|
|
|
|
|
|
| Quantitative Determination of Water-Soluble P in Biochar Based on NELIBS Technology and EN-SVR Model |
| GUO Mei1, 2, ZHANG Ruo-yu2, 3, ZHU Rong-guang2, 3, DUAN Hong-wei1, 2* |
1. School of Agricultral Engineering, Jiangsu Uniersity, Zhenjiang 212013, China
2. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
3. Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs，Shihezi 832003, China |
|
|
|
|
Abstract Biochar can provide many available phosphorus (P) that can be absorbed and utilized by plants. In this paper, laser-induced breakdown spectroscopy (LIBS) was used to detect water-soluble P in straw based biochar quantitatively. To reduce the serious “coffee ring effect” on the substrate’s surface after droplet drying, hydrophobic polyethylene plate was selected as the liquid-solid conversion substrate. To solve the problem of the low sensitivity of LIBS signal of water-soluble P element in biochar, the signal enhancement performance of three kinds of Au nanoparticles (AuNPs) on four analytical lines of P element was studied and discussed. The results show that Au nanoparticles with large particle size (73 and 105 nm) are more prone to the aggregation effect, and the spectral signal-to-noise ratio is large. Furthermore, the P element’s univariate calibration curve models enhanced by three kinds of particle size Au nanoparticles were compared and analyzed. The results show that the univariate calibration curve models with 45 nm Au nanoparticles signal enhancement are the best. Finally, the four enhanced spectral broadening bands enhanced by the Au nanoparticles were used to develop the ElasticNet-support vector regression (EN-SVR) model. The average error of the prediction set (ARP) and the relative standard deviation of the prediction set (RSDP) of the optimal model were 5.40% and 11.09%, respectively. The results show that nanoparticle enhanced laser induced breakdown spectroscopy (NELIBS) combined with the EN-SVR model can be used for the accurate quantitative determination of water-soluble P in biochar.
|
|
Received: 2020-06-30
Accepted: 2020-11-05
|
|
|
|
Corresponding Authors:
DUAN Hong-wei
E-mail: dhwsg123@sina.com
|
|
[1] ZHANG Zhong-feng, ZHOU Long-wu, XU Guang-ping, et al（张中峰，周龙武，徐广平，等）. Guangxi Sciences（广西科学）, 2018, 25(5): 149.
[2] Carranza J E, Fisher B T, Yoder G D, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2001, 56(6): 851.
[3] Zhu D, Wu L, Wang B, et al. Applied Optics, 2011, 50(29): 5695.
[4] Lin Q, Wei Z, Xu M, et al. RSC Advances, 2014, 4(28): 14392.
[5] Zhu D, Chen J, Lu J, et al. Analytical Methods, 2012, 4(3): 819.
[6] Liu Y, Baudelet M, Richardson M, et al. Journal of Analytical Atomic Spectrometry, 2010, 25(8): 1316.
[7] Li C, Hao Z, Zou Z, et al. Optics Express, 2016, 24(8): 7850.
[8] De Giacomo A, Dellaglio M, Gaudiuso R, et al. Journal of Analytical Atomic Spectrometry, 2016, 31(8): 1566.
[9] Wen X, Lin Q, Niu G, et al. Applied Optics, 2016, 55(24): 6706.
[10] Ping Y, Ran Z, Wen Z, et al. Food Chemistry, 2019, 272: 323.
[11] Xue J, Yang Z, Han L, et al. Applied Energy, 2015, 137: 18.
[12] Vieira A L, Silva T V, De Sousa F S, et al. Microchemical Journal, 2018, 139: 322.
