Kendrick mass defect analysis-based data mining technique for trace components in polyolefins observed by pyrolysis-gas chromatography/high-resolution mass spectrometry

Pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) is utilized as an effective technique to charac-terize additives and degradation products of polyolefins. However, non-target analysis of trace components buried in complex chromatograms or mass spectra is a difficult task without prior information on sample composition. In this report, we propose a data mining method that extracts meaningful trace components by combining Py-GC/time-of-flight MS (Py-GC/TOFMS) and Kendrick mass defect (KMD) analysis. In our first demonstration, we characterized the additives in the polyethylene (PE) cap of a polyethylene terephthalate (PET) beverage bottle using Py-GC/TOFMS. The summed mass spectrum, built by summing all the mass spectra over the entire range of the pyrogram, was converted to a two-dimensional KMD plot. Specific dots on the KMD plot at a distance from the dots relating to a series of hydrocarbon-derived fragment ions suggested the presence of additives. The types of additives could be identified by a library search for the extracted ion chromatograms (EICs) derived from the corresponding mass of the specific dots. The second demonstration was related to the detection of oxidized products in polypropylene (PP) resin. A "Remainders of KM" (RKM) plot was able to highlight the presence of a series of fragment ions of trace PP pyrolysates with one and two oxygen atoms. Relative peak intensities of the ions containing oxygen atoms were useful as an oxidation index to evaluate the degree of degradation in the very early stages of degradation.