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| Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers |
| FAN Ping-ping，LI Xue-ying，QIU Hui-min，HOU Guang-li，LIU Yan* |
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China
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Abstract Spectrometers are the core tool in spectral analysis, but it is still unclear how spectrometers influence the results of spectral analysis. Here, we studied the spectral analysis of organic carbon in sediments of the Yellow Sea and Bohai Sea using Agilent Cary 5000, ASD FieldSpec 4, and Ocean Optics QEPro and compared differences in the reflectance spectra of organic carbon and their spectral analysis. Cary 5000 is an indoor spectrometer, and FieldSpec 4 and QEPro are portable spectrometers. QEPro could only collect the reflectance between 200 and 1 000 nm, and the reflectance is the highest among the three spectrometers. Cary 5000 and FieldSpec 4 could collect the reflectance of the complete visible and near-infrared waveband (350~2 500 nm), and both spectral curves were almost identical, especially in the near-infrared bands. However, the reflectance collected by Cary 5000 is higher than that by FieldSpec 4. The abilities of spectral analysis of organic carbon concentrations in the Yellow and Bohai Sea were also different across the three spectrometers. Cary 5000 had the strongest ability to perform spectral analysis. The spectral models in Cary 5000 had a strong prediction ability of organic carbon concentrations in sediments. In Cary 5000, the r2 of the calibration set was as high as 0.99, and the r2 of the validation set was as high as 0.86; the root mean square error (RMSE) of the calibration set and validation set was 0.04 and 0.11, respectively; the relative prediction deviation (RPD) was as high as 2.6, showing a strong ability to predict sediment organic carbon. In FieldSpec 4, r2 of the calibration set was as high as 0.98, but r2 of the validation set was only 0.56; RMSE decreased from 0.06 to 0.19, and RPD was as low as 1.4, showing a low prediction ability of sediment organic carbon. In QEPro, r2 of the calibration set and validation set were both low (0.75 and 0.59, respectively), RMSE were stable and as high as 0.18, and RPD was larger than 1.5 (1.6), showing a convincing prediction ability of sediment organic carbon. Results showed that the portable spectrometers were worse than indoor instruments in spectral analysis due to their lower technological performance. For the portable spectrometers, results in spectral analysis were not different between FieldSpec 4 and QEPro, and even the results of QEPro were more stable. Therefore, we think QEPro can be replace with FieldSpec 4 in rapidly determining sediment organic carbon by spectroscopy because QEPro is cost-effective. In this study, the differences among three types of spectrometers in the spectral analysis of the same sample were compared, which provided an effective reference for the spectral analysis and model transfer of different studies.
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Received: 2022-06-02
Accepted: 2022-10-25
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Corresponding Authors:
LIU Yan
E-mail: sdqdliuyan@126.com
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[1] Rossel R A V, Webster R. European Journal of Soil Science, 2012, 63(6): 848.
[2] Liu S, Shen H, Chen S, et al. Geoderma, 2019, 348(1): 37.
[3] LI Xue-ying, LI Zong-min, HOU Guang-li, et al(李雪莹，李宗民，侯广利，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(9): 2898.
[4] Qiu H, Fan P, Hou G, et al. Bulletin of Environmental Contamination and Toxicology, 2022, 108: 1124.
[5] Yahaya O K M, MatJafri M Z, Aziz A A, et al. Journal of Instrumentation, 2015, 10(5): T05002.
[6] Linderholm J, Geladi P, Gorretta N, et al. Geoarchaeology, 2019, 34(3): 311.
[7] Li X, Fan P, Liu Y, et al. Journal of Applied Spectroscopy, 2019, 86(4): 765.
[8] Rossel R A V, Taylor H J, McBratney A B. European Journal of Soil Science, 2007, 58(1): 343.
[9] Eskildsen C E, Hansen P W, Skov T, et al. Journal of Near Infrared Spectroscopy, 2016, 24(2): 151.
[10] Ridder T D, Ver Steeg B J, Laaksonen B D, et al. Applied Spectroscopy, 2014, 68(8): 864.
[11] Folch-Fortuny A, Vitale R, de Noord OE, et al. Journal of Chemometrics, 2017, 31(3): e2874.
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