Quantitative Analysis of Soil by Laser-induced Breakdown Spectroscopy Using Genetic Algorithm-Partial Least Squares
10.11895/j.issn.0253-3820.140668
- VernacularTitle:基于遗传算法和偏最小二乘法的土壤激光诱导击穿光谱定量分析研究
- Author:
Xiaoheng ZOU
;
Zhongqi HAO
;
Rongxing YI
;
Lianbo GUO
;
Meng SHEN
;
Xiangyou LI
;
Zemin WANG
;
Xiaoyan ZENG
;
Yongfeng LU
- Publication Type:Journal Article
- Keywords:
Laser-induced breakdown spectroscopy;
Genetic algorithm;
Partial least squares;
Soil compositions analysis
- From:
Chinese Journal of Analytical Chemistry
2015;(2):181-186
- CountryChina
- Language:Chinese
-
Abstract:
Laser-induced breakdown spectroscopy ( LIBS) was used to detect the compositions of soil in the air, and the quantitative analysis model with genetic algorithm-partial least squares ( GA-PLS ) was established. A total of fifty-eight soil samples were split into calibration, monitoring and prediction sets. Eleven soil compositions including Mn, Cr, Cu, Pb, Ba, Al2 O3 , CaO, Fe2 O3 , MgO, Na2 O, and K2 O were quantitatively analyzed. The results demonstrated that, as a pretreatment method for optimizing the selection of spectral lines, GA could be effectively used to reduce the number of spectral lines for use in building PLS model, and hence simplify the quantitative analysis model. More importantly, for most of the soil compositions, GA-PLS could significantly improve the prediction ability compared with the conventional PLS model. Take Mn as an example, the root-mean-square error of prediction ( RMSEP ) was decreased from 0. 0215% to 0 . 0167%, and the mean percent prediction error ( MPE ) was decreased from 8 . 10% to 5 . 20%. The research provides an approach for further improving the accuracy of LIBS quantitative analysis in soil.
