可见-近红外光谱的潮间带沉积物有机碳含量的几种模型预测方法

doi:10.3964/j.issn.1000-0593(2020)04-1082-05

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Abstract Visible-near infrared spectroscopy has been shown to be a fast and effective tool for organic carbon (TOC) content prediction. However, the research target of using spectral prediction of TOC content is mainly soil or lake sediment, and there is little research on marine sediments in intertidal zone. In order to predict the content of TOC in intertidal sediments quickly and accurately, this study constructed TOC prediction model by combining abnormal sample elimination, spectral feature transformation and feature wavelength extraction, that is, collecting sediment spectra of samples in intertidal zone, using Markov distance, standard lever value and student residuals combined analysis method to remove abnormal samples, using multivariate scattering correction (MSC), smoothing + differential for spectral transformation, using genetic algorithm (GA) to extract characteristic wavelengths, using partial least squares method (PLS), least squares support vector machine (LSSVM) and BP Neural Network (BPNN) to model and predict sediment TOC content, using the decision coefficient (R²) and residual estimation deviation (PRD) to evaluate model accuracy. The results showed that the elimination of abnormal samples improved model accuracy, and the test R² and PRD of the BPNN model increased by 28% and 39% respectively. The effect of MSC was better than that of smoothing+differential, and the test R²and PRD of PLS, LSSVM and BPNN models based on MSC were 0.81, 0.86，0.78 and 2.25, 2.59, 2.07, respectively, which enhanced 9%~20% (R²) and 11%~22% (PRD) than that based on smoothing+differential, suggesting that MSC has a strong ability to extract TOC information. GA is not conducive to increasing model accuracy, the test R² and PRD of models based on GA reducedby 9%~36% and 18%~33%, respectively. This may be related to the low number of characteristic wavelengths extracted by GA. The BPNN model has the lowest predictive accuracy and may be related to its vulnerability to local minimums. PLS model has high accuracy and can predict TOC content in intertidal zone. Basing on abnormal sample elimination and MSC, the modeling set R² of PLS model was 0.98, and the prediction set R² and RPD were 0.81 and 2.25 respectively. The accuracy of LSSVM model was better than that of PLS, the modeling set R² was 0.99, the test set R² and RPD were 0.86 and 2.59 respectively, implying excellent TOC quantitative prediction ability of LSSVM. In a word, for the prediction of TOC content in intertidal sediments, the combination of abnormal sample elimination, MSC spectral transformation and LSSVM modeling can obtain a reliable and stable prediction model.

Key words：Intertidal sediment; Visible-near infrared spectroscopy; Predictive model; Organic carbon content

Received: 2019-03-18 Accepted: 2019-07-04

ZTFLH:

O657.3

Corresponding Authors: LIU Yan E-mail: sdqdliuyan@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)04-1082-05 OR https://www.gpxygpfx.com/EN/Y2020/V40/I04/1082

[1] Alaoui G, Léger M N, Gagné J, et al. Chemical Geology, 2011, 286: 290.
[2] ZHANG Hai-liang, LUO Wei, LIU Xue-mei, et al(章海亮, 罗微, 刘雪梅, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2017, 37(2): 584.
[3] SHEN Yan, ZHANG Xiao-ping, LIANG Ai-zhen, et al(申艳, 张晓平, 梁爱珍，等). System Sciemces and Comprehensive Studies in Agriculture(农业系统科学与综合研究), 2010, 2: 174.
[4] YIN Sheng-le, LIU Xiao-shou, YUAN Chao, et al(尹盛乐, 刘晓收, 袁超, 等). Transactions of Oceanology and Limnology(海洋湖沼通报), 2012, 2: 97.
[5] GAO Sheng, WANG Qiao-hua, LI Qing-xu, et al(高升, 王巧华, 李庆旭, 等). Chinese Journal of Analytical Chemistry (分析化学), 2019, 47(6): 941.
[6] LIU Xue-mei, ZHANG Hai-liang(刘雪梅，章海亮). Journal of Irrigation and Drainage(灌溉排水学报), 2013, 32: 138.
[7] DONG Zhe, YANG Wu-de, ZHU Hong-fen, et al(董哲，杨武德，朱洪芬，等). Journal of Shanxi Agricultural Sciences(山西农业科学), 2019, 47(5): 751.
[8] WANG Zhe, NIE Ya-guang, CHEN Qian-qian, et al(王哲, 聂亚光, 陈倩倩，等). Polar Science(极地研究), 2016, 28: 317.
[9] ZHANG Rui, LI Zhao-fu, PAN Jian-jun(张锐, 李兆富, 潘剑君). Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2017, 33: 175.
[10] CUI Xia, SONG Qing-jie, ZHANG Yao-yao, et al(崔霞, 宋清洁, 张瑶瑶，等). Acta Prataculturae Sinica(草业学报), 2017, 10: 20.
[11] SHEN Yan, ZHANG Xiao-ping, YANG Xue-ming, et al(申艳，张晓平，杨学明，等). Acta Pedologica Sinica(土壤学报), 2010, 5: 1006.
[12] YANG Hong-fei, ZHENG Li-ming, HAO Zhong-yao, et al(杨红飞, 郑黎明, 郜中要，等). Journal of Anhui Agricultural University(安徽农业大学学报), 2018, 45(1): 101.
[13] JI Wen-jun, SHI Zhou, ZHOU Qing, et al(纪文君, 史舟, 周清，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2012, 31(3): 277.
[14] QIAO Tian, LÜ Cheng-wen, XIAO Wen-ping, et al(乔天, 吕成文, 肖文凭，等). Chinese Journal of Soil Science(土壤通报), 2018, 49：773.
[15] LU Yan-nian, LIU Yan-fang, CHEN Yi-yun, et al(卢延年，刘艳芳，陈奕云，等). Chinese Agricultural Science Bulletin(中国农学通报), 2014, 30(26): 127.