Quantitative analysis of phosphorus in compound fertilizer by laser induced breakdown spectroscopy

LI Jiangtao; LU Cuiping; SHA Wen

doi:10.7510/jgjs.issn.1001-3806.2019.05.003

LASER TECHNOLOGY > 2019 > 43(5): 601-607. > DOI: 10.7510/jgjs.issn.1001-3806.2019.05.003

LI Jiangtao, LU Cuiping, SHA Wen. Quantitative analysis of phosphorus in compound fertilizer by laser induced breakdown spectroscopy[J]. LASER TECHNOLOGY, 2019, 43(5): 601-607. DOI: 10.7510/jgjs.issn.1001-3806.2019.05.003

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Quantitative analysis of phosphorus in compound fertilizer by laser induced breakdown spectroscopy

1.
School of Electrical Engineering and Automation, Anhui University, Hefei 230061, China
2.
Laboratory of Advanced Sensing and Intelligent Systems, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China

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Received Date: December 27, 2018
Revised Date: March 06, 2019
Published Date: September 24, 2019

Graphical Abstract

Abstract

Abstract

In order to detect its components rapidly in the production of compound fertilizer and guide the production, laser-induced breakdown spectroscopy (LIBS) and support vector machine (SVM) were used to quantitatively analyze phosphorus (P) in compound fertilizer. In the experiment, 58 compound fertilizer samples were analyzed by three characteristic spectra of PⅠ 213.5nm, PⅠ 214.9nm and PⅠ 215.4nm. 58 samples were divided into training set (43 samples) and test set (15 samples) by random selection method. The grid search method was used to optimize the parameters of the quantitative analysis model of P element in compound fertilizer. The SVM analysis model was constructed. The results show that, the correlation coefficient R² of the calibration model of training set is 0.981. It shows that the correlation between the reference value and the predicted value of the training set is high. The correlation coefficient R² between the reference value and the predicted value of phosphorus (P) in the samples is 0.992. The mean square error is 4.95×10^-5. SVM model has strong applicability. The average absolute error and relative error of the training set are 5.9×10^-4 and 3.99×10^-3, respectively. The average absolute error and relative error of the test set are 5.6×10^-4 and 3.28×10^-3, respectively. The combination of SVM algorithm and LIBS technology can realize the rapid detection of phosphorus in compound fertilizer. This study provides a reference for rapid determination of element content in compound fertilizer.
- spectroscopy,
- support vector machine regression,
- laser-induced breakdown spectroscopy,
- phosphorus,
- grid search

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References

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