Citation: LI Mao-Gang, LIANG Jing, YAN Chun-Hua, TANG Hong-Sheng, ZHANG Tian-Long, LI Hua. Rapid Quantitative Analysis of Heavy Metals in Soil by Laser Induced Breakdown Spectroscopy Combined with Random Forest Algorithm[J]. Chinese Journal of Analytical Chemistry, ;2021, 49(8): 1410-1418. doi: 10.19756/j.issn.0253-3820.211067 shu

Rapid Quantitative Analysis of Heavy Metals in Soil by Laser Induced Breakdown Spectroscopy Combined with Random Forest Algorithm

1.
Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China;
2.
College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, China

Corresponding author: ZHANG Tian-Long, LI Hua,
Received Date: 25 January 2021
Revised Date: 9 April 2021

Fund Project: Supported by the National Natural Science Foundation of China (Nos.22073074, 21873076, 21675123, 21605123).

A rapid quantitative analysis method for heavy metals in soil based on laser-induced breakdown spectroscopy (LIBS) and random forest (RF) algorithm was developed. LIBS spectra of 22 soil samples were collected by LIBS spectrometer. The effects of different spectral pretreatment methods on the prediction performance of RF model based on LIBS spectra of soil samples were explored. With normalized LIBS spectral data as initial input variables, the RF calibration models based on full spectrum, characteristic band, variable importance and backward interval were constructed for the quantitative analysis of Cu, Cr, Pb and Ni in soil. The results showed that, compared with the RF calibration models based on full spectrum, characteristic band and variable importance, the RF calibration model based on backward interval (BiRF) had a better performance in quantitative analysis of Cu, Cr, Pb and Ni in soil. The optimal root mean square error (RMSE) values of Cu, Cr, Pb and Ni were 8.0221, 6.0120, 1.7382 and 1.2851 μg/g, respectively, and the optimal coefficient of determination (R²) values were 0.9610, 0.8985, 0.7021 and 0.9850, respectively. The results showed that the LIBS technology combined with BiRF algorithm was a feasible method for detection of heavy metals in soil.
- Soil,
- Heavy metals,
- Laser-induced breakdown spectroscopy,
- Random forest,
- Feature selection
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沈阳化工大学材料科学与工程学院沈阳 110142