Quantitative Analysis of P in Compound Fertilizer by Laser-Induced Breakdown Spectroscopy Coupled with Linear Multivariate Calibration
SHA Wen1, 3, LI Jiang-tao1, LU Cui-ping2*, ZHEN Chun-hou3
1. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
2. Laboratory of Advanced Sensing and Intelligent Systems, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
3. School of Computer Science and Technology, Anhui University, Hefei 230601, China
Abstract：The rapid and in-situ detection of compound fertilizer components was of great significance to the production process control and product quality control of chemical fertilizer production enterprises. In the production of fertilizer companies, compound fertilizer samples were collected on the production line and sent to the laboratory for analysis. The time required for detecting was long, which can’t meet the compound fertilizer company’s production line for testing. Compared with the existing detection methods of compound fertilizer components, the detection time of laser-induced breakdown spectroscopy (LIBS) was several minutes. And the measurement of compound fertilizer components can be completed in one measurement, with almost no need for compound fertilizer samples. LIBS technique was very suitable for the rapid and on situ detection of compound fertilizer components. In the LIBS detection system, the laser beam output from the solid-state pulse laser (100 mJ, 1 064 nm, 1 Hz) was converted from horizontal to vertical by a 45°-mirror. The laser beam was focused onto the target using a lens (focal length=40 mm). The sample was placed at a rotating platform. The light emitted by the plasma was collected by an optical fiber spectrometer (Avantes, 195～500 nm) with a delay time of 1.28 μs and an integration time of 1.05 ms. The spectrometer was triggered by the signal of laser’s Q-switched. The LIBS spectrum of the compound fertilizer sample was finally obtained. In the experiment, 20 compound fertilizer samples were provided by Anhui Huilong Group. The reference concentration of phosphorus element was measured by the enterprise using the national standard method. The samples were ground into powder and sieved. Three grams of every sample was pressed pellets under a pressure of 8 MPa. During the experiment, a small fan was used to continuously purge the surface of sample to form a stable airflow environment. Each sample was repeatedly measured 10 times, and each spectrum was formed by 20 shot average to reduce the heterogeneity of the sample. 15 samples were selected as calibration set to train regression model, and 5 samples were chosen to test the model. The compound fertilizer was a complex mixture of components, in which the nitrogen, phosphorus, and potassium were all present as compounds. The traditional quantitative analysis method of LIBS was based on the intensity of a single characteristic line of the measured element. The influence of other elements was not considered, which greatly reduced the accuracy of the analysis results. In this paper, LIBS technology and multiple linear regression calibration were used in combination to determine the phosphorus concentration in compound fertilizer. Three feature spectral lines of P element detected by LIBS were 213.6, 214.9 and 215.4 nm. As the concentration of silicon in the phosphate rock was relatively stable. And near the spectral feature line of P element, there were many characteristic lines of Si element, such as 212.4, 220.8, 221.1 and 221.7 nm. The analysis was carried out based on unary, binary, ternary, and quaternary linear regression calibration curves, respectively. It turned out that the unary linear regression method hardly served the quantitative analysis for compound fertilizer sample only using the intensity of P Ⅰ 214.9 nm as variable, and the correlation coefficient between the LIBS prediction value and the reference concentration was only 0.083. When using the intensity of P Ⅰ: 214.9 nm and the sum of three characteristic lines (P Ⅰ: 213.6, 214.9 and 215.4 nm) as input variables to establish the binary linear regression, the correlation coefficient increased to 0.856 and the average absolute error decreased from 1.32% to 0.16%. When introducing the line intensity of Si Ⅰ: 212.4 nm into the binary linear regression equation, the ternary linear regression was established, and the correlation coefficient was only increased to 0.869. In order to further improve the accuracy of phosphorus concentration measurement in compound fertilizers, the quaternary linear regression equation was established. The sum of the Si Ⅰ: Si 2.4: 222.4, 220.8, 221.1 and 221.7 nm line intensities was used as an independent variable to add into ternary linear regression equation. The correlation coefficient was increased to 0.980. The relative error ranges were 0.06%～1.31% and 0.13%～1.26% for 15 calibration samples 5 validation samples, respectively. The results demonstrated that using the quaternary liner regression calibration method can improve the accuracy of phosphorus concentration measurement in compound fertilizers.
Key words：Compound fertilizer; Phosphorus; Laser-induced breakdown spectroscopy; Multivariate linear calibration
沙 文，李江涛，鲁翠萍，郑春厚. 复混肥中磷元素的激光诱导击穿光谱多元线性定量分析[J]. 光谱学与光谱分析, 2019, 39(06): 1958-1964.
SHA Wen, LI Jiang-tao, LU Cui-ping, ZHEN Chun-hou. Quantitative Analysis of P in Compound Fertilizer by Laser-Induced Breakdown Spectroscopy Coupled with Linear Multivariate Calibration. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1958-1964.
[1] YIN Ping, MENG Zhao-fang, CHEN Qiu-sheng(殷 萍，孟兆芳，陈秋生). Tianjin Agricultural Sciences(天津农业科学), 2012, 18(6): 30.
[2] ZHANG Zong-cai, WANG Hong-fu, WANG Jia-wei, et al(张宗彩，王洪富，王家伟，等). Chemical Fertilizer Industry(化肥工业), 2017, 44(4): 15.
[3] XIAO Zhan-mei, LIANG Hong-jiang(肖占梅，梁红江). Chemical Fertilizer Industry(化肥工业)，2015, 42(3): 12.
[4] Gabriel Gustinelli Arantes de Carvalho, Javier Moros, Dario Santos Jr, et al. Analytica. Chimica. Acta, 2015, 876(5): 26.
[5] Banaee M, Tavassoli S H. Polym. Test, 2012, 31: 759.
[6] Xiao Fang, Ahmad S R. Appl. Phys. B, 2014, 115(4): 497.
[7] Ponce L V, Flores T, Sosa-Saldana M, et al. Appl. Opt., 2016, 55(2): 254.
[8] Farooq W A, Al_Mutairi F N, Khater A E M, et al. Optics and Spectroscopy, 2012, 112: 874.
[9] Daniel Fernandes Andrade, Marco Aurelio Sperana, Edenir Rodrigues Pereira Filho. Anal. Methods, 2017, 9: 5156.
[10] Bruno S Marangoni, Kleydson S G Silva, Gustavo Nicolodelli, et al. Anal. Methods, 2016, 8: 78.
[11] Yao Shunchun, Lu Jidong, Li Junyan, et al. J. Anal. At. Spectrom., 2010, 25: 1733.
[12] Daniel F Andrade, Edenir Rodrigues Pereira-Filho. J. Agric. Food Chem., 2016, 64: 7890.
[13] Senesi G S, Romano R A, Marangoni B S, et al. Journal of Applied Spectroscopy, 2017, 84(5): 923.
[14] Chen Tianbing, Huang Lin, Yao Mingyin, et al. Appl. Opt., 2015, 54(25): 7807.
[15] NIST Atomic Spectra Database Lines Form, at http://physics.nist.gov//phyRefData.
