Errors in alkylated polycyclic aromatic hydrocarbon and sulfur heterocycle concentrations caused by currently employed standardized methods

- Standardized GC/MS-SIM methods accurately quantify parent PAH/PASH.
- Standardized methods systematically overestimate alkylated PAH/PASH.
- Overestimation increases with homologue ring number and extent of alkylation.
- MFPPH accurately quantifies homologues in complex matrixes despite minimal clean-up.
- SIM/MFPPH obtains sufficient sensitivity for trace analyses despite ≥3 ions/pattern.

Alkylated polycyclic aromatic hydrocarbon (PAH) and polycyclic aromatic sulfur heterocycle (PASH) standardized methods often rely on gas chromatography/mass spectrometry operated in the selected ion monitoring mode (GC/MS-SIM). The objective of this study is to develop a method that produces accurate data while minimizing sample preparation and achieving low levels of detection. Most standardized methods are based on acquiring a given homologue's molecular ion (1-ion). Some methods include a second, confirming ion (2-ion) in the hopes of excluding non-target ion signals from the total homologue peak area. Although all methods use homologue-specific retention windows, these windows differ greatly among the methods. In this paper we evaluate, for the first time, errors in quantitation caused by using different windows as well as common ion effects when target and/or matrix compounds coelute. Two NIST-certified Standard Reference Materials (SRMs), crude oil SRM 1582 and marine sediment SRM 1941b, were analyzed by five standardized methods and by the new method we developed, which relies on spectral deconvolution of three to five ions per PAH/PASH and as many fragmentation patterns as needed to correctly identify the C1 to C4 homologues (MFPPH). All of the standardized methods overestimated the concentrations of the majority of alkylated homologues whereas MFPPH obtained values much closer to NIST-certified concentrations. Rather than straight-line integration of all peaks in the retention window or recognizing peak patterns, the MFPPH data analysis software integrates only those peaks that meet the compound identity criteria.

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