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Mogens Humlekrog Greve

Soil Specific Surface Area Determination by Visible Near-Infrared Spectroscopy

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Soil Specific Surface Area Determination by Visible Near-Infrared Spectroscopy. / Knadel, Maria; Arthur, Emmanuel; Jensen, Peter Weber; Møldrup, Per; Greve, Mogens Humlekrog; Chrysodonta, Zampela Pittaki; de Jonge, Lis Wollesen.

I: Soil Science Society of America Journal, Bind 82, Nr. 5, 2018, s. 1046-1056.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Knadel, Maria ; Arthur, Emmanuel ; Jensen, Peter Weber ; Møldrup, Per ; Greve, Mogens Humlekrog ; Chrysodonta, Zampela Pittaki ; de Jonge, Lis Wollesen. / Soil Specific Surface Area Determination by Visible Near-Infrared Spectroscopy. I: Soil Science Society of America Journal. 2018 ; Bind 82, Nr. 5. s. 1046-1056.

Bibtex

@article{11bf99be04a940d7982a053b7bcc8026,
title = "Soil Specific Surface Area Determination by Visible Near-Infrared Spectroscopy",
abstract = "The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near-infrared spectroscopy (Vis–NIRS) to estimate clay content (particles < 2 μm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis–NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis–NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3–437 m2 g–1) and clay content (1–83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). Utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo-mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non-complexed organic carbon on the mineral phases of some of the soils.",
author = "Maria Knadel and Emmanuel Arthur and Jensen, {Peter Weber} and Per M{\o}ldrup and Greve, {Mogens Humlekrog} and Chrysodonta, {Zampela Pittaki} and {de Jonge}, {Lis Wollesen}",
year = "2018",
doi = "10.2136/sssaj2018.03.0093",
language = "English",
volume = "82",
pages = "1046--1056",
journal = "Soil Science Society of America Journal",
issn = "0361-5995",
publisher = "Soil Science Society of America",
number = "5",

}

RIS

TY - JOUR

T1 - Soil Specific Surface Area Determination by Visible Near-Infrared Spectroscopy

AU - Knadel, Maria

AU - Arthur, Emmanuel

AU - Jensen, Peter Weber

AU - Møldrup, Per

AU - Greve, Mogens Humlekrog

AU - Chrysodonta, Zampela Pittaki

AU - de Jonge, Lis Wollesen

PY - 2018

Y1 - 2018

N2 - The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near-infrared spectroscopy (Vis–NIRS) to estimate clay content (particles < 2 μm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis–NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis–NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3–437 m2 g–1) and clay content (1–83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). Utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo-mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non-complexed organic carbon on the mineral phases of some of the soils.

AB - The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near-infrared spectroscopy (Vis–NIRS) to estimate clay content (particles < 2 μm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis–NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis–NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3–437 m2 g–1) and clay content (1–83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). Utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo-mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non-complexed organic carbon on the mineral phases of some of the soils.

U2 - 10.2136/sssaj2018.03.0093

DO - 10.2136/sssaj2018.03.0093

M3 - Journal article

VL - 82

SP - 1046

EP - 1056

JO - Soil Science Society of America Journal

JF - Soil Science Society of America Journal

SN - 0361-5995

IS - 5

ER -