Effects of Moisture and Particle Size on Quantitative Determination of Total Organic Carbon (TOC) in Soils Using Near-Infrared Spectroscopy

Tamburini, Elena; Vincenzi, Fabio; Costa, Stefania; Mantovi, Paolo; Pedrini, Paola; Castaldelli, Giuseppe

doi:10.3390/s17102366

Cited by 26 publications

(14 citation statements)

References 58 publications

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“…Soil particle size was reduced to <200 mm for S-1 and S-2, thus breaking down soil structure by reducing micro-aggregates (<2 mm) to organo-mineral complexes, and exposing more SOC for spectral measurement. Previous studies on the effects of soil particle size and sample preparation such as Wu et al, 26 Yao et al 27 and Tamburini et al 28 emphasized that smaller soil particle size resulted in the higher prediction accuracy of soil properties. Soil particles below 200 mm cannot improve the prediction accuracy of soil organic matter (SOM).…”

Section: Discussionmentioning

confidence: 99%

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A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

Askari

O’Rourke

Holden

2018

Journal of Near Infrared Spectroscopy

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This study evaluated whether the accuracy of soil organic carbon measurement by laboratory hyperspectral imaging can match that of standard point spectroscopy operating in the visible-near infrared. Hyperspectral imaging allows a greater amount of spectral information to be collected from the soil sample compared to standard spectroscopy, accounting for greater sample representation. A total of 375 representative Irish soils were scanned by two-point spectrometers (a Foss NIR Systems 6500 labelled S-1 and a Varian FT-IR 3100 labelled S-2) and two laboratory hyperspectral imaging systems (two push broom line-scanning hyperspectral imaging systems manufactured by DV optics and Spectral Imaging Ltd, respectively, labelled S-3 and S-4). The objectives were (a) to compare the predictive ability of spectral datasets for soil organic carbon prediction for each instrument evaluated and (b) to assess the impact of imposing a common wavelength range and spectral resolution on soil organic carbon model accuracy. These objectives examined the predictive ability of spectral datasets for soil organic carbon prediction based on optimal settings of each instrument in (a) and introduced a constraint in wavelength range and spectral resolution to achieve common settings for instruments in (b). Based on optimal settings for each instrument, the deviation (root-mean square error of prediction) from the best fit line between laboratory measured and predicted soil organic carbon, ranked the instruments as S-1 (26.3 g kg À1) < S-2 (29.4 g kg À1) < S-3 (34.3 g kg À1) < S-4 (41.1 g kg À1). The S-1 model outperformed in all partial least squares regression performance indicators, and across all spectral ranges, and produced the most favourable outcomes in means testing, variance testing and identification of significant variables. It is assumed that a larger wavelength range produced more accurate soil organic carbon predictions for S-1 and S-2. Under common instrument settings, the prediction accuracy for S-3 that was almost equal to S-1. It is concluded that under standard operating procedures, greater soil sample representation captured by hyperspectral imaging can equal the quality of the spectra from point spectroscopy. This result is important for the development of laboratory hyperspectral imaging for soil image analysis.

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Section: Discussionmentioning

confidence: 99%

“…27 and Tamburini et al. 28 emphasized that smaller soil particle size resulted in the higher prediction accuracy of soil properties. Soil particles below 200 µm cannot improve the prediction accuracy of soil organic matter (SOM).…”

Section: Discussionmentioning

confidence: 99%

A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

Askari

O’Rourke

Holden

2018

Journal of Near Infrared Spectroscopy

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“…The impact of diets on the CP predictability may have been due to variations in particle sizes of the ingredients in the diets. This differences in particle sizes may have caused variation in the surface area, which in turn affected the amounts of light transmitted, absorbed or reflected [17] leading to differences in spectral characteristics (absorbance bands, wavelength, Treatments were analyzed as a 3 × 2 factorial, with method (laboratory, NIRS-ground and NIRS-unground), and feed form (mash and pellet).…”

Section: Discussion Experimentsmentioning

confidence: 99%

Determining the Influence of Particle Size, Diets, Analytical Methods and Feed Form on the Predictability of the Near Infrared Reflectance Spectroscopy

Ns¹,

Paulk²,

Tnn³

et al. 2020

J Anim Sci Res

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Volume 4 -Issue 1 where most nutrients are analyzed separately with different methods. The NIRS technique measures light absorption of a feed or ingredient sample when scanned using wavelengths in the near-infrared region (780 to 2500 nm). Spectrum absorption in the NIR region depends on the chemical bonds (C-H, N-H, S-H or O-H) as well as the physical and structural characteristics of the sample. The chemical bonds make it possible to identify specific regions of the spectrum associated with sample constituents such as starch, crude protein or fiber [3].Although the NIRS instrument is highly efficient, variations in sample composition can be observed in results [4]. These variations could be due to factors such as technician, cross-contamination, analysis of a sample that is not present in the installed calibrations, physical form of feed (mash or pellet), particle size, shape, distribution of sample sand also spaces between particles [5,6]. The

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“…In recent decades, as a rapid, nondestructive, and accurate technique, near-infrared (NIR) spectroscopy has been widely used to predict soil nutrient information [ 4 ]. He et al predicted soil macronutrient content using NIR spectroscopy, and showed that it is a very promising method for assessing nitrogen (N), organic matter (OM), and pH in soil [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries

Jia

2020

Sensors

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Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method. A total of 983 soil samples were collected from Zhejiang Province, and three SSLs were built according to geographic scope, representing the provincial, municipal, and district scales. The partial least squares (PLS) algorithm was applied to establish the calibration models based on the three SSLs, and the models were used to predict the SOC of two target areas in Zhejiang Province. The results show that the prediction accuracy of each model was relatively poor regardless of the scale of the SSL (residual predictive deviation (RPD) < 2.5). Then, the Kennard-Stone (KS) algorithm was applied to select 5 or 10 spiking samples from each target area. According to different SSLs and numbers of spiking samples, different spiked models were established by the PLS. The results show that the predictive ability of each model was improved by the spiking method, and the improvement effect was inversely proportional to the scale of the SSL. The spiked models built by combining the district scale SSL and a few spiking samples achieved good prediction of the SOC of two target areas (RPD = 2.72 and 3.13). Therefore, it is possible to accurately measure the SOC of new target areas by building a small-scale SSL with a few spiking samples.

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Effects of Moisture and Particle Size on Quantitative Determination of Total Organic Carbon (TOC) in Soils Using Near-Infrared Spectroscopy

Cited by 26 publications

References 58 publications

A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

Determining the Influence of Particle Size, Diets, Analytical Methods and Feed Form on the Predictability of the Near Infrared Reflectance Spectroscopy

Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries

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