Feasibility of handheld mid-infrared spectroscopy to predict particle size distribution: influence of soil field condition and utilisation of existing spectral libraries

Partial least-squares regression (PLSR), using spectra from a handheld mid-infrared instrument (the ExoScan), was tested for the prediction of particle size distribution. Soils were sampled from agricultural sites in the Eyre Peninsula under field conditions and with varying degrees of soil preparation. Issues relevant to field sampling were identified, such as sample heterogeneity, micro-aggregate size and moisture content. The PLSR models for particle size distribution were derived with the varying degrees of preparation. Cross-validation of clay content in the as-received in situ soils resulted in low accuracy: coefficient of determination (R2) = 0.55 and root mean square error (RMSE) = 7%. This was improved by manual mixing, drying, sieving to < 2 mm and fine grinding, resulting in R2 values of 0.64, 0.75 and 0.81, and RMSE of 6%, 5% and 4% respectively; less improvement resulted for sand, with corresponding R2 values of 0.82, 0.88, 0.91 and 0.89, and RMSE of 10%, 8%, 6% and 7%. Predictions for silt remained poor. Where only archival benchtop calibration models were available, predictions of clay contents for spectra scanned with the handheld ExoScan spectrometer resulted in high error because of spectral intensity mismatch between benchtop and handheld spectra (R2 = 0.72, RMSE = 24.2% and bias = 21%). Pre-processing the benchtop spectra by piecewise direct standardisation resulted in more successful predictions (R2 = 0.73, RMSE = 6.7% and bias = –1.5%), confirming the advantage of piecewise direct standardisation for prediction from archival spectral libraries.

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Effects of soil composition and preparation on the prediction of particle size distribution using mid-infrared spectroscopy and partial least-squares regression

Soil Research ◽

10.1071/sr16011 ◽

2016 ◽

Vol 54 (8) ◽

pp. 889 ◽

Cited By ~ 8

Author(s):

Leslie J. Janik ◽

José M. Soriano-Disla ◽

Sean T. Forrester ◽

Michael J. McLaughlin

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Least Squares ◽

Size Distribution ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Calcareous Soils ◽

Least Squares Regression ◽

Soil Composition ◽

Mid Infrared

Soil composition and preparation can affect prediction accuracy using diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS). In the present study, we evaluated the effect of soil composition, preparation and carbonate content on the accuracy of particle size distribution (PSD) predictions in four contrasting sets of soils, including calcareous soils, using partial least-squares regression (PLSR). The soils were scanned as <2- and <0.1-mm fine-ground samples. Regression calibrations were derived for individual soil sets, as well as a composite of the four sets. Predictions for clay and sand for the <2-mm composite calibration resulted in good accuracy (coefficient of determination R2=0.78; ratio of the standard deviation of reference values to the prediction error (RPD)=2.2), but were less accurate for clay in the calcareous soils (R2=0.70–0.78; RPD=1.8–1.1) and similarly accurate for sand (R2=0.68–0.80; RPD=1.7–2.2). Predictions for silt were poor. Accuracies improved by fine grinding (R2=0.88, RPD=2.9 for clay; R2=0.84, RPD=2.9 for sand). It was concluded that single, large and highly variable sets rather than site-specific calibrations could be used for the PSD predictions of specific soil sets. Changes in the PLSR loading weights, resulting from grinding, could be linked to an improved access of the infrared beam to the soil matrix by removal or dilution of surface coatings, resulting in a reduction of inter- and intraparticulate heterogeneity.

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Fractal dimension of particle-size distribution and their relationships with alkalinity properties of soils in the western Songnen Plain, China

Scientific Reports ◽

10.1038/s41598-020-77676-w ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yufeng Bai ◽

Yan Qin ◽

Xinrui Lu ◽

Jitao Zhang ◽

Guoshuang Chen ◽

...

Keyword(s):

Particle Size ◽

Fractal Dimension ◽

Particle Size Distribution ◽

Size Distribution ◽

Linear Regression Analysis ◽

Salt Content ◽

Clay Content ◽

Fine Sand ◽

Alkaline Soil ◽

Songnen Plain

AbstractThe purpose of this study was to identify the fractal dimension and their relationships with alkalinity properties of soils, and to evaluate the potential of fractal dimension as an indicator of alkalinity properties of soil. Six soils with an increasing salinity (electrical conductivity was 0.09, 0.18, 0.62, 0.78, 1.57 and 1.99 dS m−1, respectively) were selected from the western part of the Songnen Plain (China). Salt content, exchangeable sodium percentage, sodium adsorption ratio and other properties of the soils were determined and the soil particle-size distribution (0–2000 μm) was measured using a laser diffraction particle size analyser. Our results show that the overall fractal dimension of the selected soils ranged from 2.35 to 2.60. A linear regression analysis showed a significant negative correlation between fractal dimension and the amount of coarse sand and fine sand (r = − 0.5452, P < 0.05 and r = − 0.8641, P < 0.01, respectively), and a significant positive correlation with silt and clay (r = 0.9726, P < 0.01 and r = 0.9526, P < 0.01, respectively). Thus, soils with higher silt and clay content have higher fractal dimension values. Strong linear relationships between fractal dimension and salt content (P < 0.05), in particular a very significant positive relationship with HCO3− (P < 0.01), also exist. It is therefore possible to conclude that a soil’s fractal dimension could serve as a potential indicator of soil alkalization and the variability in alkaline soil texture.

