scholarly journals Using carbonate absorbance peak to select the most suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115403
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Dominique Arrouays ◽  
Bernard G. Barthès
Keyword(s):  
Regression Model ◽  
Carbon Concentration ◽  
Inorganic Carbon ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Soil Inorganic Carbon ◽  
Infrared Reflectance Spectroscopy ◽  
Absorbance Peak ◽  
Inorganic Carbon Concentration

Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Bernard G. Barthès
Keyword(s):  
Prediction Model ◽  
Regression Models ◽  
Inorganic Carbon ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Soil Inorganic Carbon ◽  
Infrared Reflectance Spectroscopy ◽  
Absorbance Peak

<p><span>Mid-Infrared reflectance spectroscopy (MIRS, 4000 – 400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to <em>1)</em> evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and <em>2)</em> adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm<sup>-1</sup> for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150 cm<sup>-1</sup>.</span></p><p><span>First, our results showed that PLSR provided <em>1) </em>better performances than SLR on the Validation Tunisian dataset (R<sup>2</sup><sub>test</sub> of 0.99 vs. 0.86, respectively), but <em>2) </em>lower performances than SLR on the Test French dataset (R<sup>2</sup><sub>test</sub> of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (< 15 g/kg) with SLR (RMSE<sub>test</sub> from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSE<sub>test </sub>from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (> 15 g/kg) with PLSR (RMSE<sub>test</sub> from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSE<sub>test</sub> from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150 cm<sup>-1</sup> could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (<em>R²</em><sub><em>test </em></sub>of 0.95 and <em>RMSE</em><sub><em>test</em></sub> of 3.7 g/kg<sup></sup>). </span></p><p><span>Finally, this study demonstrated that the use of the spectral absorbance peak at 2150 cm<sup>-1</sup> provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.</span></p>

Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library

2020 ◽  
Author(s):  
Tiphaine Chevallier ◽  
Cécile Gomez ◽  
Patricia Moulin ◽  
Imane Bouferra ◽  
Kaouther Hmaidi ◽  
...  
Keyword(s):  
Inorganic Carbon ◽  
Prediction Models ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Climate Conditions ◽  
Infrared Reflectance Spectroscopy ◽  
Soil Database

<p>Mid-Infrared Reflectance Spectroscopy (MIRS, 4000–400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This has mainly been demonstrated when datasets used to build, validate and test the prediction model originate from the same area A, with similar geopedological conditions. The objective of this study was to analyze how MIRS performed when used to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 soil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS soil dataset including 96 soil samples to test them. Our results showed that using the French MIRS soil database i) SOC and SIC of French samples were successfully predicted, ii) SIC of Tunisian samples was also predicted successfully, iii) local calibration significantly improved SOC prediction of Tunisian samples and iv) prediction models seemed more robust for SIC than for SOC. So in future, MIRS might replace, or at least be considered as, a conventional physico-chemical analysis technique, especially when as exhaustive as possible calibration database will become available.</p>

Mid-infrared reflectance spectroscopy of carbonaceous chondrites and Calcium–Aluminum-rich inclusions

2020 ◽  
Vol 193 ◽  
pp. 105078 ◽  
Author(s):  
Andreas Morlok ◽  
Benjamin Schiller ◽  
Iris Weber ◽  
Mohit Melwani Daswani ◽  
Aleksandra N. Stojic ◽  
...  
Keyword(s):  
Reflectance Spectroscopy ◽  
Carbonaceous Chondrites ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy

Near versus Mid-Infrared Spectroscopy for Quantitative Analysis of Chlorite Treated Forages and By-Products

1994 ◽  
Vol 2 (3) ◽  
pp. 153-162 ◽  
Author(s):  
James B. Reeves
Keyword(s):  
Infrared Spectroscopy ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Sodium Chlorite ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy ◽  
Grating Monochromator ◽  
By Products

The objective of this work was to explore the relative merits of near and mid-infrared diffuse reflectance spectroscopy in determining the composition of sodium chlorite treated forages and by-products. Sixteen feed-stuffs (174 total samples treated at one of 11 levels of sodium chlorite, 0 to 0.394 g per gram of feedstuff) were examined in the near and mid-infrared spectral regions using diffuse reflectance on a Fourier transform spectrometer, and in the near infrared region using a grating monochromator. Samples were scanned as is and as 5% sample in KBr on the Fourier spectrometer and as is on the grating monochromator. Samples were analysed chemically and spectroscopically for neutral and acid detergent fibre, in vitro digestibility, permanganate lignin, crude protein and lignin nitrobenzene oxidation products. Results showed that diffuse mid-infrared reflectance spectroscopy can perform as well as, and sometimes better than, diffuse near infrared reflectance spectroscopy in determining the composition of chlorite-treated forages and by-products. In addition, Fourier near infrared spectroscopy did not perform as well as either near infrared using a grating monochromator or the Fourier mid-infrared spectrometer. Finally, diluting samples with KBr was often beneficial for mid-infrared based determinations.

Ultraviolet-Visible, near Infrared and Mid Infrared Reflectance Spectroscopy of Turquoise

2006 ◽  
Vol 14 (4) ◽  
pp. 241-250 ◽  
Author(s):  
B. Jagannatha Reddy ◽  
Ray L. Frost ◽  
Matt L. Weier ◽  
Wayde N. Martens
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy

Mid-Infrared Reflectance Spectroscopy of Oil Sands Minerals Based on Tunable Quantum Cascade Lasers

2020 ◽  
Vol 74 (7) ◽  
pp. 832-837 ◽  
Author(s):  
Aïssa Harhira ◽  
Francis Vanier ◽  
Christian Padioleau ◽  
Josette El Haddad ◽  
Alain Blouin
Keyword(s):  
Oil Sands ◽  
Extraction Efficiency ◽  
Quantum Cascade Lasers ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Quantum Cascade ◽  
Infrared Reflectance Spectroscopy ◽  
Cascade Lasers ◽  
Mineral Type

Minerals play an important role in the oil sands extraction efficiency. It is thus important to assess the major mineral abundance in oil sands ores. This paper presents the application of tunable quantum cascade lasers for mid-infrared reflectance spectroscopy on oil sands minerals. The investigations and results show a new tool to determine oil sands mineral type and to determine potentially quartz and clay contents.

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