Application of mid-infrared spectroscopy for rapid characterization of key soil properties for engineering land use

Waruru, Bernard K.; Shepherd, Keith D.; Ndegwa, George M.; Sila, Andrew; Kamoni, P.

doi:10.1016/j.sandf.2015.09.018

Cited by 27 publications

(20 citation statements)

References 28 publications

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“…Prediction errors for the suite of soil properties in our study were compared with prediction errors from other studies that have used MIR-DRS coupled with PLSR. Our prediction errors (RMSE P ) were generally lower for validation of models developed from datasets of large geographical areas (McCarty et al, 2002;Minasny et al, 2009;Pirie et al, 2005;Waruru et al, 2015) and global scale datasets (Bornemann et al, 2008;Terhoeven-Urselmans et al, 2010). Lower prediction errors were obtained by others for validation of models developed from field scale datasets (Bornemann et al, 2010;Viscarra-Rossel et al, 2006), where the soil variability would be much lower than in our study.…”

Section: Comparison With Other Studiescontrasting

confidence: 54%

“…Each well (with sample) was then scanned in sequential manner on the VERTEX 70/HTS-XT Fourier transform infrared spectrometer (Bruker Optics Inc.) to acquire spectra from 7500 to 600 cm −1 in the diffuse reflectance mode and optimized for the MIR range. The MIR spectra range below 600 cm −1 contains wavebands with low signal-to-noise ratios and thus was not acquired (Waruru et al, 2015). The spectrometer was equipped with a mercury cadmium telluride detector cooled by liquid nitrogen.…”

Section: Soil Survey Database and Mid-infrared Librarymentioning

confidence: 99%

“…They have usually been corrected or accounted for by transforming the intensities with a Kubelka-Munk function (Brimmer and Griffiths, 1988) or converting the reflectance spectra to absorbance using the log(1/ reflectance) transformation (Janik et al, 1998;Jia et al, 2017). If curvature still exists, the reference data can be transformed (e.g., log e , x , 1/x) before calibration development and then back transformed (Baldock et al, 2013;Janik et al, 2007aJanik et al, , 2007bWaruru et al, 2015) to linearize the relationship. Janik et al (2007a) indicated that the curvature can result from a different chemistry or sample composition corresponding to low concentration values compared with high concentration values.…”

Section: Mid-infrared Model Calibration and Validationmentioning

confidence: 99%

“…Mid-infrared DRS coupled with PLSR can successfully predict several soil properties, such as organic C, cation exchange capacity (CEC), total clay, calcium carbonate, soil pH (D' Acqui et al, 2010;Reeves and Smith, 2009;Viscarra Rossel et al, 2006), and matric suction water contents ( Janik et al, 2007a). Other properties successfully predicted include soil organic C fractions (Knox et al, 2015), plasticity characteristics of clay soils (Kasprzhitskii et al, 2018), Atterberg limits (Waruru et al, 2015), soil metal contents (Siebielec et al, 2004), and the characteristics of soil organic matter (Cox et al, 2000;Madari et al, 2006), peat (Holmgren and Norden, 1988), and particulate organic matter (Bornemann et al, 2010). Because of these successful applications, the use of MIR-DRS and PSLR for predicting soil properties in soil survey has great potential.…”

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confidence: 99%

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Application of Mid‐Infrared Spectroscopy in Soil Survey

Seybold

Ferguson

Wysocki

et al. 2019

Soil Science Soc of Amer J

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Development of more efficient (rapid) and cost-effective methodologies are needed in soil survey to meet the demand for quantitative data in digital soil mapping and updates. The objective of this study was to pilot the application of mid-infrared (MIR)-diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) in a soil survey field office where soil samples are processed, MIR spectra are acquired, and predictions are obtained using calibration models developed and validated from the Kellogg Soil Survey Laboratory spectral library. Mid-infrared models were built for total C, organic C, CaCO 3 equivalent, total clay, cation exchange capacity, 1500 kPa water, and pH in water and CaCl 2 for Mollisols of the central United States. Validation results (from the MIR library) using Lin's concordance correlation (r c) of measured versus predicted values showed that most properties predicted very well (r c = 0.967-0.996), whereas models for total clay in B horizons and 1500 kPa water in B horizons predicted fairly well (r c = 0.844-0.955). Models for pH predicted the least well (r c = 0.750-0.921). The MIR-DRS coupled with PLSR was successful in predicting soil properties for completely independent samples that were collected, processed, and MIR scanned in a soil survey field office. Predicted results using r c ranged from 0.697 to 0.992, with pH in water having the lowest r c and CaCO 3 having the highest r c. All properties except pH had an acceptable level of accuracy for use in soil survey and a marginal level of acceptable accuracy for total clay. Direct calibration transfer was feasible.

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Section: Comparison With Other Studiescontrasting

confidence: 54%

Section: Soil Survey Database and Mid-infrared Librarymentioning

confidence: 99%

Section: Mid-infrared Model Calibration and Validationmentioning

confidence: 99%

mentioning

confidence: 99%

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Application of Mid‐Infrared Spectroscopy in Soil Survey

Seybold

Ferguson

Wysocki

et al. 2019

Soil Science Soc of Amer J

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“…e technique further highlights the close relationship between the physiological states of termite colonies and their environment. To establish correlations between soil and termite mound parameters and MIRS variables, statistical procedures including principal component analysis (PCA) and partial least squares regressions (PLSR) are often used [66,67]. MIR could provide an added advantage of providing a rapid, lowcost, and nondestructive method of analysing soils without the use of chemicals.…”

Section: Midinfrared Spectroscopy (Mirs)mentioning

confidence: 99%

Advances in Nanoscale Study of Organomineral Complexes of Termite Mounds and Associated Soils: A Systematic Review

Eze

Kokwe

Eze

2020

Applied and Environmental Soil Science

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Termite mounds are replete with natural nanoparticles, and they vary in physicochemical, geochemical, mineralogical, and biological properties from the adjoining soils. Although termite mounds have wide ecological and environmental roles including soil formation, faunal and vegetation growth and diversity, organic matter decomposition, geochemical exploration, water survey, treatment of underground contamination, thermoregulation, gas exchange, and global climate change, their nanoscale structures made by the associated organomineral complexes are still poorly understood because of technical limitations. In this review, we highlight the ecological and environmental significance of termite mounds and the documented techniques that have been successfully used to study nanostructure of termite mounds, namely, midinfrared spectroscopy (MIRS), photogrammetry and cross-sectional image analysis, a combination of transmission electron microscopy (TEM) and pyrolysis field ionization mass spectrometry (Py-FIMS), scanning transmission X-ray microscopy (STXM) using synchrotron radiation in conjunction with near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, and high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) for further appraisals. There is a need to continually develop and integrate nanotechnology with the routine classical soil analysis methods to improve our understanding of the functional mechanisms of nanostructure of termite mounds that are responsible for specific properties. In view of the numerous roles termite mounds play in the environments, agriculture, and engineering, there is no better time to channel much research into understanding how they function at nanoscale.

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