|
|
|
|
|
|
Terahertz Spectrum Inversion Modeling of Lead Content in Different pH Soils |
LI Chao1, 2, LI Bin2, 3*, ZHANG Li-qiong2, YE Da-peng1*, ZHENG Shu-he1 |
1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350012, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing 100097, China |
|
|
Abstract Aiming at the quantitative determination of heavy metal lead in soils, the optimal inversion prediction model of lead content in soils at different pH was studied based on terahertz spectroscopy. Lead-containing soil samples with pH of 8.5, 7.0 and 5.5 were prepared. Terahertz time-domain spectroscopy system TERA K15 was used to collect the Terahertz spectra of the samples, and multivariate scattering correction (MSC), baseline correction and Savoitzky-Golay smoothing were used to pre-process the spectra. For the spectral data of pre-treatment, successive projection algorithm (SPA) was used to select the sensitive frequencies of spectral data. Based on the selected sensitive frequencies, partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN) was used to establish inversion prediction models of lead content in the soil. The correlation coefficient of calibration (Rc), root mean square error of calibration (RMSEC), the correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used as model evaluation parameters to evaluate the performance of the model, and to determine the best prediction model of leadship in different pH soils. The experimental results show that the modeling effect after SPA choosing sensitive frequencies is generally better than that of full spectrum. Among them, the best prediction models for the samples with pH 8. 5 were SPA-PLS, Rc, Rp, RMSEC, RMSEP and RPD were 0.997 7, 0.994 6, 14.52 mg·kg-1, 22.70 mg·kg-1 and 9.63, respectively; the best prediction models for the samples with pH 7.0 were SPA-SVM, Rc, Rp, RMSEC, RMSEP and RPD were 0.996 2, 0.975 7, 20.25 mg·kg-1, 33.04 mg·kg-1 and 4.56, respectively; and the samples with pH 5.5 were the best. The prediction models are SPA-BPNN, Rc, Rp, RMSEC, RMSEP and RPD are 0.968 7, 0.974 4, 48.83 mg·kg-1, 55.03 mg·kg-1 and 4.44, respectively. The results provide a new idea for inversion prediction of lead content in different pH soils, and also provide theoretical methods and technical support for other heavy metals inversion prediction models in different pH soils.
|
Received: 2019-07-16
Accepted: 2019-11-28
|
|
Corresponding Authors:
LI Bin, YE Da-peng
E-mail: lib@nercita.org.cn
|
|
[1] XU Xi-bo, LÜ Jian-shu, XU Ru-ru(徐夕博,吕建树,徐汝汝). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(9): 216.
[2] XIE Long-tao, PAN Jian-jun, BAI Hao-ran, et al(谢龙涛, 潘剑军, 白浩然, 等). Acta Pedologica Sinica (土壤学报), http://kns.cnki.net/kcms/detail/32.1119.P.20190103.0927.002.html.
[3] YANG Xiu-min, REN Guang-meng, LI Li-xin, et al(杨秀敏,任广萌,李立新,等). China Mining Magazine(中国矿业), 2017, 26(6): 79.
[4] Liu Y L, Chen Y Y. Soil and Sediment Contamination: An International Journal, 2012, 21(8): 951.
[5] CHEN Yuan-peng, ZHANG Shi-wen, LUO Ming, et al(陈元鹏,张世文,罗 明, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(1): 170.
[6] Sun W C, Zhang X. International Journal of Applied Earth Observation and Geoinformation,2017, 58: 126.
[7] ZHANG Qiu-xia, ZHANG He-bing, LIU Wen-kai, et al(张秋霞, 张合兵, 刘文锴,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(12): 230.
[8] Sun W C, Zhang X, Sun X J, et al. Geoderma,2018, 327: 25.
[9] Shi T Z, Wang J J, Chen Y Y, et al. International Journal of Applied Earth Observation and Geoinformation,2016, 52: 95.
[10] LI Bin, ZHAO Chun-jiang(李 斌,赵春江). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(S1): 291.
[11] GB 15618—2018 National Standards of the People’s Republic of China(中华人民共和国国家标准). Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land(土壤环境质量农用地土壤污染风险管控标准),2018.
[12] FENG Hai-kuan, LI Zhen-hai, JIN Xiu-liang, et al(冯海宽, 李振海, 金秀良,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(12): 165.
[13] Liu W, Liu C H, Yu J J, et al. Food Chemistry,2018, 251:86.
[14] Li M L, Dai G B, Chang T Y, et al. Applied Sciences,2017, 7(2): 172. |
[1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[2] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[3] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[4] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[5] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[6] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[7] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[8] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[9] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
[10] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[11] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[12] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[13] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[14] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[15] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
|
|
|
|