Citation: WU Yi-Qing, LIU Jin, MO Xin-Xin, SUN Tong, LIU Mu-Hua. Quantitative Analysis of Chromium in Vegetable Oil Based on Double Pulse-Laser-induced Breakdown Spectroscopy and Least Squares Support Vector Machine[J]. Chinese Journal of Analytical Chemistry, ;2016, 44(12): 1919-1926. doi: 10.11895/j.issn.0253-3820.160514 shu

Quantitative Analysis of Chromium in Vegetable Oil Based on Double Pulse-Laser-induced Breakdown Spectroscopy and Least Squares Support Vector Machine

Key Laboratory of Biological Optics-Electric Technique and Application, Jiangxi Agricultural University, Nanchang 330045, China

Received Date: 5 July 2016
Revised Date: 8 September 2016

Fund Project: This work was supported by the National Natural Science Foundation of China (No. 31401278) and the Natural Science Foundation of Jiangxi Province, China (Nos. 20132BAB214010, 20151BAB204025).

The content of heavy metal chromium (Cr) in vegetable oil (soybean oil, peanut oil and corn oil) was quantitatively analyzed by collinear double pulse laser-induced breakdown spectroscopy (DP-LIBS). An Ava-Spec two-channel spectrometer was used to acquire LIBS spectra of samples, and then the CN molecular spectral line (421.49 nm), Ca atomic spectral line (422.64 nm) and three atomic lines of Cr (425.39 nm, 427.43nm and 428.87 nm) were determined by LIBS spectrum of samples. According to the above spectral lines, single variable calibration model and least squares support vector machine (LS-SVM) calibration model of Cr element were established, and they were verified by the validation samples. The results showed that the average prediction relative errors (PRE) of the validation samples by using single variable calibration method were 12.57%, 12.11% and 13.72%, respectively. When using LS-SVM method of three variables, the fitting degree (R²) between the true value and the predictive value of the calibration samples was 0.9785, 0.9792 and 0.9654, and the average PRE of the validation samples was 8.92%, 8.33% and 10.98%, respectively. While using LS-SVM method of five variables, the fitting degree (R²) between the true value and the predictive value of the calibration samples was 0.9895, 0.9901 and 0.9855, and the average PRE of the validation samples was 7.46%, 8.96% and 8.95%, respectively. Therefore, the LS-SVM calibration model has better performance than single variable calibration method. And the LS-SVM calibration model established with five variables has better performance than the one with three variables. The LS-SVM method and the introduction of suitable matrix elements (CN, Ca) can reduce the error of quantitative analysis effectively, and improve the accuracy of prediction for Cr content in vegetable oil by LIBS technique.
- Double pulse laser-induced breakdown spectroscopy,
- Least squares support vector machine,
- Vegetable oil,
- Chromium
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