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