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| Detection of Dimethoate Content with Laser Induced Breakdown Spectroscopy Combined with LSSVM and Internal Standard Method |
| SUN Tong, LIU Jin, GAN Lan-ping, WU Yi-qing, LIU Mu-hua* |
| Key Laboratory of Jiangxi University for Optics-Electronics Application of Biomaterials, College of Engineering, Jiangxi Agricultural University; Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang 330045，China |
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Abstract In this research, collineardouble pulselaser induced breakdown spectroscopy (LIBS) was used to detect dimethoate content in solutionquantificationally. Fortune paulownia wood chip with cylinder shape was used to enrichmentdimethoate, and the spectra of samples were acquired with a two-channel high precision spectrometer in the wavelength range of 206.28~481.77 nm. Four spectral linesof phosphorus (213.618, 214.91, 253.56, 255.325 nm) were selected as analytical lines, and carbonspectral line (247.856 nm) was used as internal standard line. Then, univariatelinear fitting and least squares support vector machine (LSSVM) were used to develop univariate calibration model, LSSVM calibration model and LSSVM calibration model based on internal standard method, and the performance of threecalibration models were compared. The results indicate that collinear double pulse LIBS combined with LSSVM and internal standard method is feasible for detecting dimethoate content in solution quantificationally. The coefficient of determination (R2) of LSSVM calibration model based on internal standard method is 0.999 7, and the average relative errors in training set and validation set are 11.24% and 12.01%, respectively. In the three calibration models, LSSVM calibration model based on internal standard method has the best performance, and the performance of LSSVM calibration model is the second, while univariatecalibration model hasthe worstperformance. So it can be concluded that LSSVM and internal standard method can improve the performance of calibration model to some extent, and improve the prediction accuracy.
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Received: 2017-04-14
Accepted: 2017-08-26
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Corresponding Authors:
LIU Mu-hua
E-mail: suikelmh@sina.com
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