Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions
Water-extractable organic matter (WEOM) is labile and a key component of soil organic matter. Thus, the prediction of soil WEOM concentration and composition-related characteristics is of great interest. The main objective of this study was to model and predict dissolved organic C (DOC) concentratio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Geoderma |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706121007588 |
| _version_ | 1797769067512201216 |
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| author | Alla Nasonova Guy J. Levy Oshri Rinot Gil Eshel Mikhail Borisover |
| author_facet | Alla Nasonova Guy J. Levy Oshri Rinot Gil Eshel Mikhail Borisover |
| author_sort | Alla Nasonova |
| collection | DOAJ |
| description | Water-extractable organic matter (WEOM) is labile and a key component of soil organic matter. Thus, the prediction of soil WEOM concentration and composition-related characteristics is of great interest. The main objective of this study was to model and predict dissolved organic C (DOC) concentration and WEOM composition-related attributes of aqueous soil extracts using mid-infrared (IR) spectra of bulk soils coupled with partial least square (PLS) regression. Absorbance of UV light at 254 nm (Abs254), considered proportional to the concentration of aromatic substances in soil extracts, and light emission intensities proportional to concentrations of some components controlling WEOM fluorescence, were used as composition-related attributes of the soil extracts. The DOC-normalized Abs254 and emission intensities were used as composition-related attributes of WEOM. Application of PLS regressions for predicting spectroscopy-based composition-related attributes of aqueous soil extracts, using bulk soil IR spectra, is novel. Mid-IR spectra were determined for 216 soil samples collected from different (i) Israeli climate regions (Mediterranean and Semi-arid), (ii) types of land use (field crops, orchard and non-cultivated land), (iii) two depths (0–10 and 30–60 cm), (iv) sampling seasons (Fall and Spring). Prediction of DOC concentrations in the soil extracts was of limited and variable success evaluated by the coefficient of determination and slope of the linear regression of predicted vs measured values, with some soil subsets yielding no satisfactory predictions. However, Abs254 and emission intensities of fluorescent humic-like components were predicted more successfully than DOC concentrations, suggesting that WEOM aromatic and fluorescent components are better presented in soil IR spectra compared to WEOM aliphatic substances. Yet, prediction of the specific UV absorbance (SUVA), using bulk soil IR spectra, was less successful than prediction of Abs254. Prediction of DOC-normalized emission intensities of fluorescent components (analogous to SUVA in fluorescence spectroscopy) was not successful. The differences between the success of predicting extract properties, i.e., Abs254 and fluorescence emission intensities, and the prediction of DOC-normalized derivatives, suggest that concentrations of aromatic (fluorescent) components in soil extracts are better predicted using PLS regression analysis of bulk soil mid-IR spectra than the WEOM composition. |
| first_indexed | 2024-03-12T21:02:29Z |
| format | Article |
| id | doaj.art-e88b38127c414750a036e55e990025c1 |
| institution | Directory Open Access Journal |
| issn | 1872-6259 |
| language | English |
| last_indexed | 2024-03-12T21:02:29Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj.art-e88b38127c414750a036e55e990025c12023-07-31T04:08:58ZengElsevierGeoderma1872-62592022-04-01411115678Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressionsAlla Nasonova0Guy J. Levy1Oshri Rinot2Gil Eshel3Mikhail Borisover4Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Institute, P.O. Box 15159, Rishon LeZion 7505101, IsraelInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Institute, P.O. Box 15159, Rishon LeZion 7505101, IsraelInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Institute, P.O. Box 15159, Rishon LeZion 7505101, IsraelSoil Erosion Research Station, Ministry of Agriculture and Rural Development, P.O.Box 30, HaMaccabim Road, Rishon LeZion 50200, IsraelInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Institute, P.O. Box 15159, Rishon LeZion 7505101, Israel; Corresponding author.Water-extractable organic matter (WEOM) is labile and a key component of soil organic matter. Thus, the prediction of soil WEOM concentration and composition-related characteristics is of great interest. The main objective of this study was to model and predict dissolved organic C (DOC) concentration and WEOM composition-related attributes of aqueous soil extracts using mid-infrared (IR) spectra of bulk soils coupled with partial least square (PLS) regression. Absorbance of UV light at 254 nm (Abs254), considered proportional to the concentration of aromatic substances in soil extracts, and light emission intensities proportional to concentrations of some components controlling WEOM fluorescence, were used as composition-related attributes of the soil extracts. The DOC-normalized Abs254 and emission intensities were used as composition-related attributes of WEOM. Application of PLS regressions for predicting spectroscopy-based composition-related attributes of aqueous soil extracts, using bulk soil IR spectra, is novel. Mid-IR spectra were determined for 216 soil samples collected from different (i) Israeli climate regions (Mediterranean and Semi-arid), (ii) types of land use (field crops, orchard and non-cultivated land), (iii) two depths (0–10 and 30–60 cm), (iv) sampling seasons (Fall and Spring). Prediction of DOC concentrations in the soil extracts was of limited and variable success evaluated by the coefficient of determination and slope of the linear regression of predicted vs measured values, with some soil subsets yielding no satisfactory predictions. However, Abs254 and emission intensities of fluorescent humic-like components were predicted more successfully than DOC concentrations, suggesting that WEOM aromatic and fluorescent components are better presented in soil IR spectra compared to WEOM aliphatic substances. Yet, prediction of the specific UV absorbance (SUVA), using bulk soil IR spectra, was less successful than prediction of Abs254. Prediction of DOC-normalized emission intensities of fluorescent components (analogous to SUVA in fluorescence spectroscopy) was not successful. The differences between the success of predicting extract properties, i.e., Abs254 and fluorescence emission intensities, and the prediction of DOC-normalized derivatives, suggest that concentrations of aromatic (fluorescent) components in soil extracts are better predicted using PLS regression analysis of bulk soil mid-IR spectra than the WEOM composition.http://www.sciencedirect.com/science/article/pii/S0016706121007588Dissolved organic matterInfraredPartial least squareChemometricsFluorescent matterchromophoric DOM |
| spellingShingle | Alla Nasonova Guy J. Levy Oshri Rinot Gil Eshel Mikhail Borisover Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions Geoderma Dissolved organic matter Infrared Partial least square Chemometrics Fluorescent matter chromophoric DOM |
| title | Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions |
| title_full | Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions |
| title_fullStr | Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions |
| title_full_unstemmed | Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions |
| title_short | Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions |
| title_sort | organic matter in aqueous soil extracts prediction of compositional attributes from bulk soil mid ir spectra using partial least square regressions |
| topic | Dissolved organic matter Infrared Partial least square Chemometrics Fluorescent matter chromophoric DOM |
| url | http://www.sciencedirect.com/science/article/pii/S0016706121007588 |
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