Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils

Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, are time-consuming a...

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Main Authors: Wetterlind, J., Viscarra Rossel, Raphael, Steffens, M.
Format: Journal Article
Language:English
Published: WILEY 2022
Subjects:
Online Access:http://purl.org/au-research/grants/arc/DP210100420
http://hdl.handle.net/20.500.11937/91030
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author Wetterlind, J.
Viscarra Rossel, Raphael
Steffens, M.
author_facet Wetterlind, J.
Viscarra Rossel, Raphael
Steffens, M.
author_sort Wetterlind, J.
building Curtin Institutional Repository
collection Online Access
description Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, are time-consuming and expensive. Diffuse reflectance spectroscopy in the visible, near infrared and mid-infrared regions (vis–NIR: 350–2500 nm; mid-IR: 4000–400 cm−1) can also be used to characterise SOC chemistry; however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13C NMR to the frequencies in the vis–NIR and mid-IR spectra using two methods: (1) 2-dimensional correlations of 13C NMR spectra and the diffuse reflectance spectra, and (2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (SVM) (validated using 5 times repeated 10-fold cross-validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid-IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid-IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R2 = 0.83 and 0.85, vis–NIR and mid-IR, respectively), and the O/N-alkyl C group was the most difficult to estimate (R2 = 0.34 and 0.38, vis–NIR and mid-IR, respectively). Combining 13C NMR with the cost-effective diffuse reflectance methods could potentially increase the number of measured samples and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these methods could be used. Highlights: Diffuse reflectance spectroscopy was used to characterise and model SOC functional chemistry. NMR derived C functional groups could be modelled with vis-NIR and mid-IR diffuse reflectance spectra. The methods allow for characterisation of SOC chemical composition on whole mineral soil samples. The approach can improve the spatial and temporal characterisation of SOC composition.
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spelling curtin-20.500.11937-910302023-05-22T08:09:06Z Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils Wetterlind, J. Viscarra Rossel, Raphael Steffens, M. Science & Technology Life Sciences & Biomedicine Soil Science Agriculture C-13 NMR C functional groups C turnover mid-IR spectroscopy NIR spectroscopy soil organic matter composition soil organic matter quality NEAR-INFRARED SPECTROSCOPY HYDROFLUORIC-ACID IR SPECTROSCOPY MATTER FRACTIONS QUALITY PREDICT REGRESSION SPECTRA Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, are time-consuming and expensive. Diffuse reflectance spectroscopy in the visible, near infrared and mid-infrared regions (vis–NIR: 350–2500 nm; mid-IR: 4000–400 cm−1) can also be used to characterise SOC chemistry; however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13C NMR to the frequencies in the vis–NIR and mid-IR spectra using two methods: (1) 2-dimensional correlations of 13C NMR spectra and the diffuse reflectance spectra, and (2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (SVM) (validated using 5 times repeated 10-fold cross-validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid-IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid-IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R2 = 0.83 and 0.85, vis–NIR and mid-IR, respectively), and the O/N-alkyl C group was the most difficult to estimate (R2 = 0.34 and 0.38, vis–NIR and mid-IR, respectively). Combining 13C NMR with the cost-effective diffuse reflectance methods could potentially increase the number of measured samples and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these methods could be used. Highlights: Diffuse reflectance spectroscopy was used to characterise and model SOC functional chemistry. NMR derived C functional groups could be modelled with vis-NIR and mid-IR diffuse reflectance spectra. The methods allow for characterisation of SOC chemical composition on whole mineral soil samples. The approach can improve the spatial and temporal characterisation of SOC composition. 2022 Journal Article http://hdl.handle.net/20.500.11937/91030 10.1111/ejss.13263 English http://purl.org/au-research/grants/arc/DP210100420 http://creativecommons.org/licenses/by-nc-nd/4.0/ WILEY fulltext
spellingShingle Science & Technology
Life Sciences & Biomedicine
Soil Science
Agriculture
C-13 NMR
C functional groups
C turnover
mid-IR spectroscopy
NIR spectroscopy
soil organic matter composition
soil organic matter quality
NEAR-INFRARED SPECTROSCOPY
HYDROFLUORIC-ACID
IR SPECTROSCOPY
MATTER
FRACTIONS
QUALITY
PREDICT
REGRESSION
SPECTRA
Wetterlind, J.
Viscarra Rossel, Raphael
Steffens, M.
Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title_full Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title_fullStr Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title_full_unstemmed Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title_short Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
title_sort diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
topic Science & Technology
Life Sciences & Biomedicine
Soil Science
Agriculture
C-13 NMR
C functional groups
C turnover
mid-IR spectroscopy
NIR spectroscopy
soil organic matter composition
soil organic matter quality
NEAR-INFRARED SPECTROSCOPY
HYDROFLUORIC-ACID
IR SPECTROSCOPY
MATTER
FRACTIONS
QUALITY
PREDICT
REGRESSION
SPECTRA
url http://purl.org/au-research/grants/arc/DP210100420
http://hdl.handle.net/20.500.11937/91030