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Particle-Size Distribution Models for the Conversion of Chinese Data to FAO/USDA System

The Scientific World JOURNAL ◽

10.1155/2014/109310 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Wei Shangguan ◽

YongJiu Dai ◽

Carlos García-Gutiérrez ◽

Hua Yuan

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Soil Texture ◽

Texture Classification ◽

Geometric Mean ◽

Coefficient Of Determination ◽

Logistic Growth ◽

United States Department ◽

Data Points

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski’s schemes with five and six data points, respectively. The adjusted coefficient of determinationr2, Akaike’s information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

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Aggregate and Particle Size Distribution of the Soil Sediment Eroded on Steep Artificial Slopes

Applied Sciences ◽

10.3390/app11104427 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4427

Author(s):

Romana Kubínová ◽

Martin Neumann ◽

Petr Kavka

Keyword(s):

Particle Size ◽

Soil Erosion ◽

Particle Size Distribution ◽

Size Distribution ◽

Soil Aggregates ◽

Aggregate Size ◽

Soil Particle ◽

Initial State ◽

Erosion Processes ◽

Soil Sediment

In this study, the particle size distribution (PSD) of the soil sediment from topsoil obtained from soil erosion experiments under different conditions was measured. Rainfall simulators were used for rain generation on the soil erosion plots with slopes 22°, 30°, 34° and length 4.25 m. The influence of the external factors (slope and initial state) on the particle and aggregate size distribution were evaluated by laser diffractometer (LD). The aggregate representation percentage in the eroded sediment was also investigated. It has been found that when the erosion processes are intensive (steep slope or long duration of the raining), the eroded sediment contains coarser particles and lower amounts of aggregates. Three methods for the soil particle analyses were tested: (i) conventional–sieving and hydrometer method; (ii) PARIO Soil Particle Analyzer combined with sieving; and (iii) laser diffraction (LD) using Mastersizer 3000. These methods were evaluated in terms of reproducibility of the results, time demands, and usability. It was verified that the LD has significant advantages compared to other two methods, especially the short measurement time for one sample (only 15 min per sample for LD) and the possibility to destroy soil aggregates using ultrasound which is much easier than using hexametaphosphate.

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Evaluation of Sand Quality and its Effect on Mortar and Cement Concrete

Science & Technology Journal ◽

10.22232/stj.2020.08.02.10 ◽

2020 ◽

Vol 8 (2) ◽

pp. 62-68

Author(s):

H. Laldintluanga ◽

◽

Rebecca Ramhmachhuani ◽

Ram thlengliani

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Clay Content ◽

Raw Material ◽

Fine Aggregate ◽

River Sand ◽

Proctor Test ◽

Additional Water ◽

Different Sources

The most used raw material in concrete are cement, sand and coarse aggregate. The study involved performing a series of tests on river sand and crushed sand that was collected from different sources to find the feasibility of commonly used sand in Mizoram. The properties of sand have been checked in terms of particle size distribution, fineness modulus, specific gravity. The effect of different sources of sand which are having different properties in mortar and concrete has been investigated. The sand was collected from different source having different particle size distribution as well as different silt and clay content. The quantity of water calculated based on normal consistency value cannot be applied to mortar which has sand having cohesion. Optimum moisture content calculated using the Standard Proctor test for cohesive sand is used to find out the additional water required on the mortar mix. The mortar with an exact amount of water has higher strength and density. The bonding is weakened in strength by non- binding material like silt and clay. Test results show that a decrease in compressive strength when the ratio of silt content to fine aggregate increase. It is found out that there is a large variation in the strength of mortar and concrete due to variation in the quality of sand use.

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Fitting Performance of Different Models on Loess Particle Size Distribution Curves

Advances in Materials Science and Engineering ◽

10.1155/2017/6295078 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Wei Liu ◽

Wenwu Chen ◽

Jun Bi ◽

Gaochao Lin ◽

Weijiang Wu ◽

...

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Empirical Model ◽

Characteristic Curve ◽

Clay Content ◽

Gompertz Model ◽

Test Time ◽

Malan Loess ◽

The Relationship

The soil water characteristic curve (SWCC) describes the relationship between matric suction and moisture of soil, the testing process of which is time-consuming. The test time of particle size distribution (PSD), in contrast, is relatively short. Thus, it is quite important to establish a proper model for PSD to forecast SWCC. This paper analyzed PSD of 25 groups of loess by way of laser diffraction technique (LD) and sieve-settlement method. Works were carried out on fitting analysis on PSD with Logarithmic model, Fredlund model, Jaky model, and Gompertz model. Statistical method was used to explain the fitting performance. Meanwhile, an empirical model was put forward. Compared to the four models, the empirical model has fewer parameters, simple model form, and smaller fluctuations of parameters. Results of LD showed higher clay content but lower silt content. It is suggested that Fredlund model or the empirical model be adopted to forecast SWCC of Malan loess.

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