profiler software or other peak picking software.
PolyMatch integrates a wide range of evidence to classify polymers: mass defect, retention time, and exact mass can be used alongside homologous series to compile groups of chemicals that likely belong to the same class. MS/MS evidence can give structural information pertaining to class or species level assignments.
PolyMatch was recently presented at the 2022 American Society for Mass Spectrometry (ASMS) Conference. A poster describing the software can be found here.
As part of a collaborative project with David Weil from Agilent Technologies, we are looking for collaborators to better understand MS/MS fragmentation patterns, and with interesting applications!
An example dataset is shown below for a polysorbate 80 sample.