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פותח על ידי קלירמאש פתרונות בע"מ -
Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions
Year:
2022
Source of publication :
Geoderma
Authors :
אשל, גיל
;
.
בוריסובר, מיכאל
;
.
לוי, גיא
;
.
נסונובה, אלה
;
.
רינות, אושרי
;
.
Volume :
411
Co-Authors:

Alla Nasonova
Guy J. Levy
Oshri Rinot
Gil Eshel
Mikhail Borisover

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

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.

Note:
Related Files :
Chemometrics
chromophoric DOM
dissolved organic matter
Fluorescent matter
Infrared
Partial least squares
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.geoderma.2021.115678
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
57867
Last updated date:
02/03/2022 17:27
Creation date:
09/02/2022 17:37
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Scientific Publication
Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions
411

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

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.

Scientific Publication
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