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Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

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

Urban J Wünsch¹, Kathleen Murphy²

¹Technical University of Denmark, National Institute of Aquatic Resources, Kemitorvet, 2800 Kgs. Lyngby, Denmark; Chalmers University of Technology, Architecture and Civil Engineering, Water Environment Technology, Sven Hultins Gata 6, 41296 Gothenburg, Sweden.

|December 21, 2020

Summary

This study introduces a low-cost method to analyze dissolved organic matter (DOM) using fluorescence spectroscopy, enabling robust characterization of complex DOM fractions even in small datasets.

Keywords:

Biogeochemistry CDOM Chemometrics Data mining FDOM Machine learning

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Published on: April 7, 2012

Area of Science:

Environmental Chemistry
Biogeochemistry
Analytical Chemistry

Background:

Dissolved organic matter (DOM) is crucial for the global carbon cycle, necessitating effective tracking methods.
Fluorescence spectroscopy with parallel factor analysis (PARAFAC) is standard for DOM analysis but requires large, diverse datasets.
Analyzing small or unique DOM datasets with PARAFAC is limited by the need for independent variation among fluorescent fractions.

Purpose of the Study:

To develop an accessible, cost-effective experimental procedure for decomposing small, highly-correlated fluorescent dissolved organic matter datasets.
To overcome the limitations of traditional PARAFAC analysis in datasets with limited sample variability.
To facilitate the study of DOM quality and quantity in diverse natural and engineered systems.

Main Methods:

Utilized commercial extraction sorbents and established protocols to augment small datasets, introducing necessary chemical variability.
Employed a novel mathematical decomposition approach to analyze datasets with highly-correlated fluorescent fractions.
Demonstrated the procedure's efficacy on a river-water dataset comprising only five bulk samples.

Main Results:

Successfully extracted a robust six-component PARAFAC model from a minimal dataset (five river-water samples).
The presented procedure effectively expands small datasets to enable detailed fluorescence analysis.
The method is inexpensive and can be widely implemented for DOM characterization.

Conclusions:

The developed procedure offers a viable solution for analyzing small fluorescence datasets of dissolved organic matter.
Widespread adoption can help confirm the ubiquity of DOM fluorescence fractions and build a shared component inventory.
This approach will significantly enhance the use of fluorescence for investigating DOM biogeochemical processing across various water sources.