Quantification of TOC and TN in reservoir sediments using Fourier transform infrared spectroscopy

Lin Mei, Yihong Yue, Yong Qin, Xueping Chen, Fushun Wang

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Soil Ecology Letters ›› 2022, Vol. 4 ›› Issue (4) : 399-408. DOI: 10.1007/s42832-021-0119-x
RESEARCH ARTICLE
RESEARCH ARTICLE

Quantification of TOC and TN in reservoir sediments using Fourier transform infrared spectroscopy

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Highlights

• A calibration models for the rapid determination of TOC and TN contents using FTIRS.

• Ÿ A rapid analytical method for quantitatively calculating TOC and TN.

Ÿ• A general model for TOC and TN quantitative analysis in reservoir sediments in the southwest China.

Abstract

This study aims to quantitatively assess the total organic carbon (TOC) and total nitrogen (TN) content of reservoir sediments in southwest China using Fourier transform infrared spectroscopy (FTIRS). FTIRS measurements were performed on 187 sediment samples from four reservoirs to develop calibration models that relate FTIR spectral information with conventional property concentrations using partial least squares regression (PLSR). Robust calibration models were established for TOC and TN content. The external validation of these models yielded a significant correlation between FTIR-inferred and conventionally measured concentrations of R2 = 0.88 for TOC, R2 = 0.90 for TN. This method can be performed with a small sample size and is non-destructive throughout the simple measurement process. The TOC and TN content in the sediment can be determined with high effectiveness without being overly expensive, making it an advantageous method when measuring a large number of samples.

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Keywords

Fourier transform infrared spectroscopy / Chemometrics / Total organic carbon / Total nitrogen / Reservoir sediment

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Lin Mei, Yihong Yue, Yong Qin, Xueping Chen, Fushun Wang. Quantification of TOC and TN in reservoir sediments using Fourier transform infrared spectroscopy. Soil Ecology Letters, 2022, 4(4): 399‒408 https://doi.org/10.1007/s42832-021-0119-x

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Acknowledgments

This work was supported by the National Key Research and Development Program of China (2016YFA0601003) and Shanghai Science and Technology Development Foundation (19010500100).

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2021 Higher Education Press
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