Understanding Biases in Natural Organic Matter FTICR Mass Spectrometry to More
Accurately Model Complex Environmental Systems
EMSL Project ID
51667
Abstract
Characterization of natural organic matter (NOM) by direct infusion (DI) Fourier transform mass spectrometry (FTMS) yields rich and complex information about environmental systems. However, the experimental approaches are not quantitative, and various methodological biases result in the final mass spectrum not comprehensively representing the original sample. Despite the high resolution and sensitivity, each detected feature in a complex mixture mass spectrum is likely several isomeric compounds, or unresolved isobaric species in the most complex samples, and thus assigned molecular formula (MF) and inferred chemistries are liable to misinterpretation. In this proposal, we aim to study the qualitative and quantitative biases introduced in routine sample preparation and to better understand the relationship between the obtained mass spectrum and the true raw sample chemistry, and thus understand the underlying environmental and biogeochemical systems more accurately. By improving our understanding of how the concentration of different compounds in the sample is reflected in the resulting mass spectrum to be able to understand how well the general trends (e.g. ratios and amounts of different compound classes) are reflective of the original sample. The intention is thus to model these experimental responses under variable matrix conditions, and thus be able to improve the interpretability of both existing and future datasets. Fundamentally, this research will allow for a better understanding of, not only how to interpret results within a single spectrum or dataset, but how, if it is possible, to more confidently compare results across datasets and across instruments. Such an understanding is pivotal to the future of complex mixture analysis. This research will help develop better dissolved organic matter (DOM) characterization workflows, data integration strategies, improved methods for uncertainty in MF assignments, and developing better models for DOM studies.
Project Details
Start Date
2020-10-26
End Date
2022-04-22
Status
Closed
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
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