Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement

McCarty, Gregory W.; Reeves, James B.; Reeves, Valerie B; Follett, R. F.; Kimble, J. M.

doi:10.2136/sssaj2002.0640

Cited by 205 publications

(218 citation statements)

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“…The region of most interest for chemical analysis is the mid-infrared region (also called intermediate infrared) with the wavenumber range of 4000 to 400 cm -1 ; however recently there raising interest in near-infrared (14290-4000 cm -1 ) and far-infrared (400-100 cm -1 ) was seen [7][8][9]. The entire spectrum of electromagnetic radiation is divided into several bands and the boundaries between them are designated by limits of corresponding experimental methods of producing and detecting radiation.…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Application of Neural Networks in Diagnostics of Chemical Compounds Based on their Infrared Spectra

Macek-Kamińska

Stemplewski

2017

Ecological Chemistry and Engineering S

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Abstract:The paper presents possibilities of using the so-called "finger-print" identification method and artificial neural network (ANN) for diagnosis of chemical compounds. The construction of a tool specifically developed for this purpose and the ANN, as well as the required conditions for its proper functioning were described. The identification of chemical compounds was tested in two different ways for proving correctness of the assumptions. First of all, initial studies were carried out with the objective to verify the proper functioning of the developed procedure for IR spectrum interpretation. The second research stage was to find out how the properties of artificial neural networks will satisfy identification or differentiation in case of spectra with very similar structures or for mixtures consisting of several chemical compounds. Interpretation of infrared spectra of mono-constituent substances was successfully performed for both -the training and test data. Interpretation process of infrared spectra of bi-component substances, based on the example of structurally related compounds obstructing identification process, should also be described as positive. The model was able to interpret spectra of mixtures, which were previously registered into the database. Unfortunately, the program is not always able to determine which chemical substances reflect their presence in the infrared spectrum of ternary mixtures. During the research tests, it was also noted that the more complex the structure of a substance being present in the mixture was, the more difficult the interpretation of the spectra to be carry out properly by the program was. On the other hand, positive results were obtained for mixtures of compounds with not so complex structure. It must be emphasized that the results so far are promising and more attention should be paid to them in further studies.

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Section: Infrared Spectroscopymentioning

confidence: 99%

Application of Neural Networks in Diagnostics of Chemical Compounds Based on their Infrared Spectra

Macek-Kamińska

Stemplewski

2017

Ecological Chemistry and Engineering S

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“…Hence, the interest of pursuing research in spectroscopy was to implement more accurate and reproducible model estimation of soil properties. For the exploration of these spectral data, pre-processing functions were carried out to determine the most relevant spectral wavelengths for estimating soil properties [18]. The applications of statistical model associated with other processing functions have given good results in the analysis of soil properties through spectroscopy [2] [5].…”

Section: Introductionmentioning

confidence: 99%

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Loum¹,

Diack²,

Ndour³

et al. 2016

OJSS

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Spectroscopy plays a major role in the access of the analytical parameters of the soil. It tends to substitute the conventional laboratory analysis because hyperspectral data were least expensive and easier to obtain. The objective of this study was to evaluate the effect of the continuum removal (CR) in the validation of the accurate prediction model of the soil properties with Vis-NIR spectroscopy data. Few studies using Vis-NIR reflectance spectroscopy have well focused the calculation of the CR method; its effect in the calibration of the accurate models was also not well emphasized. In this study, we used the remote sensing software ENVI 4.7 to compute the CR function where the value of the continuum for each sample and for each spectral wavelength was obtained by dividing the reflectance values of the full spectrum (FS) with those of the continuum curve (CC). The partial least square regression (PLSR) model was applied in the spectral data from the soil of the Senegal Sahelian region. It was calibrated with both data from the full spectrum (FS) and those obtained after the application of the continuum removal. With the application of the CR, ultraviolet wavelengths (350 -429 nm) and those of near infrared (2491 -2500 nm) were removed from the explanatory variables of PLSR model. With the FS, all wavelengths between 350 and 2500 nm were taken into account in predicting soil properties. Our findings show a positive effect of the application of CR in the estimation of soil organic carbon. In calibration, the R2 increased up to 10% with the continuum removal in the model of 12 components (CP). In terms of validation, it's the 15-component model which is the most accurate with the same range in calibration between the FS and the CR. The lowest RMSE ranged from 0.04 with the FS to 0.03 with the application of the CR in calibration and validation. These results show that the interest of this study as soil organic carbon is recognized as a key indicator of fertility of the soil 136in Sahelian-African regions. For future studies, it's important to apply the model of neural networks to better evaluate the effect of continuum removal in predicting soil properties from the spectral data and other methods of preprocessing like the multiplicative scatter correction (msc).

