Reviews and syntheses: Use and misuse of peak intensities from high resolution mass spectrometry in organic matter studies: opportunities for robust usage

Abstract

Abstract. Earth’s biogeochemical cycles are intimately tied to the biotic and abiotic processing of organic matter (OM). Spatial and temporal variation in OM chemistry is often studied using high resolution mass spectrometry (HRMS). An increasingly common approach is to use ecological metrics (e.g., within-sample diversity) to summarize high-dimensional HRMS data, notably Fourier transform ion cyclotron resonance MS (FTICR MS). However, problems arise when HRMS peak intensity data are used in a way that is analogous to abundances in ecological analyses (e.g., species abundance distributions). Using peak intensity data in this way requires the assumption that intensities act as direct proxies for concentrations, which is often invalid. Here we discuss theoretical expectations and provide empirical evidence why concentrations do not map to HRMS peak intensities. The theory and data show that comparisons of the same peak across samples (within-peak) may carry information regarding variation in relative concentration, but comparing different peaks (between-peak) within or between samples does not. We further developed a simulation model to study the quantitative implications of both within-peak and between-peak errors that decouple concentration from intensity. These implications are studied in terms of commonly used ecological metrics that quantify different aspects of diversity and functional trait values. We show that despite the poor linkages between concentration and intensity, the ecological metrics often perform well in terms of providing robust qualitative inferences and sometimes quantitatively-accurate estimates of diversity and trait values. We conclude with recommendations for using peak intensities in an informed and robust way for natural organic matter studies. A primary recommendation is the use and extension of the simulation model to provide objective, quantitative guidance on the degree to which conceptual and quantitative inferences can be made for a given analysis of a given dataset. Without objective guidance, researchers that use peak intensities are doing so with unknown levels of uncertainty and bias, potentially leading to spurious scientific outcomes.

Reaction of Organic Matter with Nitronium Ion as a Source of Organic Nitrate Esters in Proxies for Organic Aerosols

“…Whereas the unreacted SRFA sample showed peaks more deficient in N, the reacted sample showed a factor of 4 increase in the number of peaks detected with 1 or 2 nitrogen atoms in the molecular formula, and two times as many peaks containing 3 nitrogen atoms, relative to the unreacted sample. Although caution should be exercised in inferring elemental composition from this data, 46 the comparison strongly suggests that organic nitrogen functional groups are incorporated into the SRFA following its reaction with NO 2 + , which is consistent with XPS analysis.…”

Rethinking Aerobic Respiration in the Hyporheic Zone under Variation in Carbon and Nitrogen Stoichiometry

“…Overall, FTICR-MS results were regarded as semiquantitative because of influences of the environment and instrument’s specifics on resulting peak intensity, as described in detail in a recent synthesis by Kew et al Briefly, peak intensities are strongly influenced by variability in ionization efficiency among molecules, ion transmission and accumulation time, and interference between ions with similar frequencies in ion signal detection. ,− Therefore, we converted intensities to a binary presence/absence format (i.e., 1 or 0) for all FTICR-MS data analysis.…”

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

“…To better assess the cross-compatibility for linking optical and FTICR-MS analyses, Spearman rank correlations were derived from relationships between the relative intensity of individual molecular formulae and individual PARAFAC components for both whole water and SPE-DOM. Spearman rank correlations were performed due to the non-normal distribution of FTICR-MS peak intensities and to avoid an assumption of linearity between peak intensity and analyte concentration (Kew et al 2022). For these analyses, the FTICR-MS dataset was constrained to molecular formulae that were present in > 90% of all samples, which accounted for 2770 of the 16,536 total identified molecular formulae across the whole dataset.…”