[13] Marangoni B S, Silva K S, Nicolodelli G, et al. Analytical Methods, 2016, 8(1): 78. |
| [1] |
WEI Si-ye1, 2, FAN Xing-cheng3, MAO Han1, 2, CAO Tao4, 5, CHENG Ao3, FAN Xing-jun3*, XIE Yue3. Abundance and Spectral Characteristics of Molecular Weight Separated Dissolved Organic Matter Released From Biochar at Different Pyrolysis Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1809-1815. |
| [2] |
ZHANG Xing-long1, LIU Yu-zhu1, 2*, SUN Zhong-mou1, ZHANG Qi-hang1, CHEN Yu1, MAYALIYA·Abulimiti3*. Online Monitoring of Pesticides Based on Laser Induced Breakdown
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1711-1715. |
| [3] |
YANG Lin-yu1, 2, 3, DING Yu1, 2, 3*, ZHAN Ye4, ZHU Shao-nong1, 2, 3, CHEN Yu-juan1, 2, 3, DENG Fan1, 2, 3, ZHAO Xing-qiang1, 2, 3. Quantitative Analysis of Mn and Ni Elements in Steel Based on LIBS and GA-PLS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1804-1808. |
| [4] |
HE Ya-xiong1, 2, ZHOU Wen-qi1, 2, ZHUANG Bin1, 2, ZHANG Yong-sheng1, 2, KE Chuan3, XU Tao1, 2*, ZHAO Yong1, 2, 3. Study on Time-Resolved Characteristics of Laser-Induced Argon Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1049-1057. |
| [5] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
| [6] |
GONG Zheng1, LIN Jing-jun2*, LIN Xiao-mei3*, HUANG Yu-tao1. Effect of Heating and Cooling on the Characteristic Lines of Al During Melting[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 598-602. |
| [7] |
QIN Shuang1, LI Ming-liang1, DAI Yu-jia1, GAO Xun1*, SONG Chao2*, LIN Jing-quan1. The Accuracy Improvement of Fe Element in Aluminum Alloy by Millisecond Laser Induced Breakdown Spectroscopy Under Spatial Confinement Combined With Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 582-586. |
| [8] |
WANG Ya-wen1,2,3, ZHANG Yong4, CHEN Xiong-fei1,2,3, LIU Ying1,2,3, ZHAO Zhen-yang4, YE Ming-guo5, XU Yu-xing6, LIU Peng-yu1,2,3*. Quantitative Analysis of Nickel-Based Superalloys Based on a Remote LIBS System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 603-608. |
| [9] |
XU Yu-ting1, SUN Hao-ran2, GAO Xun1*, GUO Kai-min3*, LIN Jing-quan1. Identification of Pork Parts Based on LIBS Technology Combined With PCA-SVM Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3572-3576. |
| [10] |
DENG Fan1, HU Zhen-lin2, CUI Hao-hao2, ZHANG Deng2, TANG Yun4, ZHAO Zhi-fang2, ZENG Qing-dong2, 3*, GUO Lian-bo2*. Progress in the Correction of Self-Absorption Effect on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 2989-2998. |
| [11] |
YOU Wen1, XIA Yang-peng1, HUANG Yu-tao1, LIN Jing-jun2*, LIN Xiao-mei3*. Research on Selection Method of LIBS Feature Variables Based on CART Regression Tree[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3240-3244. |
| [12] |
YANG Wen-feng1*, QIAN Zi-ran1, CAO Yu2, WEI Gui-ming1, ZHU De-hua2, WANG Feng3, FU Chan-yuan1. Research on the Controllability of Aircraft Skin Laser Paint Remove Based on Laser-Induced Breakdown Spectrum and Composition Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3233-3239. |
| [13] |
HE Ya-xiong1, ZHOU Wen-qi1, KE Chuan2, XU Tao1*, ZHAO Yong1, 2. Review of Laser-Induced Breakdown Spectroscopy in Gas Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2681-2687. |
| [14] |
CHEN Geng-yin1, ZHANG Qi-hang1, LIU Yu-zhu1, 2*, ZHANGCHENG Yuan-zhe1, CHEN Yu1, CHEN Guo-qing1, HAN Bo-yuan1, ABULIMITI Bumaliya3*. Online Detection of VOCs Based on LIBS and Raman Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2729-2733. |
| [15] |
LIAO Wen-long1, LIU Kun-ping2, HU Jian-ping1, GAN Ya1, LIN Qing-yu3, DUAN Yi-xiang3*. Research Advances and Trends of Rapid Detection Technologies for Pathogenic Bacteria Based on Fingerprint Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2372-2377. |
|
|
|
|
