A simple method to isolate fluorescence spectra from small dissolved organic matter datasets

Urban J. Wünsch, Kathleen Murphy*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Dissolved organic matter (DOM) is a complex pool of compounds with a key role in the global carbon cycle. To understand its role in natural and engineered systems, efficient approaches are necessary for tracking DOM quality and quantity. Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) is very widely used to identify and quantify different fractions of DOM as proxies of DOM source, concentration and biogeochemical processing. A major limitation of the PARAFAC approach is the requirement for a large data set containing many variable samples in which the fractions vary independently. This severely curtails the possibilities to study fluorescence composition and behavior in small or unique datasets. Herein, we present a simple and inexpensive experimental procedure that makes it possible to mathematically decompose a small dataset containing only highly-correlated fluorescent fractions. The approach, which uses widely-available commercial extraction sorbents and previously established protocols to expand the original dataset and inject the missing chemical variability, can be widely implemented at low cost. A demonstration of the procedure shows how a robust six-component PARAFAC model can be extracted from even a river-water dataset with only five bulk samples. Widespread adoption of the procedure for analyzing small fluorescence datasets is needed to confirm the suspected ubiquity of certain DOM fluorescence fractions and to create a shared inventory of ubiquitous components. Such an inventory could greatly simplify and improve the use of fluorescence as a tool to investigate biogeochemical processing of DOM in diverse water sources.
Original languageEnglish
Article number116730
JournalWater Research
Number of pages9
Publication statusPublished - 2021


  • Chemometrics
  • FDOM
  • CDOM
  • Biogeochemistry
  • Data mining
  • Machine learning

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