Citation: HE Xiu-Wen, CHEN Tian-Bing, YAO Ming-Yin, ZHOU Hua-Mao, HU Hui-Qin, WANG Cai-Hong, LIU Mu-Hua. Quantitative Analysis of Chrome in Soil Based on Multiple Quadratic Non-linear Regression Model by Laser-Induced Breakdown Spectroscopy[J]. Chinese Journal of Analytical Chemistry, ;2016, 44(1): 68-73. doi: 10.11895/j.issn.0253-3820.150501 shu

Quantitative Analysis of Chrome in Soil Based on Multiple Quadratic Non-linear Regression Model by Laser-Induced Breakdown Spectroscopy

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Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Province, College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China

Corresponding author: LIU Mu-Hua,
Received Date: 19 June 2015
Available Online: 11 August 2015
Fund Project: 本文系国家自然科学基金(31460419) (31460419)江西省科技计划项目(20143ACB21013) (20143ACB21013)江西省教育厅科技计划(CJJ12249) (CJJ12249)江西省学术带头人计划(09004004)资助 (09004004)

The chrome concentration in soil was quantitatively analyzed by laser induced breakdown spectroscopy (LIBS). Due to the complexity of soil components, there is serious overlap interference spectral line, which leads to the unsatisfactory results for the monadic regression analysis. To obtain the more effective spectrum information, by using the spectral intensity of Cr Ⅰ 425.43 nm and Fe Ⅰ 425.07 nm as independent variables and Cr concentration as dependent variable, the methods of cross dimension reduction approximate multivariate nonlinear regression, multiple quadratic non-linear regression and square dimension reduction approximate multivariate nonlinear regression were established. The results showed that when the cross term for characteristic spectral intensity of Cr Ⅰ 425.43 nm and Fe Ⅰ 425.07 nm was used, the multiple quadratic nonlinear regression model was better than others. The correlation coefficients between predicted concentration and reference concentration reached 0.9943, and the relative error of four predicted sample was 3.57%, 0.76%, 7.66% and 2.24% respectively.
- Laser induced breakdown spectroscopy,
- Chrome,
- Multiple quadratic nonlinear regression,
- Quantitative analysis
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沈阳化工大学材料科学与工程学院沈阳 110142