Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data
Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC-MS or LC-MS). MS instrumentation can b...
Main Authors: | , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://orbit.dtu.dk/en/publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf https://doi.org/10.1002/lom3.10561 https://backend.orbit.dtu.dk/ws/files/332288087/Limnology_Ocean_Methods_2023_Bruhn.pdf https://doi.org/10.11583/DTU.22182694.v1 https://doi.org/10.11583/DTU.22178825.v1 |
id |
ftdtupubl:oai:pure.atira.dk:publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf |
---|---|
record_format |
openpolar |
spelling |
ftdtupubl:oai:pure.atira.dk:publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf 2024-09-15T18:03:32+00:00 Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data Bruhn, Anders Dalhoff Wunsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. 2023 application/pdf https://orbit.dtu.dk/en/publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf https://doi.org/10.1002/lom3.10561 https://backend.orbit.dtu.dk/ws/files/332288087/Limnology_Ocean_Methods_2023_Bruhn.pdf https://doi.org/10.11583/DTU.22182694.v1 https://doi.org/10.11583/DTU.22178825.v1 eng eng https://orbit.dtu.dk/en/publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf info:eu-repo/semantics/openAccess Bruhn , A D , Wunsch , U , Osburn , C L , Rudolph , J C & Stedmon , C A 2023 , ' Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data ' , Limnology and Oceanography: Methods , vol. 21 , no. 8 , pp. 508-528 . https://doi.org/10.1002/lom3.10561 article 2023 ftdtupubl https://doi.org/10.1002/lom3.1056110.11583/DTU.22182694.v110.11583/DTU.22178825.v1 2024-08-13T00:03:06Z Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC-MS or LC-MS). MS instrumentation can be relatively expensive to purchase and maintain. Here we present an improved approach for quantification of lignin phenols using LC and absorbance detection. The approach applies a modified version of parallel factor analysis (PARAFAC2) to 2 nd derivative absorbance chromatograms. It is capable of isolating individual elution profiles of analytes despite co-elution and overall improves sensitivity and specificity, compared to manual integration methods. For most lignin phenols, detection limits below 5 nmol L -1 were achieved, which is comparable to MS detection. The reproducibility across all laboratory stages for our reference material showed a relative standard deviation between 1.47% and 16.84% for all 11 lignin phenols. Changing the amount of DOM in the reaction vessel for the oxidation (dissolved organic carbon between 22 and 367 mmol L -1 ), did not significantly affect the final lignin phenol composition. The new method was applied to seawater samples from the Kattegat and Davis Strait. The total concentration of dissolved lignin phenols measured in the two areas was between 4.3-10.1 and 2.1-3.2 nmol L -1 , respectively, which is within the range found by other studies. Comparison with a different oxidation approach and detection method (GC-MS) gave similar results and underline the potential of LC and absorbance detection for analysis of dissolved lignin with our proposed method. Article in Journal/Newspaper Davis Strait Technical University of Denmark: DTU Orbit |
institution |
Open Polar |
collection |
Technical University of Denmark: DTU Orbit |
op_collection_id |
ftdtupubl |
language |
English |
description |
Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC-MS or LC-MS). MS instrumentation can be relatively expensive to purchase and maintain. Here we present an improved approach for quantification of lignin phenols using LC and absorbance detection. The approach applies a modified version of parallel factor analysis (PARAFAC2) to 2 nd derivative absorbance chromatograms. It is capable of isolating individual elution profiles of analytes despite co-elution and overall improves sensitivity and specificity, compared to manual integration methods. For most lignin phenols, detection limits below 5 nmol L -1 were achieved, which is comparable to MS detection. The reproducibility across all laboratory stages for our reference material showed a relative standard deviation between 1.47% and 16.84% for all 11 lignin phenols. Changing the amount of DOM in the reaction vessel for the oxidation (dissolved organic carbon between 22 and 367 mmol L -1 ), did not significantly affect the final lignin phenol composition. The new method was applied to seawater samples from the Kattegat and Davis Strait. The total concentration of dissolved lignin phenols measured in the two areas was between 4.3-10.1 and 2.1-3.2 nmol L -1 , respectively, which is within the range found by other studies. Comparison with a different oxidation approach and detection method (GC-MS) gave similar results and underline the potential of LC and absorbance detection for analysis of dissolved lignin with our proposed method. |
format |
Article in Journal/Newspaper |
author |
Bruhn, Anders Dalhoff Wunsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. |
spellingShingle |
Bruhn, Anders Dalhoff Wunsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
author_facet |
Bruhn, Anders Dalhoff Wunsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. |
author_sort |
Bruhn, Anders Dalhoff |
title |
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
title_short |
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
title_full |
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
title_fullStr |
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
title_full_unstemmed |
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
title_sort |
lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data |
publishDate |
2023 |
url |
https://orbit.dtu.dk/en/publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf https://doi.org/10.1002/lom3.10561 https://backend.orbit.dtu.dk/ws/files/332288087/Limnology_Ocean_Methods_2023_Bruhn.pdf https://doi.org/10.11583/DTU.22182694.v1 https://doi.org/10.11583/DTU.22178825.v1 |
genre |
Davis Strait |
genre_facet |
Davis Strait |
op_source |
Bruhn , A D , Wunsch , U , Osburn , C L , Rudolph , J C & Stedmon , C A 2023 , ' Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data ' , Limnology and Oceanography: Methods , vol. 21 , no. 8 , pp. 508-528 . https://doi.org/10.1002/lom3.10561 |
op_relation |
https://orbit.dtu.dk/en/publications/f5c4c35b-6da8-4a10-bc11-4772bce390cf |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1002/lom3.1056110.11583/DTU.22182694.v110.11583/DTU.22178825.v1 |
_version_ |
1810441027665788928 |