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Citation: ZHOU Hua-Mao, CHEN Tian-Bing, LIU Mu-Hua, XU Jiang, HE Xiu-Wen, XU Fang-Hao, YAO Ming-Yin. Quantitative Analysis of Chromium in Rice Husks by Laser Induced Breakdown Spectroscopy Based on Particle Swarm Optimization-Support Vector Machine[J]. Chinese Journal of Analytical Chemistry, ;2020, 48(6): 811-816. doi: 10.19756/j.issn.0253-3820.191731 shu

Quantitative Analysis of Chromium in Rice Husks by Laser Induced Breakdown Spectroscopy Based on Particle Swarm Optimization-Support Vector Machine

Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Agricultural University, Jiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang 330045, China

Received Date: 11 December 2019
Revised Date: 19 March 2020

Fund Project: This work was supported by the National Natural Science Foundation of China (No.31460419) and the Project of Jiangxi Education Department (No. GJJ160404).

To rapidly determine Cr content in rice husks, laser induced breakdown spectroscopy (LIBS) was used to obtain plasma signal spectral data of Cr elements of rice in 24 groups of polluted paddy fields around Poyang Lake, Jiangxi Province. By constructing nine-point smoothing and normalized spectral pretreatment in the band range of 422.04-445.94 nm, 18 groups were selected as training samples and the other 6 groups as test samples, and thus a support vector machine (SVM) parameter adjustment optimization method based on particle swarm optimization (PSO) was proposed. The LIBS quantitative analysis model of Cr content by PSO-SVM intelligent algorithm was established, and the root mean square error (RMSE) of test set was 7.83 μg/g, the average absolute error percentage (MAPE) was 4.10%, the correlation coefficient between predicted value and true value was 0.9948. Under the same conditions, partial least square method (siPLS) was used to predict the concentration of 6 groups of samples, and it was found that the RMSE was 22.58 μg/g, the MAPE was 6.17%, and the correlation coefficient was 0.9840. The analysis results showed that the PSO-SVM regression quantitative method could be applied to the composition analysis of LIBS agricultural products, and its analysis effect was improved compared with siPLS.
- Laser induced breakdown spectroscopy,
- Particle swarm optimization-support vector machine,
- Chromium,
- Rice husk
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