Lignin phenol quantification from machine learning‐assisted decomposition of liquid chromatography‐absorbance spectroscopy data
Abstract 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 instrumentat...
Published in: | Limnology and Oceanography: Methods |
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Online Access: | http://dx.doi.org/10.1002/lom3.10561 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10561 |
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crwiley:10.1002/lom3.10561 2024-09-15T18:03:32+00:00 Lignin phenol quantification from machine learning‐assisted decomposition of liquid chromatography‐absorbance spectroscopy data Bruhn, Anders Dalhoff Wünsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. Danmarks Frie Forskningsfond National Science Foundation 2023 http://dx.doi.org/10.1002/lom3.10561 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10561 en eng Wiley http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ Limnology and Oceanography: Methods volume 21, issue 8, page 508-528 ISSN 1541-5856 1541-5856 journal-article 2023 crwiley https://doi.org/10.1002/lom3.10561 2024-08-30T04:12:57Z Abstract 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 Wiley Online Library Limnology and Oceanography: Methods 21 8 508 528 |
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Wiley Online Library |
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English |
description |
Abstract 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. |
author2 |
Danmarks Frie Forskningsfond National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Bruhn, Anders Dalhoff Wünsch, Urban Osburn, Christopher L. Rudolph, Jacob C. Stedmon, Colin A. |
spellingShingle |
Bruhn, Anders Dalhoff Wünsch, 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 Wünsch, 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 |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1002/lom3.10561 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10561 |
genre |
Davis Strait |
genre_facet |
Davis Strait |
op_source |
Limnology and Oceanography: Methods volume 21, issue 8, page 508-528 ISSN 1541-5856 1541-5856 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/lom3.10561 |
container_title |
Limnology and Oceanography: Methods |
container_volume |
21 |
container_issue |
8 |
container_start_page |
508 |
op_container_end_page |
528 |
_version_ |
1810441027494871040 |