Soil Chemical Insights Provided through Vibrational Spectroscopy

Parikh, Sanjai J.; Goyne, Keith W.; Margenot, Andrew J.; Mukome, Fungai N.D.; Calderón, Francisco J.

doi:10.1016/b978-0-12-800132-5.00001-8

Cited by 217 publications

(186 citation statements)

References 640 publications

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“…For example, decomposition of polysaccharides such as cellulose can result in decreased absorbance of polysaccharide C-O and greater relative absorbance of the oxidation product carboxyl C = O (Pérez et al 2002). Shifts in SOM composition due to tillage and residue management can be characterized by diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy as relative changes in organic functional groups (Parikh et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Biochemical proxies indicate differences in soil C cycling induced by long-term tillage and residue management in a tropical agroecosystem

et al. 2017

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Background & aim A potential benefit of conservation agriculture (CA) is soil organic carbon (SOC) accrual, yet recent studies indicate limited or no i m p a c t o f C A o n t o t a l S O C i n tr o p i c a l agroecosystems. We evaluated biochemical indicators of soil C cycling after 9 years (18 seasons) of contrasting tillage with and without maize residue retention in western Kenya. Methods Potential activities of C-cycling enzymes (β-glucosidase, GLU; β-galactosidase, GAL; glucosaminidase, GLM; cellobiohydrolase, CEL), permanganate-oxidizable C (POXC), and soil organic matter (SOM) composition (by infrared spectroscopy) were measured.Results POXC tended to be greater under reduced tillage and residue retention, but did not significantly differ among treatments (≤ 2% of SOC). Despite no significant differences in SOC concentrations or stocks, activities of all 4 C-cycling enzymes responded strongly to tillage, and to a lesser extent to residue management. Activities of GLU, GAL, and GLM were greatest under the combination of reduced tillage and residue retention relative to other treatments. Reduced tillage produced an enrichment in carboxyl C = O (+6%) and decreased polysaccharide C-O (−3.5%) relative to conventional tillage irrespective of residue management. Conclusions Though enzyme activities and POXC are typically associated with SOC accrual, changes in soil C cycling at this site have not translated into significant differences in SOC after 9 years. Elevated enzyme activities may have offset potential SOC accumulation under CA. However, the ratio of C-cycling enzyme activities to SOC was higher under reduced tillage and residue retention relative to other treatments, indicating that stoichiometric scaling of SOC and enzyme activities does not explain absence of significant differences in SOC among tillage and residue managements. Potential factors that may explain the low SOC accrual rates in this tropical agroecosystem included the low, albeit realistic, levels of residue retention, nutrient limitations, and high temperatures favoring decomposition.

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Section: Introductionmentioning

confidence: 99%

Biochemical proxies indicate differences in soil C cycling induced by long-term tillage and residue management in a tropical agroecosystem

et al. 2017

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“…The major advantages of these techniques are their accuracy, rapidity, and reduced costs. [9] The spectral investigation of phosphorus [10] and nitrogen compounds absorption [11][12][13][14] in Spinacia oleracea is almost absent from the literature. Thus, the present study was carried out to evaluate the effect of phosphogypsum used in combination with soil, on phosphorus and nitrogen compounds absorption by Spinacia oleracea.…”

Section: Introductionmentioning

confidence: 98%

Spectral Investigation of Phosphorus Compounds inSpinacia OleraceaUsing Fourier Transform Infrared Method