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“…[7][8][9][10][11] Among these studies, special attention has been given to those employing mid-infrared (DRIFTS) [12][13][14] and near-infrared (NIRRS) reflectance. [15][16] This is a non-invasive method of analysis that allows for the quantification of the main components in the mix of generated products in one measurement, with a high degree of reliability.…”

Section: Introductionmentioning

confidence: 99%

Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data

Marder¹,

Tomedi²,

Ferrão³

et al. 2006

J. Braz. Chem. Soc.

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Neste trabalho são apresentados modelos de regressão multivariada, empregando espectros por reflexão difusa no infravermelho com transformada de Fourier (DRIFTS), para análise quantitativa do sistema ternário formado pelos carbonatos de cálcio e magnésio e pelo óxido de magnésio. Através de um diagrama ternário, foram definidas as misturas para aquisição dos espectros por reflexão no infravermelho médio. Empregando-se a regressão por mínimos quadrados parciais (PLS), foram construídos modelos com os dados espectrais centrados na média e/ou escalados pela variância, utilizando o conjunto de espectros de calibração. Para selecionar os melhores modelos, foram comparados os valores de RMSEP (root mean square error of prediction). Os resultados comprovam que bons modelos de calibração multivariada para a determinação de carbonatos de cálcio e magnésio e de óxido de cálcio podem ser obtidos a partir dos espectros por reflexão no infravermelho. Estas determinações são particularmente úteis no estudo da decomposição térmica de rochas dolomíticas.In the present work multivariate regression models were developed for the quantitative analysis of ternary systems using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) to determine the concentration in weight of calcium carbonate, magnesium carbonate and magnesium oxide. Nineteen spectra of standard samples previously defined in ternary diagram by mixture design were prepared and mid-infrared diffuse reflectance spectra were recorded. The partial least squares (PLS) regression method was applied to the model. The spectra set was preprocessed by either mean-centered and variance-scaled (model 2) or mean-centered only (model 1). The results based on the prediction performance of the external validation set expressed by RMSEP (root mean square error of prediction) demonstrated that it is possible to develop good models to simultaneously determine calcium carbonate, magnesium carbonate and magnesium oxide content in powdered samples that can be used in the study of the thermal decomposition of dolomite rocks.Keywords: dolomite, DRIFTS, partial least squares, multivariate regression, ternary mixture design IntroductionThe reserves of carbonate rocks in the state of Rio Grande do Sul -Brazil, are mainly constituted by dolomitelike rocks (MgO content ≅ 18%), while calcite-like rocks (MgO content < 4%) occur in a smaller proportion. It is assumed that, in volume, only 18% of the calcareous rocks are calcite-like (used mainly in cement production) and 82% are dolomite-like and are used mainly in the correction of soil acidity and in the production of lime.The search for a solution to the suppressed demand of raw materials by turning dolomite into calcium-rich carbonate and magnesium oxide, which are thoroughly used in industrial processes impelled the development of a physical process of dissociation of dolomite-like rocks, in order to obtain these two different products. The basic idea of the process is a fractionated calcination of a dolomite-like rock, composed m...

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Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement

Cited by 205 publications

References 0 publications

Application of Neural Networks in Diagnostics of Chemical Compounds Based on their Infrared Spectra

Application of Neural Networks in Diagnostics of Chemical Compounds Based on their Infrared Spectra

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data

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