Caraman

Zirnea

Răducanu

et al. 2015

Spectroscopy Letters

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The study uses the Fourier transform infrared spectroscopy method to evaluate the influence of phosphogypsum from the soil on the growth and accumulation of nutrients in Spinacia oleracea. Attenuated total reflectance spectra of Spinacia oleracea roots and stems originated from plants cultivated on 28% to 52% phosphogypsum enriched soil have been studied in the 4000-600 cm À1 area. The structure of the infrared absorption bands for spinach roots and stems and for the soil where plants were grown was analyzed. Thus, it was observed that phosphorus-oxygen bonds contribution can be identified both in roots and stems absorption spectra. The significance of the research is the possibility to use the infrared investigation as rapid technique, particularly useful for agriculture, to study the extraction and assimilation process of the compounds containing phosphorus and nitrogen from the soil in which the spinach seedlings grew. In the case of this study, it was observed that the absorption of phosphorus in the spinach roots and stems has not the same effect. The analysis of obtained data indicated that the assimilation of phosphorus from phosphogypsum enriched soil induces changes in spinach roots structure that are most likely due to an increase synthesis of proteins, rather than to a decrease in carbohydrates quantity.

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“…Vibrational spectroscopy can be a versatile and highly informative analytical tool for soil scientists. The wide range of techniques to be applied and information obtained makes vibrational spectrometers very useful tools for investigating minerals …”

Section: Introductionmentioning

confidence: 99%

“…The wide range of techniques to be applied and information obtained makes vibrational spectrometers very useful tools for investigating minerals. 5 Since the mineral composition of rock samples is routinely determined by an FT-IR spectroscopy method, 6 which takes about 20 minutes to acquire the IR spectrum of a sample, including the sample preparation, 7 and we can assume a certain level of correlation between the mineral composition and solubilities of rock samples, 4 we have embarked on an IR spectra library based solubility prediction method development. The FT-IR method provides a good basis for our intention to considerably improve the response time of the laboratory in those situations, in which the laboratory has to deliver in a shorter timeframe than possible reliable data for field scale acid jobs.…”

Section: Introductionmentioning

confidence: 99%

Machine learning methods to predict solubilities of rock samples

Hanzelik

Gergely

Gáspár

et al. 2019

Journal of Chemometrics

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Interests in the use of chemometric and data science methods for laboratory techniques have grown rapidly over the last 10 years, for the reason that they are cheaper and faster than traditional analytical methods of material testing.This study uses 888 rock samples collected from the exploration and production (E&P) sector of the oil industry. Based on the Fourier-transform infrared (FT-IR) spectra of these rock samples their solubility predictions have been developed and investigated with nine methods including both linear and non-linear ones. Two of these methods such as Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) are available in a commercial software package and the other seven methods, Extreme Gradient Boosting (XGBoost), Ridge Regression (RR), k-nearest neighbours (k-NN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Artificial Neural Network (ANN) with TensorFlow (TF), were coded by the authors based either on commercial applications or open source libraries. The investigation starts with spectral data pre-processing carried out by standard normal variate (SNV), baseline correction and feature selection methods creating the feature set for all machine learning (ML) applications.The accuracy of predictions has been evaluated with mean squared error as a performance metric for each investigated method. The comparisons of predicted values to real data of test samples have shown that mineral solubility in acids can be well predicted in the range of the uncertainties of real laboratory measurements, therefore it can be used to improve the response time of these investigations and reduce the risk in industrial applications. In those cases, where the unknown samples have got some out of the range features, the limitations in the accuracy of predictions have become clear. We have also identified the limitations in the methodology and planned steps to further improve the prediction capabilities. The identified constraint of samples' multitude further emphasizes the need for database building efforts, so that the real potential in big data and machine learning can be realized. KEYWORDSacid solubility, artificial neural network (ANN) TensorFlow, Extreme Gradient Boosting (XGBoost), mid-infrared spectra, multivariate data analysis (MVDA)

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Soil Chemical Insights Provided through Vibrational Spectroscopy

Cited by 217 publications

References 640 publications

Biochemical proxies indicate differences in soil C cycling induced by long-term tillage and residue management in a tropical agroecosystem

Biochemical proxies indicate differences in soil C cycling induced by long-term tillage and residue management in a tropical agroecosystem

Spectral Investigation of Phosphorus Compounds inSpinacia OleraceaUsing Fourier Transform Infrared Method

Machine learning methods to predict solubilities of rock samples

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